Skip to main content
International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2013 May 30;14(6):11560–11606. doi: 10.3390/ijms140611560

MicroRNA-Regulated Protein-Protein Interaction Networks and Their Functions in Breast Cancer

Chia-Hsien Lee 1,, Wen-Hong Kuo 2,, Chen-Ching Lin 1, Yen-Jen Oyang 1, Hsuan-Cheng Huang 3,*, Hsueh-Fen Juan 1,4,*
PMCID: PMC3709748  PMID: 23722663

Abstract

MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs.

Keywords: miRNA, breast cancer, protein interaction network, functional analysis

1. Introduction

Breast cancer is a global health threat for women. According to a 2008 survey [1], breast cancer was the leading cause of cancer deaths in women. Our knowledge of possible risk factors has led to developments in diagnostic methods, drugs, and surgery procedures for treatment [2,3]; however, the details of breast carcinoma progression, and perhaps most importantly, how to cure breast cancer, remain elusive.

Previous research has identified a number of risk factors for breast cancer. Early menarche, late menopause, obesity, late first full pregnancy, and hormone replacement therapy were considered as high risk factors for breast cancer [2]. Breast cancer risk has also been reported to be related to fat intake in diets rich in red meats and high-fat dairy foods [3].

MicroRNAs (miRNAs) [4], are short endogenous non-coding RNAs which are able to regulate gene expression. After miRNA precursors are transcribed from the genome or generated from spliceosomes, they are exported to the cytoplasm and further processed by the Dicer complex [5]. The mature miRNA is then bound to Argonaute protein, forming a miRNA-protein complex known as the RNA-induced silencing complex (RISC) [6], miRNP, or RNAi (RNA interference) enzyme complex [7,8]. The RISC has been reported to down-regulate target genes by translational repression [9] or mRNA cleavage [10].

Like other protein-based regulators, miRNAs have been associated with cancer. Calin et al., reported that miR-15 and miR-16 were deleted in leukemia [11], which was believed to be one of the earliest reports associating miRNAs with cancer [12]. After this report, many miRNAs were found to act as tumor suppressors or oncogenes (also known as oncomirs). For example, miR-21 was identified as an oncomir in hepatocellular cancer [13], breast cancer [14], and kidney cancer [15]. On the other hand, let-7c was found to be a tumor suppressor in prostate cancer [16], and miR-181a was reported as a tumor suppressor in glioma [17]. Further, miR-125b [18], and miR-145 [19] were identified as tumor suppressors in breast cancer, and miR-125a was found to repress tumor growth in breast cancer [20]. Thus, it is highly likely that miRNAs play an important role in breast cancer.

Since miRNA functions by regulating its target genes, we may deduce the effects of miRNAs by analyzing their regulated networks. To use such a method to elucidate miRNA functions, targets of miRNAs should be deduced. Currently, predictions in most target prediction database are based on sequence and statistical methods [21]. For example, in TargetScan, seed base pairing, target site context, conservation of target site and miRNA, and site accessibility are considered in the prediction process [22].

Another method to elucidate miRNA targets is to integrate expression profiles of miRNA and mRNA. In the work of Huang et al. [23], a Bayesian-based algorithm, GenMiR++, was developed to predict possible targets of 104 miRNAs in humans. They also verified their results by RT-PCR and microarray experiments. However, the power of other sequence-based target prediction algorithms was not utilized in their work.

It is also possible to combine sequence-based target prediction and expression-based target prediction methods. By integrating expression data into sequence-based predictions, possible false positives can be reduced. Previously, miRNA-mRNA interactions were explored with splitting-averaging Bayesian networks [24]. In that work, expression profiles of miRNA and mRNA from public databases, miRNA target prediction databases, and miRNA sequence information were integrated together to discover miRNA-mRNA interaction networks.

Here, we combined expression profiles of miRNA and mRNA, and three target prediction databases, TargetScan, PicTar and miRanda, to obtain confident miRNA-mRNA relationships and construct miRNA-regulated protein-protein networks for breast cancer. Furthermore, we explored the functions of the miRNAs by inspecting the underlying protein interaction networks (PINs) of the miRNAs with functional enrichment analysis. This method, as described in Figure 1, was used to elucidate the functions of gastric cancer-related miRNAs in our previous work [25]. In that study, a gastric cancer-associated miRNA, miR-148a, was identified and validated as being involved in tumor proliferation, invasion, migration, and the survival rate of the patients. By using a similar method, we aimed to elucidate breast cancer-related miRNA-regulated PINs and their functions.

Figure 1.

Figure 1

Analysis flow chart used in this work. After expression profiles and target prediction databases were fetched and preprocessed, they were subjected to the analysis process described here and in the “Experimental Section”.

2. Results and Discussion

To construct miRNA-regulated PINs, differentially expressed miRNAs and genes from the dataset from Farazi et al. [26] were extracted following proper processing of the expression profiles. From our selected public miRNA dataset, we found 89 down-regulated miRNAs (93 prior to fold-change filtering) and only 1 up-regulated miRNA (Table S1). In gene expression dataset GSE29174, we found a total of 1268 down-regulated genes and 587 up-regulated genes before applying the fold change filter. There were 726 down-regulated genes (Table S2) and 437 up-regulated genes (Table S3) after significantly and differentially expressed genes were filtered by fold change (fold change >2).

From the results of SAM analysis, we identified some well-known breast cancer-related miRNAs (Table S1). For example, miR-214-3p [27] and miR-335-5p [28] have been previously reported to be down-regulated in breast cancer. Let-7c was found to be down-regulated in this work, while let-7a, another member of the let-7 family, was found to be down-regulated in another work [29]. MicroRNAs of the let-7 family were also reportedly down-regulated in several types of cancer [30]. We also found that miR-21-5p, the sole up-regulated miRNA in our list, was also previously found to be up-regulated [14,31]. However, changes in the expression of most of the miRNAs in our down-regulated list have not been reported in the literature. Therefore, we could not rule out the possibility that these miRNAs were novel breast cancer-related miRNAs. There are also some well-known miRNAs not presented in our list (for such a list, one may see [3234]). The reason that some known miRNAs, for example, miR-19a, miR-155 and miR-205, did not show up in our result might be that we used a very stringent threshold (described in Experimental Section) when selecting differentially expressed miRNAs for PIN construction.

Since the miRNAs of the miRNA-regulated PINs were differentially expressed between normal and tumor tissues, and we identified some cancer-related functions in our functional enrichment analysis, the miRNAs may potentially be useful diagnostic markers for breast cancer. To verify this, we applied ROC curve analysis on the miRNA expression profile that was not used in constructing the miRNA-regulated PINs. Notably, our results (Figures 2 and S1 and Table S4) showed that let-7c (Figure 2), miR-497-5p, miR-125b-5p, and some other miRNAs of miRNA-regulated PINs, performed well when used as breast cancer diagnostic markers.

Figure 2.

Figure 2

Receiver operating characteristic (ROC) curve of let-7c from our miRNA array dataset. For ROC curves of other miRNAs, see Figure S1.

Following elucidation of differentially expressed miRNAs and genes, miRNA-regulated PINs could then be constructed. We identified and constructed partial networks, containing the miRNA and its direct target, with the differentially expressed miRNAs and genes as described in the “Experimental Section”. We then extended the network by appending known interactions from the HPRD database. Finally, 18 miRNA-regulated PINs were constructed by the steps described above (Figure 3, Figures S2–S13, and Table S5).

Figure 3.

Figure 3

The let-7c-regulated protein interaction network (PIN). This is one of the 18 miRNA-regulated PINs constructed in this work. Figures of all other miRNA-regulated PINs are displayed in Supplementary Figures S2–S13.

After construction of the 18 PINs was completed, we observed that the sizes of the PINs were not similar: some of the miRNAs seemed to regulate larger sized PINs, while other miRNAs affected only a small number of genes. Small miRNA-regulated PINs may be caused by the strict q-value threshold set during SAM analysis, the processing steps performed on the target prediction databases discussed previously, and possibly by lack of protein-protein interaction data for some proteins in the HPRD. Although the HPRD may be considered the most comprehensive source of protein-protein interaction data [35], some proteins may not have been considered and researched by other investigators, and therefore, interaction data for those proteins would not be included in the HPRD. However, it may be true that some of the miRNA-regulated PINs were small in breast cancer, since the construction of the PINs were based on differentially expressed miRNAs and genes between normal tissues and tumor samples, and those miRNAs with small PINs may not be as important as others with larger PINs.

To elucidate the functions of a miRNA with a regulated PIN, GO enrichment analysis was applied to the miRNA-regulated PINs. We did not consider the miRNAs with ≤5 genes in their regulated PINs, and some of the miRNA-regulated PINs had no enriched functions using the defined threshold, FDR < 0.0001. To exclude GO terms that describe a broad range of concepts, we only included high level GO terms, i.e., larger than 5.

The results of the GO enrichment analysis for let-7c related to cancer are listed in Table 1. (Results of all miRNA-regulated PINs were in Table S6). We defined a GO term as cancer-related if a GO term contained “cell proliferation”, “cell death”, “apoptosis”, “signaling”, “microtubule”, and “actin”. We noted that 7 miRNAs had enriched GO terms related to apoptosis, cell death, and cell proliferation, i.e., miR-520d-3p, miR-497-5p, miR-125b-5p, miR-21-5p, miR-31-5p, let-7c, and miR-125-5p. Further, some miRNA-regulated PINs may have functions other than cell survival. For example, the nerve growth factor receptor pathway was enriched in miR-regulated PINs of miR-520d-3p, miR-497-5p, miR-125a-5p, miR-125b-5p, and miR-31-5p, and the epidermal growth factor receptor pathway was enriched in miR-regulated PINs of miR-520d-3p, miR-21-5p, and miR-497-5p. Most of the miRNAs had been previously described and were known to be implicated in breast cancer. Let-7c was not only likely to be down-regulated in breast cancer [29], but was also found to be a tumor suppressor in prostate cancer [16]. Another reported tumor suppressor was miR-125b-5p, which was found to be down-regulated in breast tumor tissue [18], and this finding was consistent with our functional enrichment results. The miR-125a-5p-regulated PIN was found to be able to inhibit apoptosis and regulate epithelial cell proliferation, and has been reported to repress cell growth [36].

Table 1.

Selected enriched functions of let-7c. Member genes of the let-7c-regulated network annotated with corresponding enriched functions are listed.

MIMAT0000064(hsa-let-7c)
GO term Genes Adj. p-value
GO:0043067, Regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, PSMA3, BIRC5, ACTN2, HOXA13, IRS2, FASTK, VAV1, PSMB6, BCL2, CDK1, HDAC1, SOX10, TIA1, AKT1, AURKB 3.43 × 10−8
GO:0043069, Negative regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 6.58 × 10−6
GO:0060548, Negative regulation of cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 8.92 × 10−6
GO:0015630, Microtubule cytoskeleton INCENP, SNTB2, SEPT1, TACC1, BIRC5, RACGAP1, PIN4, CDCA8, CDK1, PHF1, AKT1, AURKB, NINL, CCDC85B 5.15 × 10−5

We also found that some breast cancer-specific functions were enriched in our results. For example, in the miR-497-5p-regulated PIN, the term “androgen receptor signaling pathway” was enriched. Although it is not clear whether androgens are related to breast cancer, androgen receptors are known to be up-regulated in breast cancer and related to node invasiveness [37].

To further verify the results of the enriched cancer-related functions, we used GOBO for survival analysis. Our hypothesis is that expression of genes annotated with enriched cancer-related terms may be related to survival outcome of patients. With exception to some cell death/proliferation-related terms, it is already known that some pathways or functions are also related to clinical outcomes. For example, cell proliferation-related GO terms have a high probability of affecting survival of cancer tissues, and patient outcome may worsen if cancer tissue survives. In addition, some signaling pathways were known to enhance invasiveness, migration abilities, or were associated with reduced patient survival. For example, the BMP signaling pathway is known to confer various tumor cells with enhanced migration and invasion abilities [38], and nerve growth factor receptor (NGFR) was found to be associated with overall survival of breast cancer [38]. Furthermore, Toll-like receptor 4 has been reported to promote adhesion and invasive migration in breast cancer [39]. Finally, the cytoskeleton plays an important role in regulating cell motility in all cells. Actin filaments are known to participate in the invasive migration of cancer cells [40]. Since some of these functions were present in our enriched terms, we wished to test if the expression of a gene set annotated to the cancer-related enriched terms in the PIN would be related to clinical outcome of patients.

As shown in Figure 4, Figure S14 and Table S7, only some of the enriched terms were significantly associated with clinical outcome. This may be because the changes in these key genes occurred at the protein level, such as in protein expression or even post-translational modification; therefore, mRNA expression-centric tools like GOBO cannot explore association of such genes to clinical outcomes. Alternatively, it is possible the miRNA did not regulate the whole pathway, or the miRNA did not target the key part of the pathway directly, and thus the clinical outcome of gene sets of the enriched GO terms in this condition cannot be determined. However, some functions associated with clinical outcomes were observed. For example, proteins annotated with the terms “microtubule cytoskeleton”, “negative regulation of programmed cell death”, and “negative regulation of cell death” in the let-7c-regulated PIN were related to 10 year survival rate of patients, as reported by GOBO (Figure 4). Also, the enriched term “regulation of epithelial cell proliferation” for both miR-125a-5p and miR-125b-5p were found to be associated with the 10-year survival rate of patients. Therefore, these results further supported the GO enrichment analysis discussed previously.

Figure 4.

Figure 4

Validation of let-7c result. Gene expression-based outcome for breast cancer online (GOBO) survival analysis of let-7c-regulated PIN members marked with the following functions: (A) Microtubule cytoskeleton; (B) Negative regulation of programmed cell death; and (C) negative regulation of cell death. Red: samples with high expression of selected gene set (PIN members); grey: samples with low expression of selected gene set (PIN members).

3. Experimental Section

3.1. miRNA Microarray Experiments

We performed a miRNA microarray to obtain the expression profiles for receiver operating characteristic (ROC) curve analysis. This dataset was deposited in Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/, Series Accession: GSE45666). In total, there were 15 normal samples and 101 tumor samples in the expression profile. Detailed pathophysiogical characteistics of these samples were in Table S8 and GSE45666. All human tissue samples collected from breast cancer patients were approved and human subject confidentiality was protected by the Institute Review Board of National Taiwan University Hospital (IRB, 20071211R).

Total RNA was extracted from tissues collected from the patients using Trizol® Reagent (Invitrogen, Carlsbad, CA, USA, USA) according to the manufacturer’s protocol. Purified RNA was quantified at OD260nm by using a ND-1000 spectrophotometer (Nanodrop Technology, Wilmington, DE, USA) and qualitated by using a Bioanalyzer 2100 with the RNA 6000 Nano LabChip kit (Agilent Technologies, Santa Clara, CA, USA).

After RNA extraction, 100 ng of total RNA was dephosphorylated and labeled with pCp-Cy3 using the Agilent miRNA Complete Labeling and Hyb Kit in conjunction with the microRNA Spike-In kit (Agilent Technologies, Santa Clara, CA, USA). Briefly, 2X hybridization buffer (Agilent Technologies) was added to the labeled mixture to a final volume of 45 μL. The mixture was heated for 5 min at 100 °C and immediately cooled to 0 °C. Each 45 μL sample was hybridized onto an Agilent human miRNA Microarray Release 12.0, 8 × 15 K (Agilent Technologies) at 55 °C for 20 h. After hybridization, slides were washed for 5 min in Gene Expression Wash Buffer 1 at room temperature, then for 5 min in Gene Expression Wash Buffer 2 at 37 °C. Slides were scanned on an Agilent microarray scanner (Agilent Technologies, model G2505C) at 100% and 5% sensitivity settings. Feature Extraction (Agilent Technologies) software version 10.7.3.1 (Agilent Technologies, Santa Clara, CA, USA) was used for image analysis.

3.2. mRNA Expression Profiles

For miRNA-regulated protein-protein interaction network construction, the mRNA expression profile was fetched from GEO (Series Accession: GSE29174). This dataset was produced by Farazi et al. [26], which was the only dataset publicly available with large size of tumor samples and reasonably sized normal samples. In total, 161 clinical samples were collected from breast cancer patients by biopsy: 110 invasive ductal carcinoma (IDC), 11 normal, 17 ductal carcinoma in situ (DCIS), 1 mucinous A, 8 atypical medullary, 4 apocrine, 8 metaplastic, and 2 adenoid, as classified by Farazi et al. The 110 IDC samples were classified as the tumor group and the 11 normal samples were classified as the normal group in this study.

3.3. miRNA Expression Profiles

The miRNA expression profile used in this work was fetched from Table S4A of the work of Farazi et al. [26]. There were 189 samples in this dataset, with 6 of them from cell lines, and another 183 samples from patient tissues. In the 183 clinical samples collected from breast cancer patients by biopsy in this dataset, there were 128 IDC, 11 normal, 18 DCIS, 1 mucinous A, 8 atypical medullary, 4 apocrine, 9 metaplastic, and 2 adenoid cases, as classified by Farazi et al. The 128 IDC samples were classified as the tumor group and the 11 normal samples as were classified as the normal group.

3.4. Data Analysis

The overall workflow design was similar to the previous work of Tseng et al. [25] (see Figure 1). However, we applied the workflow on breast cancer expression profiles instead of gastric cancer as in the work of Tseng et al. Additionally, we used 3 miRNA target prediction databases, TargetScan (v6.0) [22,41,42], PicTar [43,44], and miRanda (release August 2010) [45] here, while only TargetScan was used in the previous study.

To construct the networks, we first elucidated differentially expressed miRNAs and mRNAs from published datasets. Both of miRNA (generated with miRNA-Seq technique) and mRNA array expression data were produced by Farazi et al. [26] from the same batch of clinical tumor samples. To obtain a list of differentially expressed genes and miRNAs between normal and tumor groups, we used significance analysis of microarrays (SAM) [46] implemented in R package samr (version 2.0; Stanford University, Stanford, CA, USA). We set the false discovery rate (FDR) as ≤0.0001%, fold change as ≥2.5 for miRNA, and fold change as ≥1.9 for genes as thresholds to reduce false positives. If a gene/miRNA was expressed higher in the normal group compared to the tumor group, we defined that gene/miRNA as a down-regulated gene/miRNA, and vice versa.

Following this, we paired the miRNAs and mRNAs with different expression trends. For example, an up-regulated miRNA would be paired with a down-regulated mRNA. If such pairs could be found in 2 (or more) of the 3 miRNA target prediction databases, they were then added to their corresponding miRNA-regulated network. To further extend the coverage of our network, we incorporated the human protein reference database (HPRD) [47], which contains experimentally verified interaction data, into our miRNA-regulated PINs.

Finally, we used gene ontology (GO) enrichment analysis to explore the function of the miRNA-regulated PINs. Hypergeometric tests were used to determine if a GO term was enriched in a PIN. In this section, we excluded PINs with less than 5 proteins. We also excluded GO terms with levels of less than 5 to avoid non-specific GO terms. Since we tested multiple GO terms on each miRNA-regulated PIN, we adjusted the significance of the test with the FDR method developed by Benjamini et al. [48]. We used the adjusted p-value < 0.0001 as our threshold. A GO term would be excluded if its p-value was larger than 0.0001. Therefore, for each network, we ran the GO enrichment analysis, collected the calculated p-values, and adjusted these values using the methods described above.

3.5. ROC and GOBO Survival Analysis

After the PINs were constructed, we attempted to verify our results by literature search, ROC, and GOBO survival analysis [49]. To determine if the miRNAs we found could serve as classification markers for discriminating between normal and tumor samples, we applied ROC analysis on our miRNA array data (described in Section 2.1). ROC analysis is usually used to evaluate the efficiency of a classifier or a biological marker. R package ROCR [50] (version 1.0.4) was used to plot the ROC curve and calculate the area under curve (AUC). The standard error of AUC was then calculated as described in the work of Hanlye and McNeil [51]. The p-value of AUC was thus calculated with standard error obtained in the previous step. To further validate if the PINs we found were related to cancer, we used survival analysis implemented in GOBO [49] (available at http://co.bmc.lu.se/gobo), which provides a large amount of breast cancer gene expression profiles collected from public databases with clinical outcome data. In both ROC analysis and GOBO survival analysis, we considered our results significant when the p-value was smaller than 0.05.

4. Conclusions

Using integrative analysis of miRNA and mRNA expression profiles, we have identified not only breast cancer-related miRNAs and genes, but also putative roles for miRNAs in cancer as elucidated from miRNA-regulated PINs constructed in this work. Here, some previously known functions of miRNAs were again presented in our results, e.g., the relationship between the miRNAs, let-7c, miR-125a-5p, miR-125b-5p, and miR-21-5p, and breast cancer were demonstrated in this research. Furthermore, we have identified additional miRNAs and their related functions that have not been previously reported or discussed, providing valuable resources for further research in breast cancer.

Supplementary Information

Table S1.

Significantly differentially expressed miRNAs found in miRNA dataset in Farazi et al. [26]. There are 89 down-regulated miRNAs and 1 up-regulated miRNA in this list. Q-values reported by SAM were 0 for all miRNAs in this list.

miRBase Accession miRNA Name Fold Change
MIMAT0004761 hsa-miR-483-5p 0.01
MIMAT0004552 hsa-miR-139-3p 0.01
MIMAT0000738 hsa-miR-383 0.02
MIMAT0002856 hsa-miR-520d-3p 0.02
MIMAT0002811 hsa-miR-202-3p 0.03
MIMAT0002177 hsa-miR-486-5p 0.04
MIMAT0022721 hsa-miR-1247-3p 0.05
MIMAT0002175 hsa-miR-485-5p 0.06
MIMAT0000265 hsa-miR-204-5p 0.07
MIMAT0000752 hsa-miR-328 0.07
MIMAT0000421 hsa-miR-122-5p 0.07
MIMAT0000447 hsa-miR-134 0.08
MIMAT0000722 hsa-miR-370 0.09
MIMAT0004513 hsa-miR-101-5p 0.09
MIMAT0000446 hsa-miR-127-3p 0.10
MIMAT0000097 hsa-miR-99a-5p 0.10
MIMAT0004566 hsa-miR-218-2-3p 0.10
MIMAT0000729 hsa-miR-376a-3p 0.11
MIMAT0009197 hsa-miR-205-3p 0.11
MIMAT0004615 hsa-miR-195-3p 0.11
MIMAT0005899 hsa-miR-1247-5p 0.11
MIMAT0000720 hsa-miR-376c 0.12
MIMAT0000762 hsa-miR-324-3p 0.12
MIMAT0004679 hsa-miR-296-3p 0.12
MIMAT0004614 hsa-miR-193a-5p 0.12
MIMAT0003880 hsa-miR-671-5p 0.12
MIMAT0004795 hsa-miR-574-5p 0.12
MIMAT0004599 hsa-miR-143-5p 0.13
MIMAT0000423 hsa-miR-125b-5p 0.13
MIMAT0004957 hsa-miR-760 0.13
MIMAT0004911 hsa-miR-874 0.14
MIMAT0004603 hsa-miR-125b-2-3p 0.15
MIMAT0004952 hsa-miR-665 0.15
MIMAT0018205 hsa-miR-3928 0.15
MIMAT0004767 hsa-miR-193b-5p 0.15
MIMAT0002861 hsa-miR-518e-3p 0.15
MIMAT0004604 hsa-miR-127-5p 0.16
MIMAT0002807 hsa-miR-491-5p 0.16
MIMAT0004689 hsa-miR-377-5p 0.16
MIMAT0004762 hsa-miR-486-3p 0.16
MIMAT0000732 hsa-miR-378a-3p 0.17
MIMAT0017981 hsa-miR-3605-5p 0.18
MIMAT0004605 hsa-miR-129-2-3p 0.19
MIMAT0006789 hsa-miR-1468 0.20
MIMAT0000737 hsa-miR-382-5p 0.21
MIMAT0000077 hsa-miR-22-3p 0.21
MIMAT0000089 hsa-miR-31-5p 0.21
MIMAT0004612 hsa-miR-186-3p 0.21
MIMAT0004592 hsa-miR-125b-1-3p 0.22
MIMAT0001639 hsa-miR-409-3p 0.22
MIMAT0015032 hsa-miR-3158-3p 0.22
MIMAT0004496 hsa-miR-23a-5p 0.22
MIMAT0000690 hsa-miR-296-5p 0.22
MIMAT0000731 hsa-miR-378a-5p 0.23
MIMAT0000448 hsa-miR-136-5p 0.23
MIMAT0004796 hsa-miR-576-3p 0.23
MIMAT0010133 hsa-miR-2110 0.23
MIMAT0004951 hsa-miR-887 0.23
MIMAT0003239 hsa-miR-574-3p 0.25
MIMAT0005901 hsa-miR-1249 0.25
MIMAT0000510 hsa-miR-320a 0.26
MIMAT0002172 hsa-miR-376b 0.26
MIMAT0000250 hsa-miR-139-5p 0.27
MIMAT0005825 hsa-miR-1180 0.27
MIMAT0000437 hsa-miR-145-5p 0.28
MIMAT0004601 hsa-miR-145-3p 0.28
MIMAT0003322 hsa-miR-652-3p 0.28
MIMAT0000756 hsa-miR-326 0.28
MIMAT0000098 hsa-miR-100-5p 0.29
MIMAT0003296 hsa-miR-627 0.29
MIMAT0002820 hsa-miR-497-5p 0.31
MIMAT0004507 hsa-miR-92a-1-5p 0.31
MIMAT0000271 hsa-miR-214-3p 0.32
MIMAT0004702 hsa-miR-339-3p 0.33
MIMAT0004611 hsa-miR-185-3p 0.33
MIMAT0000064 hsa-let-7c 0.34
MIMAT0004673 hsa-miR-29c-5p 0.35
MIMAT0000733 hsa-miR-379-5p 0.35
MIMAT0004594 hsa-miR-132-5p 0.35
MIMAT0000765 hsa-miR-335-5p 0.35
MIMAT0002819 hsa-miR-193b-3p 0.36
MIMAT0000088 hsa-miR-30a-3p 0.36
MIMAT0005951 hsa-miR-1307-3p 0.36
MIMAT0004597 hsa-miR-140-3p 0.37
MIMAT0004556 hsa-miR-10b-3p 0.37
MIMAT0000272 hsa-miR-215 0.37
MIMAT0004511 hsa-miR-99a-3p 0.37
MIMAT0000443 hsa-miR-125a-5p 0.38
MIMAT0004482 hsa-let-7b-3p 0.38
MIMAT0000076 hsa-miR-21-5p 6.58

Table S2.

Down-regulated genes found in dataset GSE29174. There are 726 down-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene ID Gene Symbol Fold Change
2949 GSTM5 0.06
10894 LYVE1 0.06
5950 RBP4 0.07
762 CA4 0.09
54997 TESC 0.09
3489 IGFBP6 0.09
3952 LEP 0.09
213 ALB 0.09
3131 HLF 0.10
4023 LPL 0.10
10633 RASL10A 0.11
364 AQP7 0.11
1908 EDN3 0.11
1811 SLC26A3 0.11
91851 CHRDL1 0.11
729359 PLIN4 0.13
1149 CIDEA 0.13
5959 RDH5 0.13
5348 FXYD1 0.14
5346 PLIN1 0.14
10249 GLYAT 0.14
158800 RHOXF1 0.14
221476 PI16 0.14
3040 HBA2 0.14
6939 TCF15 0.14
79645 EFCAB1 0.14
80343 SEL1L2 0.14
9413 FAM189A2 0.15
26289 AK5 0.15
25891 PAMR1 0.15
3679 ITGA7 0.15
1264 CNN1 0.15
92304 SCGB3A1 0.15
2167 FABP4 0.15
23285 KIAA1107 0.15
7145 TNS1 0.16
4881 NPR1 0.16
1028 CDKN1C 0.16
1036 CDO1 0.16
130271 PLEKHH2 0.16
8736 MYOM1 0.16
8908 GYG2 0.16
619373 MBOAT4 0.17
130399 ACVR1C 0.17
1646 AKR1C2 0.17
80763 C12orf39 0.17
2159 F10 0.18
84889 SLC7A3 0.18
1308 COL17A1 0.18
83699 SH3BGRL2 0.18
84417 C2orf40 0.18
4081 MAB21L1 0.18
3484 IGFBP1 0.18
5239 PGM5 0.19
4969 OGN 0.19
2719 GPC3 0.19
116362 RBP7 0.19
948 CD36 0.19
5764 PTN 0.19
3043 HBB 0.19
56920 SEMA3G 0.20
94274 PPP1R14A 0.20
57447 NDRG2 0.20
84795 PYROXD2 0.20
84649 DGAT2 0.20
2690 GHR 0.20
22802 CLCA4 0.20
5179 PENK 0.20
6663 SOX10 0.20
6649 SOD3 0.21
54922 RASIP1 0.21
8406 SRPX 0.21
1446 CSN1S1 0.21
7123 CLEC3B 0.22
9647 PPM1F 0.22
1842 ECM2 0.22
3909 LAMA3 0.22
8639 AOC3 0.23
2934 GSN 0.23
9370 ADIPOQ 0.23
3202 HOXA5 0.23
9452 ITM2A 0.23
6290 SAA3P 0.23
4604 MYBPC1 0.23
79785 RERGL 0.16
221091 LRRN4CL 0.17
3991 LIPE 0.17
27175 TUBG2 0.24
1346 COX7A1 0.24
6376 CX3CL1 0.24
50486 G0S2 0.24
6285 S100B 0.24
443 ASPA 0.24
947 CD34 0.25
84632 AFAP1L2 0.25
3866 KRT15 0.25
147463 ANKRD29 0.25
2878 GPX3 0.25
7079 TIMP4 0.25
54345 SOX18 0.25
51277 DNAJC27 0.25
84870 RSPO3 0.25
55323 LARP6 0.25
6387 CXCL12 0.25
137835 TMEM71 0.25
5212 VIT 0.25
26577 PCOLCE2 0.25
845 CASQ2 0.25
6422 SFRP1 0.25
10351 ABCA8 0.26
10840 ALDH1L1 0.26
65983 GRAMD3 0.26
84327 ZBED3 0.26
57124 CD248 0.26
3235 HOXD9 0.26
2192 FBLN1 0.26
91653 BOC 0.26
4147 MATN2 0.26
126669 SHE 0.27
2788 GNG7 0.27
129804 FBLN7 0.27
270 AMPD1 0.27
79656 BEND5 0.27
58503 PROL1 0.27
3316 HSPB2 0.27
729440 CCDC61 0.27
54438 GFOD1 0.27
5243 ABCB1 0.27
1128 CHRM1 0.23
83878 USHBP1 0.24
63970 TP53AIP1 0.24
79192 IRX1 0.28
3400 ID4 0.28
57519 STARD9 0.29
57666 FBRSL1 0.29
3590 IL11RA 0.29
57664 PLEKHA4 0.29
197257 LDHD 0.29
66036 MTMR9 0.29
2321 FLT1 0.29
126 ADH1C 0.29
1363 CPE 0.29
56131 PCDHB4 0.29
22915 MMRN1 0.29
7069 THRSP 0.29
57161 PELI2 0.30
770 CA11 0.30
53342 IL17D 0.30
79987 SVEP1 0.30
857 CAV1 0.30
222166 C7orf41 0.30
27190 IL17B 0.30
116159 CYYR1 0.30
4487 MSX1 0.30
9068 ANGPTL1 0.30
10411 RAPGEF3 0.30
3199 HOXA2 0.30
2944 GSTM1 0.30
2920 CXCL2 0.30
201134 CEP112 0.31
220001 VWCE 0.31
83888 FGFBP2 0.31
6366 CCL21 0.31
6711 SPTBN1 0.31
85378 TUBGCP6 0.31
26040 SETBP1 0.31
4692 NDN 0.31
25890 ABI3BP 0.31
23531 MMD 0.31
30846 EHD2 0.31
6196 RPS6KA2 0.31
2009 EML1 0.31
810 CALML3 0.27
6898 TAT 0.27
5648 MASP1 0.28
25999 CLIP3 0.28
125875 CLDND2 0.28
7102 TSPAN7 0.28
1879 EBF1 0.28
23252 OTUD3 0.28
5493 PPL 0.28
83987 CCDC8 0.28
9073 CLDN8 0.28
221981 THSD7A 0.28
64102 TNMD 0.28
137872 ADHFE1 0.33
27151 CPAMD8 0.33
387923 SERP2 0.33
145581 LRFN5 0.33
6263 RYR3 0.33
2354 FOSB 0.33
51302 CYP39A1 0.33
4128 MAOA 0.34
117248 GALNTL2 0.34
10268 RAMP3 0.34
7730 ZNF177 0.34
10873 ME3 0.34
7461 CLIP2 0.34
7049 TGFBR3 0.34
79901 CYBRD1 0.34
5152 PDE9A 0.34
50805 IRX4 0.34
8644 AKR1C3 0.34
5915 RARB 0.34
2770 GNAI1 0.34
54996 2-Mar 0.35
79791 FBXO31 0.35
54776 PPP1R12C 0.35
9079 LDB2 0.35
57104 PNPLA2 0.35
30008 EFEMP2 0.35
91461 PKDCC 0.35
23368 PPP1R13B 0.35
23461 ABCA5 0.35
9572 NR1D1 0.35
23338 PHF15 0.35
6289 SAA2 0.31
345275 HSD17B13 0.31
2701 GJA4 0.32
112609 MRAP2 0.32
727 C5 0.32
477 ATP1A2 0.32
9627 SNCAIP 0.32
4435 CITED1 0.32
10974 C10orf116 0.32
11005 SPINK5 0.32
80325 ABTB1 0.33
221395 GPR116 0.33
10014 HDAC5 0.33
1489 CTF1 0.37
35 ACADS 0.37
3749 KCNC4 0.37
140738 TMEM37 0.37
2791 GNG11 0.37
23604 DAPK2 0.37
10217 CTDSPL 0.37
23550 PSD4 0.37
4306 NR3C2 0.37
119587 CPXM2 0.37
7942 TFEB 0.37
3815 KIT 0.37
1805 DPT 0.37
23242 COBL 0.37
4313 MMP2 0.37
4139 MARK1 0.37
9104 RGN 0.37
2329 FMO4 0.37
25802 LMOD1 0.38
4239 MFAP4 0.38
10392 NOD1 0.38
6794 STK11 0.38
85458 DIXDC1 0.38
4123 MAN2C1 0.38
54476 RNF216 0.38
9920 KBTBD11 0.38
6329 SCN4A 0.38
10253 SPRY2 0.38
1910 EDNRB 0.38
9249 DHRS3 0.38
22869 ZNF510 0.38
114800 CCDC85A 0.35
2550 GABBR1 0.35
4638 MYLK 0.35
2327 FMO2 0.35
139411 PTCHD1 0.35
10391 CORO2B 0.35
25854 FAM149A 0.35
55701 ARHGEF40 0.36
1759 DNM1 0.36
22849 CPEB3 0.36
57716 PRX 0.36
1628 DBP 0.36
80031 SEMA6D 0.36
259217 HSPA12A 0.36
6909 TBX2 0.36
1511 CTSG 0.36
79971 WLS 0.36
90865 IL33 0.36
11343 MGLL 0.36
55800 SCN3B 0.36
1949 EFNB3 0.36
284217 LAMA1 0.36
22927 HABP4 0.37
23645 PPP1R15A 0.39
342574 KRT27 0.39
83543 AIF1L 0.39
624 BDKRB2 0.39
347 APOD 0.39
84935 C13orf33 0.39
858 CAV2 0.39
5138 PDE2A 0.40
114928 GPRASP2 0.40
58190 CTDSP1 0.40
513 ATP5D 0.40
57684 ZBTB26 0.40
7041 TGFB1I1 0.40
5787 PTPRB 0.40
7294 TXK 0.40
56301 SLC7A10 0.40
55937 APOM 0.40
6368 CCL23 0.40
55020 TTC38 0.40
134265 AFAP1L1 0.40
4485 MST1 0.40
3384 ICAM2 0.38
8613 PPAP2B 0.38
1950 EGF 0.38
55273 TMEM100 0.38
6297 SALL2 0.38
9365 KL 0.38
8863 PER3 0.38
8404 SPARCL1 0.38
2202 EFEMP1 0.38
8369 HIST1H4G 0.38
5187 PER1 0.39
30815 ST6GALNAC6 0.39
256364 EML3 0.39
57381 RHOJ 0.39
761 CA3 0.39
83989 FAM172A 0.39
1408 CRY2 0.39
2281 FKBP1B 0.39
51222 ZNF219 0.39
54540 FAM193B 0.39
4053 LTBP2 0.39
55184 DZANK1 0.39
5740 PTGIS 0.39
84814 PPAPDC3 0.42
79365 BHLHE41 0.42
316 AOX1 0.42
23380 SRGAP2 0.42
84033 OBSCN 0.42
90353 CTU1 0.42
9013 TAF1C 0.42
474344 GIMAP6 0.42
84883 AIFM2 0.42
58480 RHOU 0.42
65982 ZSCAN18 0.42
666 BOK 0.42
79762 C1orf115 0.42
525 ATP6V1B1 0.42
4675 NAP1L3 0.42
3257 HPS1 0.43
55781 RIOK2 0.43
63947 DMRTC1 0.43
1969 EPHA2 0.43
25927 CNRIP1 0.43
57685 CACHD1 0.43
51559 NT5DC3 0.40
7169 TPM2 0.40
51705 EMCN 0.40
8938 BAIAP3 0.40
10365 KLF2 0.40
59 ACTA2 0.40
80309 SPHKAP 0.40
3779 KCNMB1 0.41
10826 C5orf4 0.41
219654 ZCCHC24 0.41
92162 TMEM88 0.41
7450 VWF 0.41
10266 RAMP2 0.41
25875 LETMD1 0.41
1938 EEF2 0.41
121551 BTBD11 0.41
2119 ETV5 0.41
9696 CROCC 0.41
1031 CDKN2C 0.41
9037 SEMA5A 0.41
3397 ID1 0.41
84707 BEX2 0.41
57616 TSHZ3 0.41
1471 CST3 0.41
55214 LEPREL1 0.41
3914 LAMB3 0.41
57478 USP31 0.41
3783 KCNN4 0.41
8839 WISP2 0.41
1583 CYP11A1 0.42
10124 ARL4A 0.42
738 C11orf2 0.42
29800 ZDHHC1 0.42
23135 KDM6B 0.44
171024 SYNPO2 0.44
10350 ABCA9 0.44
3691 ITGB4 0.44
2348 FOLR1 0.44
11145 PLA2G16 0.44
554 AVPR2 0.45
64072 CDH23 0.45
80177 MYCT1 0.45
5957 RCVRN 0.45
408 ARRB1 0.45
29997 GLTSCR2 0.43
26051 PPP1R16B 0.43
83604 TMEM47 0.43
2308 FOXO1 0.43
55225 RAVER2 0.43
54839 LRRC49 0.43
122953 JDP2 0.43
29775 CARD10 0.43
166 AES 0.43
25924 MYRIP 0.43
2852 GPER 0.43
51421 AMOTL2 0.43
124936 CYB5D2 0.43
1294 COL7A1 0.43
127435 PODN 0.43
84952 CGNL1 0.43
83483 PLVAP 0.43
1958 EGR1 0.43
230 ALDOC 0.43
65987 KCTD14 0.43
4804 NGFR 0.44
64852 TUT1 0.44
84253 GARNL3 0.44
5866 RAB3IL1 0.44
10608 MXD4 0.44
4211 MEIS1 0.44
83547 RILP 0.44
9172 MYOM2 0.44
57192 MCOLN1 0.44
255877 BCL6B 0.44
56904 SH3GLB2 0.44
51285 RASL12 0.44
3425 IDUA 0.44
402117 VWC2L 0.46
81490 PTDSS2 0.46
283748 PLA2G4D 0.46
23523 CABIN1 0.46
6146 RPL22 0.46
85360 SYDE1 0.46
60468 BACH2 0.46
57451 ODZ2 0.46
4013 VWA5A 0.46
339768 ESPNL 0.46
3860 KRT13 0.46
144699 FBXL14 0.45
83719 YPEL3 0.45
22841 RAB11FIP2 0.45
283927 NUDT7 0.45
293 SLC25A6 0.45
90507 SCRN2 0.45
37 ACADVL 0.45
112744 IL17F 0.45
6709 SPTAN1 0.45
8086 AAAS 0.45
7423 VEGFB 0.45
64221 ROBO3 0.45
7273 TTN 0.45
2657 GDF1 0.45
59271 C21orf63 0.45
132160 PPM1M 0.45
27244 SESN1 0.45
51310 SLC22A17 0.45
4828 NMB 0.45
54360 CYTL1 0.45
203245 NAIF1 0.45
23166 STAB1 0.45
2121 EVC 0.45
116496 FAM129A 0.45
23239 PHLPP1 0.45
51673 TPPP3 0.45
64094 SMOC2 0.45
6383 SDC2 0.45
2180 ACSL1 0.45
23770 FKBP8 0.45
55901 THSD1 0.46
25895 METTL21B 0.46
23731 C9orf5 0.46
126393 HSPB6 0.46
4056 LTC4S 0.46
79825 CCDC48 0.46
10810 WASF3 0.46
29911 HOOK2 0.46
583 BBS2 0.46
28984 C13orf15 0.46
1465 CSRP1 0.46
55258 THNSL2 0.46
161198 CLEC14A 0.46
3699 ITIH3 0.48
7094 TLN1 0.46
4232 MEST 0.46
1410 CRYAB 0.46
57452 GALNTL1 0.47
63935 PCIF1 0.47
25873 RPL36 0.47
9812 KIAA0141 0.47
51665 ASB1 0.47
64123 ELTD1 0.47
6122 RPL3 0.47
222962 SLC29A4 0.47
23102 TBC1D2B 0.47
3476 IGBP1 0.47
93408 MYL10 0.47
5310 PKD1 0.47
4628 MYH10 0.47
221935 SDK1 0.47
23328 SASH1 0.47
8522 GAS7 0.47
10023 FRAT1 0.47
7301 TYRO3 0.47
2767 GNA11 0.47
9457 FHL5 0.47
4094 MAF 0.47
65268 WNK2 0.47
54585 LZTFL1 0.47
375449 MAST4 0.47
138311 FAM69B 0.47
160622 GRASP 0.47
22837 COBLL1 0.47
51435 SCARA3 0.47
217 ALDH2 0.47
6236 RRAD 0.47
8322 FZD4 0.47
653275 CFC1B 0.47
10908 PNPLA6 0.47
57526 PCDH19 0.47
8424 BBOX1 0.47
9905 SGSM2 0.48
10435 CDC42EP2 0.48
23087 TRIM35 0.48
60314 C12orf10 0.48
1073 CFL2 0.48
5256 PHKA2 0.49
92922 CCDC102A 0.48
65057 ACD 0.48
9095 TBX19 0.48
6441 SFTPD 0.48
22846 VASH1 0.48
51066 C3orf32 0.48
23179 RGL1 0.48
4664 NAB1 0.48
50511 SYCP3 0.48
6430 SRSF5 0.48
11078 TRIOBP 0.48
78991 PCYOX1L 0.48
6623 SNCG 0.48
23384 SPECC1L 0.48
53826 FXYD6 0.48
9397 NMT2 0.48
6041 RNASEL 0.48
113510 HELQ 0.48
64788 LMF1 0.48
2217 FCGRT 0.48
79720 VPS37B 0.48
6764 ST5 0.48
252969 NEIL2 0.48
8987 STBD1 0.48
41 ACCN2 0.48
7905 REEP5 0.48
5919 RARRES2 0.48
10544 PROCR 0.48
6876 TAGLN 0.48
8436 SDPR 0.49
23500 DAAM2 0.49
130132 RFTN2 0.49
80310 PDGFD 0.49
4215 MAP3K3 0.49
282775 OR5J2 0.49
51161 C3orf18 0.49
29098 RANGRF 0.49
53336 CPXCR1 0.49
9081 PRY 0.49
9459 ARHGEF6 0.49
2995 GYPC 0.49
23057 NMNAT2 0.49
4669 NAGLU 0.49
6452 SH3BP2 0.49
6237 RRAS 0.49
5288 PIK3C2G 0.49
10252 SPRY1 0.49
79026 AHNAK 0.49
9693 RAPGEF2 0.49
51226 COPZ2 0.49
158326 FREM1 0.49
1956 EGFR 0.49
5360 PLTP 0.49
290 ANPEP 0.49
1756 DMD 0.49
5118 PCOLCE 0.49
56654 NPDC1 0.49
9254 CACNA2D2 0.49
55536 CDCA7L 0.49
124975 GGT6 0.49
1906 EDN1 0.49
81029 WNT5B 0.49
2646 GCKR 0.49
9811 CTIF 0.50
145376 PPP1R36 0.50
222865 TMEM130 0.50
92999 ZBTB47 0.50
168002 DACT2 0.50
6829 SUPT5H 0.50
9992 KCNE2 0.50
58509 C19orf29 0.50
79706 PRKRIP1 0.50
1153 CIRBP 0.50
9639 ARHGEF10 0.50
4054 LTBP3 0.50
1120 CHKB 0.50
286046 XKR6 0.50
9590 AKAP12 0.50
64115 C10orf54 0.50
2067 ERCC1 0.50
7507 XPA 0.50
22897 CEP164 0.50
652 BMP4 0.50
55702 CCDC94 0.50
57613 KIAA1467 0.50
28514 DLL1 0.50
169270 ZNF596 0.50
83982 IFI27L2 0.50
51458 RHCG 0.49
1112 FOXN3 0.49
29954 POMT2 0.49
9612 NCOR2 0.49
3198 HOXA1 0.49
5311 PKD2 0.49
2946 GSTM2 0.49
2109 ETFB 0.49
56062 KLHL4 0.49
6915 TBXA2R 0.50
64288 ZNF323 0.50
5195 PEX14 0.50
84557 MAP1LC3A 0.50
6164 RPL34 0.50
8835 SOCS2 0.50
2735 GLI1 0.50
26022 TMEM98 0.50
3908 LAMA2 0.50
1825 DSC3 0.50
5730 PTGDS 0.50
162515 SLC16A11 0.51
274 BIN1 0.51
79654 HECTD3 0.51
22863 ATG14 0.51
25949 SYF2 0.51
84872 ZC3H10 0.51
23187 PHLDB1 0.51
5434 POLR2E 0.51
6181 RPLP2 0.51
6141 RPL18 0.51
84747 UNC119B 0.51
23399 CTDNEP1 0.51
599 BCL2L2 0.51
197258 FUK 0.51
5207 PFKFB1 0.51
8131 NPRL3 0.51
25839 COG4 0.51
10816 SPINT3 0.51
60485 SAV1 0.51
5681 PSKH1 0.51
80318 GKAP1 0.51
57088 PLSCR4 0.51
93129 ORAI3 0.51
5829 PXN 0.51
2247 FGF2 0.50
26248 OR2K2 0.50
84303 CHCHD6 0.50
3615 IMPDH2 0.50
1813 DRD2 0.50
80148 PQLC1 0.50
390081 OR52E4 0.50
352954 GATS 0.50
90871 C9orf123 0.50
50945 TBX22 0.52
5204 PFDN5 0.52
5338 PLD2 0.52
94 ACVRL1 0.52
54039 PCBP3 0.52
7691 ZNF132 0.52
338 APOB 0.52
84658 EMR3 0.52
283232 TMEM80 0.52
5430 POLR2A 0.52
54623 PAF1 0.52
11070 TMEM115 0.52
10395 DLC1 0.52
57140 RNPEPL1 0.52
79781 IQCA1 0.52
1838 DTNB 0.52
51386 EIF3L 0.52
56919 DHX33 0.52
57542 KLHDC5 0.52
3628 INPP1 0.52
4520 MTF1 0.52
8547 FCN3 0.52
60401 EDA2R 0.52
8082 SSPN 0.52
80755 AARSD1 0.52
710 SERPING1 0.52
56246 MRAP 0.52
10555 AGPAT2 0.52
949 SCARB1 0.52
23743 BHMT2 0.52
3910 LAMA4 0.52
60370 AVPI1 0.52
5021 OXTR 0.52
55997 CFC1 0.52
23144 ZC3H3 0.52
56776 FMN2 0.51
85456 TNKS1BP1 0.51
283 ANG 0.51
7035 TFPI 0.51
51232 CRIM1 0.51
112616 CMTM7 0.51
22981 NINL 0.51
8727 CTNNAL1 0.51
9902 MRC2 0.51
10900 RUNDC3A 0.51
51299 NRN1 0.51
79632 FAM184A 0.52
80820 EEPD1 0.52
150709 ANKAR 0.52
6591 SNAI2 0.52
10129 FRY 0.52
5166 PDK4 0.52
146433 IL34 0.52
118812 MORN4 0.53
10516 FBLN5 0.53
9463 PICK1 0.53
127495 LRRC39 0.53
7753 ZNF202 0.53
79827 CLMP 0.53
203260 CCDC107 0.53
83657 DYNLRB2 0.53

Table S1.

Up-regulated genes found in dataset GSE29174. There are 437 up-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene ID Gene Symbol Fold Change
1300 COL10A1 42.74
3007 HIST1H1D 29.72
8366 HIST1H4B 25.58
6286 S100P 25.19
1301 COL11A1 24.72
3627 CXCL10 17.83
4283 CXCL9 15.88
1387 CREBBP 12.83
27299 ADAMDEC1 12.78
54986 ULK4 12.46
55771 PRR11 12.02
54790 TET2 11.25
6241 RRM2 10.60
3433 IFIT2 10.49
6999 TDO2 9.73
1656 DDX6 9.72
55088 C10orf118 9.37
9648 GCC2 9.24
6696 SPP1 8.92
2803 GOLGA4 8.57
83540 NUF2 7.73
10112 KIF20A 7.66
9833 MELK 7.59
55165 CEP55 7.50
10142 AKAP9 7.44
9447 AIM2 7.42
54443 ANLN 5.79
6710 SPTB 5.71
7272 TTK 5.64
10635 RAD51AP1 5.49
4069 LYZ 5.37
55183 RIF1 5.34
891 CCNB1 5.34
91543 RSAD2 5.31
81610 FAM83D 5.24
64581 CLEC7A 5.10
10051 SMC4 5.02
4085 MAD2L1 4.96
55872 PBK 4.83
991 CDC20 4.82
9221 NOLC1 4.74
2124 EVI2B 4.66
375248 ANKRD36 4.66
1164 CKS2 4.64
1230 CCR1 4.62
890 CCNA2 4.56
127933 UHMK1 4.49
10274 STAG1 4.45
597 BCL2A1 4.43
55355 HJURP 4.41
54210 TREM1 4.36
253558 LCLAT1 4.26
2706 GJB2 7.33
6498 SKIL 7.13
219285 SAMD9L 7.06
10261 IGSF6 7.01
2335 FN1 6.95
699 BUB1 6.75
1058 CENPA 6.75
332 BIRC5 6.73
51203 NUSAP1 6.59
259266 ASPM 6.54
1063 CENPF 6.49
165918 RNF168 6.44
9232 PTTG1 6.34
5996 RGS1 6.07
29089 UBE2T 5.96
22974 TPX2 5.94
4321 MMP12 5.91
983 CDK1 5.89
85444 LRRCC1 5.87
29121 CLEC2D 3.83
4090 SMAD5 3.80
2123 EVI2A 3.80
57695 USP37 3.79
133418 EMB 3.76
4131 MAP1B 3.76
9787 DLGAP5 3.75
9768 KIAA0101 3.74
54625 PARP14 3.73
2215 FCGR3B 3.71
9134 CCNE2 3.70
3117 HLA-DQA1 3.68
10380 BPNT1 3.67
79056 PRRG4 3.63
10673 TNFSF13B 3.63
8467 SMARCA5 3.61
115908 CTHRC1 3.61
3428 IFI16 3.61
1520 CTSS 3.61
10797 MTHFD2 3.57
55681 SCYL2 3.57
9749 PHACTR2 3.57
94240 EPSTI1 3.56
64151 NCAPG 3.51
25879 DCAF13 3.51
1033 CDKN3 4.24
79801 SHCBP1 4.23
126731 C1orf96 4.21
6772 STAT1 4.20
55729 ATF7IP 4.14
6713 SQLE 4.14
157570 ESCO2 4.10
79871 RPAP2 4.09
9493 KIF23 4.09
4751 NEK2 4.05
10631 POSTN 4.03
23515 MORC3 4.02
7153 TOP2A 4.02
10403 NDC80 4.00
10915 TCERG1 3.99
57650 KIAA1524 3.99
23049 SMG1 3.93
80231 CXorf21 3.87
5111 PCNA 3.86
79682 MLF1IP 3.11
29123 ANKRD11 3.09
5429 POLH 3.09
701 BUB1B 3.07
200030 NBPF11 3.06
55677 IWS1 3.06
160418 TMTC3 3.04
9147 NEMF 3.04
11320 MGAT4A 3.04
5238 PGM3 3.03
2820 GPD2 3.02
388886 FAM211B 3.01
7852 CXCR4 3.00
57082 CASC5 2.99
22926 ATF6 2.98
7594 ZNF43 2.98
968 CD68 2.97
7171 TPM4 2.96
11004 KIF2C 2.96
10808 HSPH1 2.95
84909 C9orf3 2.94
1894 ECT2 2.93
1629 DBT 2.92
116969 ART5 2.90
3227 HOXC11 2.88
116064 LRRC58 3.47
29899 GPSM2 3.47
135114 HINT3 3.45
27333 GOLIM4 3.43
55839 CENPN 3.43
23213 SULF1 3.41
81671 VMP1 3.39
9889 ZBED4 3.36
3092 HIP1 3.34
51512 GTSE1 3.34
92797 HELB 3.34
51426 POLK 3.30
5611 DNAJC3 3.30
6596 HLTF 3.28
9910 RABGAP1L 3.25
528 ATP6V1C1 3.23
3833 KIFC1 3.23
197131 UBR1 3.20
29923 HILPDA 3.20
28998 MRPL13 3.19
58527 C6orf115 3.19
79000 C1orf135 3.19
9857 CEP350 3.18
84296 GINS4 3.18
81034 SLC25A32 3.15
55723 ASF1B 3.14
7110 TMF1 3.14
84081 NSRP1 3.14
23075 SWAP70 3.12
6726 SRP9 2.69
55215 FANCI 2.68
57590 WDFY1 2.67
55142 HAUS2 2.66
23047 PDS5B 2.66
5373 PMM2 2.66
11065 UBE2C 2.66
23085 ERC1 2.66
389197 C4orf50 2.65
11260 XPOT 2.65
29980 DONSON 2.65
64399 HHIP 2.64
6453 ITSN1 2.63
29108 PYCARD 2.63
9877 ZC3H11A 2.62
3149 HMGB3 2.87
10437 IFI30 2.87
57489 ODF2L 2.87
2151 F2RL2 2.86
23215 PRRC2C 2.85
128710 C20orf94 2.85
23594 ORC6 2.84
5205 ATP8B1 2.83
51430 C1orf9 2.80
57405 SPC25 2.80
112401 BIRC8 2.80
3606 IL18 2.80
115362 GBP5 2.80
50515 CHST11 2.79
83461 CDCA3 2.79
10744 PTTG2 2.78
51765 MST4 2.77
10926 DBF4 2.76
27125 AFF4 2.75
10615 SPAG5 2.75
55143 CDCA8 2.74
51602 NOP58 2.74
51478 HSD17B7 2.73
2209 FCGR1A 2.73
9958 USP15 2.72
5469 MED1 2.72
8813 DPM1 2.70
6731 SRP72 2.70
9991 PTBP3 2.70
79866 BORA 2.41
7072 TIA1 2.40
55632 G2E3 2.40
2213 FCGR2B 2.40
3987 LIMS1 2.39
829 CAPZA1 2.39
26973 CHORDC1 2.38
435 ASL 2.38
29979 UBQLN1 2.38
8548 BLZF1 2.37
9694 TTC35 2.37
55055 ZWILCH 2.36
4481 MSR1 2.36
10213 PSMD14 2.35
9966 TNFSF15 2.35
81624 DIAPH3 2.62
79723 SUV39H2 2.61
55789 DEPDC1B 2.61
10097 ACTR2 2.59
23036 ZNF292 2.58
22936 ELL2 2.57
8477 GPR65 2.57
23397 NCAPH 2.57
3015 H2AFZ 2.54
55749 CCAR1 2.53
25937 WWTR1 2.52
360023 ZBTB41 2.51
5080 PAX6 2.51
4193 MDM2 2.51
24137 KIF4A 2.51
9212 AURKB 2.51
168850 ZNF800 2.50
55109 AGGF1 2.49
23185 LARP4B 2.49
51571 FAM49B 2.49
51077 FCF1 2.49
23167 EFR3A 2.49
23468 CBX5 2.48
5396 PRRX1 2.48
10096 ACTR3 2.47
10308 ZNF267 2.47
6782 HSPA13 2.47
3832 KIF11 2.47
917 CD3G 2.47
80821 DDHD1 2.46
52 ACP1 2.46
4179 CD46 2.46
10499 NCOA2 2.44
60558 GUF1 2.44
55676 SLC30A6 2.43
6646 SOAT1 2.43
5440 POLR2K 2.43
84955 NUDCD1 2.42
54739 XAF1 2.42
84295 PHF6 2.23
7295 TXN 2.23
2710 GK 2.23
10905 MAN1A2 2.22
6780 STAU1 2.22
51582 AZIN1 2.35
54843 SYTL2 2.34
9039 UBA3 2.33
933 CD22 2.33
5685 PSMA4 2.33
9885 OSBPL2 2.33
9262 STK17B 2.33
56942 C16orf61 2.32
10767 HBS1L 2.32
87178 PNPT1 2.32
6303 SAT1 2.32
7316 UBC 2.32
4205 MEF2A 2.32
85465 EPT1 2.31
84640 USP38 2.31
5810 RAD1 2.30
64397 ZFP106 2.29
5706 PSMC6 2.29
22948 CCT5 2.29
10672 GNA13 2.29
339344 MYPOP 2.28
7292 TNFSF4 2.28
57103 C12orf5 2.28
388403 YPEL2 2.28
54876 DCAF16 2.27
113235 SLC46A1 2.27
11177 BAZ1A 2.27
339175 METTL2A 2.26
26586 CKAP2 2.26
55785 FGD6 2.26
24145 PANX1 2.25
253461 ZBTB38 2.25
23232 TBC1D12 2.25
995 CDC25C 2.25
55974 SLC50A1 2.25
472 ATM 2.25
23008 KLHDC10 2.24
10024 TROAP 2.24
9521 EEF1E1 2.24
7402 UTRN 2.09
55589 BMP2K 2.08
158747 MOSPD2 2.08
56886 UGGT1 2.07
203100 HTRA4 2.07
10282 BET1 2.22
134430 WDR36 2.21
4299 AFF1 2.21
6747 SSR3 2.21
7334 UBE2N 2.21
5965 RECQL 2.21
4605 MYBL2 2.2
6093 ROCK1 2.19
161725 OTUD7A 2.19
23518 R3HDM1 2.18
2239 GPC4 2.18
28977 MRPL42 2.18
64859 OBFC2A 2.18
3845 KRAS 2.18
51388 NIP7 2.18
7586 ZKSCAN1 2.18
10762 NUP50 2.17
7328 UBE2H 2.17
10730 YME1L1 2.17
23093 TTLL5 2.17
6790 AURKA 2.17
22889 KIAA0907 2.17
10875 FGL2 2.17
23161 SNX13 2.17
9169 SCAF11 2.16
1788 DNMT3A 2.15
9088 PKMYT1 2.15
23033 DOPEY1 2.13
89882 TPD52L3 2.13
6556 SLC11A1 2.13
64216 TFB2M 2.13
3071 NCKAP1L 2.13
51068 NMD3 2.13
509 ATP5C1 2.13
953 ENTPD1 2.13
51105 PHF20L1 2.13
5062 PAK2 2.13
9205 ZMYM5 2.12
55157 DARS2 2.12
8520 HAT1 2.11
79739 TTLL7 2.11
9495 AKAP5 2.10
3181 HNRNPA2B
1
2.10
55279 ZNF654 2.07
54499 TMCO1 2.07
81930 KIF18A 2.07
142686 ASB14 2.06
55209 SETD5 2.06
9736 USP34 2.04
116285 ACSM1 2.04
2201 FBN2 2.04
963 CD53 2.04
55159 RFWD3 2.03
9871 SEC24D 2.03
9887 SMG7 2.02
23376 UFL1 2.02
79646 PANK3 2.01
50613 UBQLN3 2.00
201595 STT3B 2.00
59345 GNB4 1.99
5876 RABGGTB 1.99
79820 CATSPERB 1.99
6637 SNRPG 1.99
51330 TNFRSF12A 1.99
9928 KIF14 1.99
286097 EFHA2 1.98
9131 AIFM1 1.98
488 ATP2A2 1.98
23042 PDXDC1 1.98
7114 TMSB4X 1.98
9123 SLC16A3 1.98
54454 ATAD2B 1.97
23143 LRCH1 1.97
4212 MEIS2 1.97
1457 CSNK2A1 1.97
80012 PHC3 1.97
128497 SPATA25 1.96
186 AGTR2 1.96
53981 CPSF2 1.96
56996 SLC12A9 1.96
1584 CYP11B1 1.96
133619 PRRC1 1.96
4288 MKI67 1.96
9014 TAF1B 1.96
55858 TMEM165 1.96
2212 FCGR2A 1.96
389898 UBE2NL 2.10
29850 TRPM5 2.10
3070 HELLS 2.10
331 XIAP 2.09
55751 TMEM184C 2.09
2146 EZH2 2.09
26057 ANKRD17 1.95
128061 C1orf131 1.95
64090 GAL3ST2 1.94
130507 UBR3 1.93
2298 FOXD4 1.93
123169 LEO1 1.93
57187 THOC2 1.93
148789 B3GALNT2 1.93
58508 MLL3 1.92
5701 PSMC2 1.92
148066 ZNRF4 1.92
6670 SP3 1.92
10075 HUWE1 1.96
220988 HNRNPA3 1.96
80146 UXS1 1.95
122011 CSNK1A1L 1.95
150468 CKAP2L 1.95
84624 FNDC1 1.95
7332 UBE2L3 1.92
3336 HSPE1 1.92
54800 KLHL24 1.92
2290 FOXG1 1.91
50848 F11R 1.91
10627 MYL12A 1.91
5074 PAWR 1.91
6476 SI 1.91
1009 CDH11 1.90
29066 ZC3H7A 1.90
51319 RSRC1 1.90

Table S4.

Result of ROC curve analysis on our miRNA array data. ROC analysis was done to validate the diagnostic value of the miRNA in the miRNA-regulated PINs.

miRBase Accession miRNA name AUC p-value
MIMAT0002856 hsa-miR-520d-3p 0.49 0.549112
MIMAT0000265 hsa-miR-204-5p 0.98 6.47 × 10−10 ***
MIMAT0000272 hsa-miR-215 0.21 0.999782
MIMAT0000271 hsa-miR-214-3p 0.68 0.010387 *
MIMAT0002820 hsa-miR-497-5p 0.99 2.75 × 10−10 ***
MIMAT0000076 hsa-miR-21-5p 0.78 0.000184 ***
MIMAT0000738 hsa-miR-383 0.60 0.106284
MIMAT0000423 hsa-miR-125b-5p 0.99 2.48 × 10−10 ***
MIMAT0000064 hsa-let-7c 0.93 3.79 × 10−8 ***
MIMAT0000089 hsa-miR-31-5p 0.80 8.63 × 10−5 ***
MIMAT0000077 hsa-miR-22-3p 0.27 0.99749
MIMAT0000098 hsa-miR-100-5p 0.98 5.55 × 10−10 ***
MIMAT0000097 hsa-miR-99a-5p 0.99 2.55 × 10−10 ***
MIMAT0000443 hsa-miR-125a-5p 0.31 0.990694
MIMAT0002819 hsa-miR-193b-3p 0.41 0.86128
MIMAT0000250 hsa-miR-139-5p 0.99 2.42 × 10−10 ***
MIMAT0000437 hsa-miR-145-5p 0.96 3.14 × 10−9 ***
MIMAT0000421 hsa-miR-122-5p 0.48 0.597483

AUC: area under (ROC) curve;

*

p-value < 0.05;

***

p-value < 0.001.

Table S5.

Summary of constructed miRNA-regulated networks. L0 gene: genes connected directly to the miRNA (i.e., direct target of miRNA); L1 gene: genes not connected directly to the miRNA.

miRBase Accession miR name Total gene count L0 count L1 count
MIMAT0002819 hsa-miR-193b-3p 16 1 15
MIMAT0000250 hsa-miR-139-5p 28 10 18
MIMAT0000437 hsa-miR-145-5p 86 22 64
MIMAT0000423 hsa-miR-125b-5p 211 16 195
MIMAT0000443 hsa-miR-125a-5p 206 14 192
MIMAT0000097 hsa-miR-99a-5p 14 1 13
MIMAT0000265 hsa-miR-204-5p 64 18 46
MIMAT0000076 hsa-miR-21-5p 91 16 75
MIMAT0000064 hsa-let-7c 96 20 76
MIMAT0000421 hsa-miR-122-5p 5 3 2
MIMAT0000098 hsa-miR-100-5p 14 1 13
MIMAT0000272 hsa-miR-215 3 3 0
MIMAT0000271 hsa-miR-214-3p 14 8 6
MIMAT0000738 hsa-miR-383 34 3 31
MIMAT0002856 hsa-miR-520d-3p 146 23 123
MIMAT0000077 hsa-miR-22-3p 46 11 35
MIMAT0002820 hsa-miR-497-5p 267 32 235
MIMAT0000089 hsa-miR-31-5p 34 3 31

Table S6.

Specific enriched GO terms of each miRNA-regulated PINs. Genes annotated with the specific GO term in the PIN were also listed in this table. Adj. p-value: multiple-test adjusted p-value calculated by the method described in the work of Benjamini and Yekutieli [48].

MIMAT0002856(hsa-miR-520d-3p)

GO term Genes Adj. p-value
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway SH3KBP1, HDAC2, RET, ABI1, LYN, GRB2, SORBS1, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, RPS6, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, EIF4G1, HDAC1, ECT2, MKNK1, CASP3, PRKACA, ADRB2, PRKAR2A, EIF4B, SHC1, RAC1 2.77 × 10−29

GO:0048011, Nerve growth factor receptor signaling pathway HDAC2, GRB2, CLTC, CLTA, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, HDAC1, ECT2, CASP3, PRKACA, PRKAR2A, SHC1, RAC1 5.09 × 10−24

GO:0007173, Epidermal growth factor receptor signaling pathway SH3KBP1, GRB2, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, CDK1, SH3GL2, PRKACA, PRKAR2A, SHC1 2.96 × 10−22

GO:0043067, Regulation of programmed cell death HDAC2, STK17B, ESR1, ABL1, LYN, TP53, GABRB3, PAK2, LCK, CASP9, RAF1, PLK1, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, IFT57, ARHGAP10, ARHGEF6, CDK1, APAF1, HDAC1, ECT2, CASP3, SOX10, EP300, ARAF, TFAP2A, ADRB2, HCK, KLHL20, CASP8, HIP1, RAC1 4.76 × 10−19

GO:0042058, Regulation of epidermal growth factor receptor signaling pathway SH3KBP1, ESR1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL2, SHC1 2.36 × 10−12

GO:0008543, Fibroblast growth factor receptor signaling pathway GRB2, CASP9, RAF1, SRC, MAPK3, PRKCA, PRKAR2B, MAPK1, CDK1, MKNK1, PRKACA, PRKAR2A, SHC1 2.39 × 10−12

GO:0043068, Positive regulation of programmed cell death STK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC1 3.09 × 10−12

GO:0010942, Positive regulation of cell death STK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC1 4.49 × 10−12

GO:0006917, Induction of apoptosis STK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC1 6.91 × 10−11

GO:0012502, Induction of programmed cell death STK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC1 7.42 × 10−11

GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway SH3KBP1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL2 8.63 × 10−11

GO:0015630, Microtubule cytoskeleton STMN1, SORBS1, SMAD4, CLTC, CDC42, LCK, RACGAP1, PLK1, PRKAR2B, YES1, MAPK1, IFT57, CDK1, ECT2, PRKACA, RB1, EP300, CCNB1, CHAF1B, TFAP2A, CASP8, PRKAR2A 4.75 × 10−10

GO:0060548, Negative regulation of cell death HDAC2, ESR1, TP53, SMAD4, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL20 6.27 × 10−8

GO:0008286, Insulin receptor signaling pathway GRB2, SORBS1, RAF1, MAPK3, RPS6, MAPK1, CDK1, EIF4G1, EIF4B, SHC1 2.15 × 10−7

GO:0043069, Negative regulation of programmed cell death HDAC2, ESR1, TP53, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL20 3.13 × 10−7

GO:0008284, Positive regulation of cell proliferation HDAC2, ESR1, LYN, CDC42, E2F1, PRKCA, MAPK1, CDK1, RHOG, HDAC1, NCK1, SOX10, CCNB1, ADRB2, HCK, SHC1 8.34 × 10−7

GO:0051988, Regulation of attachment of spindle microtubules to kinetochore CDC42, RACGAP1, ECT2, CCNB1 3.27 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals ABL1, TP53, CASP9, APAF1, CASP3, EP300, CASP8 5.83 × 10−5

MIMAT0002820(hsa-miR-497-5p)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death ESR1, MEN1, ABL1, HIPK3, PPARGC1A, SIAH1, SH3RF1, PAK2, LCK, MED1, PPARG, CBX4, ARHGEF7, YWHAB, RXRA, ACVR1, MAPK1, CASP3, CASP6, AR, PTPRF, MDM2, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, VHL, SKI, NR4A1, LYN, TP53, PSMC2, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, PSMD11, HOXA13, RPS6, ESR2, ARHGAP10, ARHGEF6, SMAD3, SKIL, RYR2, PSEN1, HCK, TRAF2 2.67 × 10−25

GO:0043068, Positive regulation of programmed cell death MEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 1.74 × 10−17

GO:0010942, Positive regulation of cell death MEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 3.08 × 10−17
GO:0008285, Negative regulation of cell proliferation MEN1, MED1, PPARG, RXRA, CASP3, AR, PTPRF, VDR, VHL, SKI, LYN, TP53, TOB1, GATA1, GATA3, RAF1, HNF4A, CDKN1B, BRD7, MED25, ESR2, ABI1, SMAD1, SMAD2, SMAD3, SMAD4, SOX7 3.85 × 10−14

GO:0015629, Actin cytoskeleton ABL1, SORBS1, FLNA, SEPT7, ANLN, MACF1, HAP1, SH3PXD2A, IQGAP2, BRCA1, ACTC1, ACTA1, MYL2, MYLK, SORBS2, ARPC4, ARPC5, ACTR2, ACTR3, ARPC1B, WASF1, WASF2, HCK 2.52 × 10−13

GO:0006917, Induction of apoptosis ABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 9.69 × 10−11

GO:0012502, Induction of programmed cell death ABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 1.06 × 10−10

GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway ACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, GATA4, HNF4A, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, RYR2 1.22 × 10−10

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway SORBS1, CDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, EIF4G1, RAC1, SH3KBP1, NR4A1, LYN, GRB2, RAF1, CDKN1B, RPS6, ABI1, ARHGEF6, MKNK1, PSEN1, EIF4B 1.67 × 10−10

GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway MEN1, ACVR1, SMURF2, SKI, GDF6, TP53, BMP6, GATA4, GATA6, HOXA13, SMAD2, SMAD3, SMAD4, SKIL 7.51 × 10−10

GO:0030509, BMP signaling pathway ACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, SMAD1, SMAD4, SMAD5, RYR2 3.25 × 10−9

GO:0060548, Negative regulation of cell death ESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, SMAD4, PSEN1, HCK 7.88 × 10−9

GO:0007173, Epidermal growth factor receptor signaling pathway CDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, MDM2, SH3KBP1, NR4A1, GRB2, RAF1, CDKN1B 9.22 × 10−9

GO:0030521, Androgen receptor signaling pathway PPARGC1A, MED14, MED1, AR, BRCA1, MED12, PIAS1, RAN, NR1I3 1.42 × 10−8

GO:0043069, Negative regulation of programmed cell death ESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, PSEN1, HCK 2.44 × 10−8

GO:0048011, Nerve growth factor receptor signaling pathway SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, RAC1, NR4A1, GRB2, RAF1, CDKN1B, ARHGEF6, PSEN1 2.64 × 10−8

GO:0032956, Regulation of actin cytoskeleton organization ABL1, LRP1, ARPC4, ARPC5, ACTR3, ARPC1B, SMAD3, NCK1, SORBS3, HCK, LIMK1 6.42 × 10−6

GO:0008543, Fibroblast growth factor receptor signaling pathway SRC, MAPK3, YWHAB, MAPK1, MDM2, NR4A1, GRB2, RAF1, CDKN1B, MKNK1 9.06 × 10−6

GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway CDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN1 1.11 × 10−5

GO:0042058, Regulation of epidermal growth factor receptor signaling pathway ESR1, CDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN1 1.17 × 10−5

GO:0007179, Transforming growth factor beta receptor signaling pathway ACVR1, SMURF2, SKI, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5 1.67 × 10−5

GO:0015630, Microtubule cytoskeleton STMN1, RIF1, SORBS1, CDC42, LCK, RACGAP1, YES1, YWHAB, MAPK1, SEPT7, KIF23, CDC16, MACF1, BRCA1, FEZ1, NCOR1, PLK1, CHD3, SMAD4, CEP350, CDC27, PSEN1 2.14 × 10−5

GO:0017015, Regulation of transforming growth factor beta receptor signaling pathway MEN1, SMURF2, SKI, TP53, SMAD2, SMAD3, SMAD4, SKIL 2.30 × 10−5

GO:0070302, Regulation of stress-activated protein kinase signaling cascade MEN1, ZEB2, HIPK3, SH3RF1, CDC42, MAPK3, MAPK1, LYN, NCOR1, TRAF2 2.74 × 10−5

GO:0001959, Regulation of cytokine-mediated signaling pathway HSP90AB1, MED1, PPARG, PTPRF, NR1H2, PIAS1, IL36RN, HIPK1 6.35 × 10−5

GO:0008284, Positive regulation of cell proliferation ESR1, CDC42, MED1, RARA, MAPK1, AR, MDM2, NR4A1, LYN, FZR1, BMP6, GATA1, GATA4, GATA6, CDKN1B, NCK1, HCLS1, HCK 7.29 × 10−5

MIMAT0000423(hsa-miR-125b-5p)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death HMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B1 4.56 × 10−24

GO:0060548, Negative regulation of cell death HMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B1 8.04 × 10−17

GO:0008284, Positive regulation of cell proliferation HMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN 2.20 × 10−15
GO:0043069, Negative regulation of programmed cell death HMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B1 3.62 × 10−15

GO:0043068, Positive regulation of programmed cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF2 4.51 × 10−14

GO:0010942, Positive regulation of cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF2 7.15 × 10−14

GO:0006916, Anti-apoptosis PRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B1 1.25 × 10−10

GO:0008285, Negative regulation of cell proliferation SERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN 2.08 × 10−9

GO:0015630, Microtubule cytoskeleton STMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B1 2.24 × 10−9

GO:0048011, Nerve growth factor receptor signaling pathway HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA 1.99 × 10−8

GO:0050678, Regulation of epithelial cell proliferation SERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN 3.41 × 10−8

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway FGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP1 6.39 × 10−8
GO:0006917, Induction of apoptosis PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.68 × 10−7

GO:0012502, Induction of programmed cell death PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.81 × 10−7

GO:0035666, TRIF-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.40 × 10−6

GO:0034138, Toll-like receptor 3 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.71 × 10−6

GO:0051693, Actin filament capping SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 3.43 × 10−6
GO:0002756, MyD88- independent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 3.82 × 10−6

GO:0034134, Toll-like receptor 2 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 5.33 × 10−6

GO:0034130, Toll-like receptor 1 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 5.33 × 10−6

GO:0030835, Negative regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 5.58 × 10−6

GO:0015629, Actin cytoskeleton WAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB49 5.58 × 10−6

GO:0002755, MyD88-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 7.66 × 10−6

GO:0034142, Toll-like receptor 4 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 1.14 × 10−5

GO:0050679, Positive regulation of epithelial cell proliferation FGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1 1.14 × 10−5

MIMAT0000076(hsa-miR-21-5p)

GO term Genes Adj.p-value

GO:0030834, Regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 1.27 × 10−5

GO:0030837, Negative regulation of actin filament polymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.71 × 10−5

GO:0002224, Toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.96 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals PML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI16 3.52 × 10−5

GO:0043067, Regulation of programmed cell death SPRY2, TP53, ADAMTSL4, ETS1, TDGF1, RAF1, HOXA5, HOXA13, MSX1, MSX2, NKX2-5, CBL, INHBB, COL4A3, ACVR1C, TRAF2 1.92 × 10−5
GO:0007173, Epidermal growth factor receptor signaling pathway SPRY2, SPRY1, GRB2, PTPN11, TDGF1, RAF1, CBL 9.44 × 10−5

MIMAT0000250(hsa-miR-139-5p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta LTBP1, FBN1, FBN2 2.61 × 10−5

MIMAT0000089(hsa-miR-31-5p)

GO term Genes Adj.p-value

GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messenger GNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR4 8.74 × 10−7

GO:0019935, Cyclic-nucleotidemediated signaling GNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR4 1.60 × 10−6

GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messenger GNA12, GNA13, DRD5, S1PR3, TSHR, S1PR4 6.82 × 10−6

GO:0048011, Nerve growth factor receptor signaling pathway ARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF11 6.93 × 10−6

GO:0019933, CAMP-mediated signaling GNA12, GNA13, DRD5, S1PR3, TSHR, S1PR4 1.05 × 10−5

GO:0043067, Regulation of programmed cell death PTK2B, PRKCD, TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11, F2R 1.25 × 10−5

GO:0003376, Sphingosine-1- phosphate signaling pathway S1PR3, S1PR2, S1PR4 3.22 × 10−5

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway PTK2B, ARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF11 5.20 × 10−5

GO:0043068, Positive regulation of programmed cell death TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 7.67 × 10−5

GO:0010942, Positive regulation of cell death TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 8.78 × 10−5

GO:0006917, Induction of apoptosis TGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 9.32 × 10−5
GO:0012502, Induction of programmed cell death TGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 9.51 × 10−5

MIMAT0000437(hsa-miR-145-5p)

GO term Genes Adj.p-value

GO:0030509, BMP signaling pathway BMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF6 1.37 × 10−11

GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway BMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF6 2.56 × 10−8

GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway MEN1, TP53, BMP6, HOXA13, ACVR1, SMAD4, SULF1, SMURF2, GDF6 8.22 × 10−8

MIMAT0000064(hsa-let-7c)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, PSMA3, BIRC5, ACTN2, HOXA13, IRS2, FASTK, VAV1, PSMB6, BCL2, CDK1, HDAC1, SOX10, TIA1, AKT1, AURKB 3.43 × 10−8

GO:0043069, Negative regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 6.58 × 10−6

GO:0060548, Negative regulation of cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 8.92 × 10−6

GO:0015630, Microtubule cytoskeleton INCENP, SNTB2, SEPT1, TACC1, BIRC5, RACGAP1, PIN4, CDCA8, CDK1, PHF1, AKT1, AURKB, NINL, CCDC85B 5.15 × 10−5

GO:0043067, Regulation of programmed cell death HMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B1 1.62 × 10−23

GO:0060548, Negative regulation of cell death HMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B1 4.53 × 10−17

GO:0008284, Positive regulation of cell proliferation HMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN 1.23 × 10−15

MIMAT0000443(hsa-miR-125a-5p)

GO term Genes Adj.p-value

GO:0043069, Negative regulation of programmed cell death HMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B1 2.02 × 10−15

GO:0043068, Positive regulation of programmed cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF2 2.80 × 10−13

GO:0010942, Positive regulation of cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF2 4.35 × 10−13

GO:0006916, Anti-apoptosis PRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B1 9.04 × 10−11

GO:0008285, Negative regulation of cell proliferation SERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN 1.32 × 10−9

GO:0015630, Microtubule cytoskeleton STMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B1 1.32 × 10−9

GO:0048011, Nerve growth factor receptor signaling pathway HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA 1.50 × 10−8

GO:0050678, Regulation of epithelial cell proliferation SERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN 2.66 × 10−8

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway FGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP1 4.37 × 10−8

GO:0006917, Induction of apoptosis PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.22 × 10−7

GO:0012502, Induction of programmed cell death PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.31 × 10−7

GO:0035666, TRIF-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.01 × 10−6

GO:0034138, Toll-like receptor 3 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.27 × 10−6

GO:0051693, Actin filament capping SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.97 × 10−6

GO:0002756, MyD88- independent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 3.20 × 10−6

GO:0015629, Actin cytoskeleton WAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB49 4.25 × 10−6

GO:0034134, Toll-like receptor 2 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 4.43 × 10−6

GO:0034130, Toll-like receptor 1 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 4.43 × 10−6

GO:0030835, Negative regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 4.72 × 10−6

GO:0002755, MyD88-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 6.41 × 10−6

GO:0050679, Positive regulation of epithelial cell proliferation FGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1 9.31 × 10−6

GO:0034142, Toll-like receptor 4 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 9.31 × 10−6

GO:0030834, Regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 1.09 × 10−5

GO:0030837, Negative regulation of actin filament polymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.37 × 10−5

GO:0002224, Toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.48 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals PML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI16 2.92 × 10−5

GO:0050851, Antigen receptor-mediated signaling pathway WAS, MEF2C, RELA, PSEN2, PTPRC, PAK1, PTEN, NFKBIA 8.55 × 10−5

GO:0002221, Pattern recognition receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 9.15 × 10−5

GO:0001936, Regulation of endothelial cell proliferation FGF2, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, JUN 9.47 × 10−5

MIMAT0000077(hsa-miR-22-3p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta FBN2, FBN1, LTBP1 1.45 × 10−5

MIMAT0000265(hsa-miR-204-5p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta FBN1, FBN2, LTBP1 6.31 × 10−5

Table S7.

GOBO survival analysis results. Genes which was annotated with the specified GO term of proteins in the PIN would be used as input gene set for GOBO analysis.

miRNA GO term p-value
MIMAT0000064(hsa-let-7c) GO:0015630, Microtubule cytoskeleton 9.97 × 106***
GO:0043067, Regulation of programmed cell death 0.268067
GO:0043069, Negative regulation of programmed cell death 0.0390439*
GO:0060548, Negative regulation of cell death 0.0390439*

MIMAT0000076(hsa-miR-21-5p) GO:0007173, Epidermal growth factor receptor signaling pathway 0.721139
GO:0043067, Regulation of programmed cell death 0.266312

MIMAT0000077(hsa-miR-22-3p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000089(hsa-miR-31-5p) GO:0003376, Sphingosine-1-phosphate signaling pathway 0.062202
GO:0006917, Induction of apoptosis 0.048584*
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.050408
GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messenger 0.289466
GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messenger 0.687572
GO:0010942, Positive regulation of cell death 0.356228
GO:0012502, Induction of programmed cell death 0.048584*
GO:0019933, CAMP-mediated signaling 0.687572
GO:0019935, Cyclic-nucleotide-mediated signaling 0.289466
GO:0043067, Regulation of programmed cell death 0.694486
GO:0043068, Positive regulation of programmed cell death 0.356228
GO:0048011, Nerve growth factor receptor signaling pathway 0.154543

MIMAT0000250(hsa-miR-139-5p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000265(hsa-miR-204-5p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000423(hsa-miR-125b-5p) GO:0002224, Toll-like receptor signaling pathway 0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway 0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway 0.380928
GO:0006916, Anti-apoptosis 0.0593
GO:0006917, Induction of apoptosis 0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.776269
GO:0008284, Positive regulation of cell proliferation 0.882324
GO:0008285, Negative regulation of cell proliferation 0.883393
GO:0008629, Induction of apoptosis by intracellular signals 0.073118
GO:0010942, Positive regulation of cell death 0.972892
GO:0012502, Induction of programmed cell death 0.618064
GO:0015629, Actin cytoskeleton 0.596528
GO:0015630, Microtubule cytoskeleton 0.028245*
GO:0030834, Regulation of actin filament depolymerization 0.654383
GO:0030835, Negative regulation of actin filament depolymerization 0.654383
GO:0030837, Negative regulation of actin filament polymerization 0.654383
GO:0034130, Toll-like receptor 1 signaling pathway 0.380928
GO:0034134, Toll-like receptor 2 signaling pathway 0.380928
GO:0034138, Toll-like receptor 3 signaling pathway 0.380928
GO:0034142, Toll-like receptor 4 signaling pathway 0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway 0.380928
GO:0043067, Regulation of programmed cell death 0.643418
GO:0043068, Positive regulation of programmed cell death 0.972892
GO:0043069, Negative regulation of programmed cell death 0.492576
GO:0048011, Nerve growth factor receptor signaling pathway 0.171634
GO:0050678, Regulation of epithelial cell proliferation 0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation 0.205483
GO:0051693, Actin filament capping 0.654383
GO:0060548, Negative regulation of cell death 0.413665

MIMAT0000437(hsa-miR-145-5p) GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway 0.196953
GO:0030509, BMP signaling pathway 0.196953
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 0.843529

MIMAT0000443(hsa-miR-125a-5p) GO:0001936, Regulation of endothelial cell proliferation 0.115146
GO:0002221, Pattern recognition receptor signaling pathway 0.380928
GO:0002224, Toll-like receptor signaling pathway 0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway 0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway 0.380928
GO:0006916, Anti-apoptosis 0.0593
GO:0006917, Induction of apoptosis 0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.776269
GO:0008284, Positive regulation of cell proliferation 0.882324
GO:0008285, Negative regulation of cell proliferation 0.883393
GO:0008629, Induction of apoptosis by intracellular signals 0.073118
GO:0010942, Positive regulation of cell death 0.972892
GO:0012502, Induction of programmed cell death 0.618064
GO:0015629, Actin cytoskeleton 0.596528
GO:0015630, Microtubule cytoskeleton 0.028245*
GO:0030834, Regulation of actin filament depolymerization 0.654383
GO:0030835, Negative regulation of actin filament depolymerization 0.654383
GO:0030837, Negative regulation of actin filament polymerization 0.654383
GO:0034130, Toll-like receptor 1 signaling pathway 0.380928
GO:0034134, Toll-like receptor 2 signaling pathway 0.380928
GO:0034138, Toll-like receptor 3 signaling pathway 0.380928
GO:0034142, Toll-like receptor 4 signaling pathway 0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway 0.380928
GO:0043067, Regulation of programmed cell death 0.643418
GO:0043068, Positive regulation of programmed cell death 0.972892
GO:0043069, Negative regulation of programmed cell death 0.492576
GO:0048011, Nerve growth factor receptor signaling pathway 0.171634
GO:0050678, Regulation of epithelial cell proliferation 0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation 0.205483
GO:0050851, Antigen receptor-mediated signaling pathway 0.103325
GO:0051693, Actin filament capping 0.654383
GO:0060548, Negative regulation of cell death 0.413665

MIMAT0002820(hsa-miR-497-5p) GO:0001959, Regulation of cytokine-mediated signaling pathway 0.06699
GO:0006917, Induction of apoptosis 0.142401
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.837635
GO:0007173, Epidermal growth factor receptor signaling pathway 0.387447
GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway 0.240167
GO:0007179, Transforming growth factor beta receptor signaling pathway 0.017876*
GO:0008284, Positive regulation of cell proliferation 0.430255
GO:0008285, Negative regulation of cell proliferation 0.149994
GO:0008543, Fibroblast growth factor receptor signaling pathway 0.521978
GO:0010942, Positive regulation of cell death 0.237692
GO:0012502, Induction of programmed cell death 0.142401
GO:0015629, Actin cytoskeleton 0.228804
GO:0015630, Microtubule cytoskeleton 0.18331
GO:0017015, Regulation of transforming growth factor beta receptor signaling pathway 0.128773
GO:0030509, BMP signaling pathway 0.39837
GO:0030521, Androgen receptor signaling pathway 0.383811
GO:0032956, Regulation of actin cytoskeleton organization 0.91762
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway 0.934045
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway 0.789492
GO:0043067, Regulation of programmed cell death 0.111544
GO:0043068, Positive regulation of programmed cell death 0.237692
GO:0043069, Negative regulation of programmed cell death 0.856892
GO:0048011, Nerve growth factor receptor signaling pathway 0.471986
GO:0060548, Negative regulation of cell death 0.667437
GO:0070302, Regulation of stress-activated protein kinase signaling cascade 0.561032
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 0.182314

MIMAT0002856(hsa-miR-520d-3p) GO:0006917, Induction of apoptosis 0.489781
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.171689
GO:0007173, Epidermal growth factor receptor signaling pathway 0.449696
GO:0008284, Positive regulation of cell proliferation 0.05916
GO:0008286, Insulin receptor signaling pathway 0.237933
GO:0008543, Fibroblast growth factor receptor signaling pathway 0.159318
GO:0008629, Induction of apoptosis by intracellular signals 0.502822
GO:0010942, Positive regulation of cell death 0.076906
GO:0012502, Induction of programmed cell death 0.489781
GO:0015630, Microtubule cytoskeleton 0.000292***
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway 0.762633
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway 0.826854
GO:0043067, Regulation of programmed cell death 0.740092
GO:0043068, Positive regulation of programmed cell death 0.076906
GO:0043069, Negative regulation of programmed cell death 0.067499
GO:0048011, Nerve growth factor receptor signaling pathway 0.014926*
GO:0051988, Regulation of attachment of spindle microtubules to kinetochore 7.48 × 106***
GO:0060548, Negative regulation of cell death 0.213035
*

p < 0.05,

**

p < 0.01;

***

p < 0.001.

Table S8.

Pathophysiogical characteristics of miRNA array data used in ROC curve analysis.

Sample Name ER PR HER TNM Stage Grade
S621T1 0 0 0 pT1N0M0 I 2
S434T1 1 0 1 T2N1M0 IIB 3
S403T1 0 1 0 T2N1M0 IIB 2
S459T1 1 0 0 T4N0M1 IV 1
S455N1 1 0 0 T3N3M1 IV 3
S545T1 0 0 0 pT2N0M0 IIA 3
S173N1 0 0 1 T2N3M0 IIIC 3
S363T1 1 1 0 T2N1M0 IIB 1
S909T1 1 1 1 pT3N3aM0 IIIC (Unknown)
S645T1 0 1 1 pT1bN0M0 I 3
S898T1 1 0 0 pT2N0(i-)M0 IIA (Unknown)
S201T1 1 0 0 T1N1M0 IIA 2
S631T1 1 1 1 T2N3aM1 IV 2
S303T1 0 0 1 T2N0M0 IIA 2
S502T1 0 0 0 pT3N0M0 IIB 3
S498N1 1 1 1 pT1cN1aM0 IIA 2
S536T1 1 0 1 T1cN1miM0 IIA 2
S660T1 0 0 1 T1N0M0 I 3
S358N1 0 0 1 T2pN2M0 IIIA 2
S665T1 0 0 0 T2N3M0 IIIC 3
S475T1 0 0 1 pT1cN0M0 I 3
S423T1 1 1 0 T2N3M0 IIIC 1
S507T1 0 0 0 pT1cN1aM0 IIA 3
S891T1 1 0 0 pT2NxM0 IIA 2
S422T1 1 1 0 T2N0M0 IIA 1
S961T1 0 0 1 T2N2aM0 IIIA 2
S622T1 0 0 0 pT2N0M0 IIA 2
S454T1 0 0 0 T1cN0M0 I 2
S433T1 1 0 0 T2N0M0 IIA 1
S673T1 0 0 0 pT2N0M0 IIA 2
S450T1 0 0 1 T2N1M0 IIB 3
S430T1 1 1 0 T2N3M0 IIIC 2
S437T1 1 0 0 T3N1M0 IIIA 3
S574T1 0 0 0 T2N0M0 IIA 2
S401T1 1 1 0 T1N0M0 I 1
S427N1 0 0 0 T3N1M0 IIIA 2
S894T1 0 0 0 pT1cN0M0 I 2
S929T1 1 0 0 pT2N0M0 IIA 2
S173T1 0 0 1 T2N3M0 IIIC 3
S622N1 0 0 0 pT2N0M0 IIA 2
S562T1 1 1 1 pT1aN0M0 I 3
S602T1 0 0 0 pT1cN0M0 I 3
S490T1 1 1 1 pT2N0M0 IIA 2
S677T1 0 0 0 pT2N1M0 IIB 3
S881T1 0 0 0 pT2N1aM0 IIB 3
S619T1 0 0 0 pT2N0M0 IIA 3
S446T1 0 0 0 T2N2M0 IIIA 3
S446T2 0 0 0 T2N2M0 IIIA 3
S453T1 1 1 1 T2N1M0 IIB 2
S562N1 1 1 1 pT1aN0M0 I 3
S557T1 0 0 0 pT2N0M0 IIA 3
S594T1 1 1 1 pT2N3aM0 IIIC 3
S582T1 0 0 0 pT2N0M0 IIA 3
S358T1 0 0 1 T2pN2M0 IIIA 2
S368T1 0 0 1 T3N3M0 IIIC 2
S175T1 1 1 0 T3N3Mx IIIC 3
S357T1 0 0 0 T2pN1M0 IIIC 3
S653T1 0 0 0 pT3N3aM0 IIIC 2
S722T1 1 1 1 pT1N0M0 I 3
S593T1 0 0 0 pT1N0M0 I 3
S543T1 1 1 1 pT2N1M0 IIB 2
S498T1 1 1 1 pT1cN1aM0 IIA 2
S389T1 1 0 0 T2N1M0 IIB 3
S614T1 0 1 1 TxN0M1 IIIA (Unknown)
S536N1 1 0 1 T1cN1miM0 IIA 2
S462T1 1 1 0 T3N3M0 IIIC 2
S477T1 0 0 0 pT1cN0M0 I 3
S917T1 0 0 0 T2N0M0 IIA (Unknown)
S213T1 0 0 1 T1cN1M0 IIA 3
S628T1 0 0 0 T1N0M0 I 2
S291T1 0 0 0 T3pN2M0 IIIA 3
S593N1 0 0 0 pT1N0M0 I 3
S418T1 1 1 0 T2N3M0 IIIC 2
S420T1 1 1 0 T1N0M0 I 2
S363N1 1 1 0 T2N1M0 IIB 1
S629T1 1 0 1 pT1N0M0 I 2
S586T1 1 1 1 pT2N3aM0 IIIC 2
S415T1 0 1 0 T2N2M0 IIIA 2
S439T1 1 1 0 T2N2M0 IIIA 2
S941N1 0 0 0 pT1cN1aM0 IIA 3
S893T1 0 0 0 pT2N1micM0 IIB 3
S380T1 1 0 0 T2N1M0 IIB 2
S906N1 1 1 1 pT3N3aM0 IIIC 2
S918T1 0 0 0 pT2N0M0 IIA 3
S400T1 0 0 1 T2N0M0 IIA 2
S328T1 0 0 0 T1cpN0M0 I 3
S367T1 1 0 0 T1N1M0 IIIA 1
S420N1 1 1 0 T1N0M0 I 2
S922T1 0 0 0 pT1cN0(i-)M0 I 3
S896T1 0 0 1 pT2N1aM0 IIB 2
S410T1 0 1 1 T1N0M0 I 3
S572T1 0 0 1 T4N1aM0 IIIC 3
S448T1 0 0 0 T1N0M0 I 3
S207T1 0 0 1 T2N0M0 IIA 3
S604T1 0 0 1 pT2N1M0 IIB 3
S379T1 0 0 1 T2N2M0 IIIA 2
S906T1 1 1 1 pT3N3aM0 IIIC 2
S941T2 0 0 0 pT1cN1aM0 IIA 3
S941T1 0 0 0 pT1cN1aM0 IIA 3
S375T1 1 1 0 T1N0M0 I 2
S427T1 0 0 0 T3N1M0 IIIA 2
S417T1 0 0 0 pT2N0M0 IIA 3
S180T1 1 0 0 T4NxM0 IIIC
S455T1 1 0 0 T3N3M1 IV
S887T1 0 0 0 pT2N0M0 IIA
S445T1 1 0 1 T2N1M0 IIB
S469T1 1 1 0 T2N0M0 IIA
S469T2 1 1 0 T2N0M0 IIA
S483T1 1 1 0 T2N3M0 IIIC
S444T1 1 1 0 T1NxM0 (Unknown)
S909N1 1 1 1 pT3N3aM0 IIIC
S464T1 1 1 0 T1N0M0 I
S894N1 0 0 0 pT1cN0M0 I
S698T1 1 1 1 pT1cN0M0 I
S452T1 0 0 0 T1N0M0 I
S474T1 (Unknown) (Unknown) (Unknown) (Unknown) (Unknown)

Supplementary Information

ijms-14-11560-s001.pdf (1.2MB, pdf)

Acknowledgments

This work was supported by the National Science Council of Taiwan (NSC 97-2311-B-002-010-MY3 and NSC 99-2621-B-002-005-MY3), National Taiwan University Cutting-Edge Steering Research Project (NTU-CESRP-102R7602C3) and the National Health Research Institutes, Taiwan (NHRI-EX98-9819PI).

Conflict of Interest

The authors declare no conflict of interest.

References

  • 1.Jemal A., Center M.M., DeSantis C., Ward E.M. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol. Biomark. Prev. 2010;19:1893–1907. doi: 10.1158/1055-9965.EPI-10-0437. [DOI] [PubMed] [Google Scholar]
  • 2.Veronesi U., Boyle P., Goldhirsch A., Orecchia R., Viale G. Breast cancer. Lancet. 2005;365:1727–1741. doi: 10.1016/S0140-6736(05)66546-4. [DOI] [PubMed] [Google Scholar]
  • 3.Cho E., Spiegelman D., Hunter D.J., Chen W.Y., Stampfer M.J., Colditz G.A., Willett W.C. Premenopausal fat intake and risk of breast cancer. J. Natl. Cancer Inst. 2003;95:1079–1085. doi: 10.1093/jnci/95.14.1079. [DOI] [PubMed] [Google Scholar]
  • 4.Huttenhofer A., Schattner P., Polacek N. Non-coding RNAs: Hope or hype? Trends Genet. 2005;21:289–297. doi: 10.1016/j.tig.2005.03.007. [DOI] [PubMed] [Google Scholar]
  • 5.Kim V.N., Han J., Siomi M.C. Biogenesis of small RNAs in animals. Nat. Rev. Mol. Cell Biol. 2009;10:126–139. doi: 10.1038/nrm2632. [DOI] [PubMed] [Google Scholar]
  • 6.Kwak P.B., Iwasaki S., Tomari Y. The microRNA pathway and cancer. Cancer Sci. 2010;101:2309–2315. doi: 10.1111/j.1349-7006.2010.01683.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hutvagner G., Zamore P.D. A microRNA in a multiple-turnover RNAi enzyme complex. Science. 2002;297:2056–2060. doi: 10.1126/science.1073827. [DOI] [PubMed] [Google Scholar]
  • 8.Mourelatos Z., Dostie J., Paushkin S., Sharma A., Charroux B., Abel L., Rappsilber J., Mann M., Dreyfuss G. miRNPs: A novel class of ribonucleoproteins containing numerous microRNAs. Genes Dev. 2002;16:720–728. doi: 10.1101/gad.974702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nelson P.T., Hatzigeorgiou A.G., Mourelatos Z. miRNP:mRNA association in polyribosomes in a human neuronal cell line. RNA. 2004;10:387–394. doi: 10.1261/rna.5181104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Meister G., Landthaler M., Patkaniowska A., Dorsett Y., Teng G., Tuschl T. Human Argonaute2 mediates RNA cleavage targeted by miRNAs and siRNAs. Mol. Cell. 2004;15:185–197. doi: 10.1016/j.molcel.2004.07.007. [DOI] [PubMed] [Google Scholar]
  • 11.Calin G.A., Dumitru C.D., Shimizu M., Bichi R., Zupo S., Noch E., Aldler H., Rattan S., Keating M., Rai K., et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc. Natl. Acad. Sci. USA. 2002;99:15524–15529. doi: 10.1073/pnas.242606799. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zhang B., Pan X., Cobb G.P., Anderson T.A. microRNAs as oncogenes and tumor suppressors. Dev. Biol. 2007;302:1–12. doi: 10.1016/j.ydbio.2006.08.028. [DOI] [PubMed] [Google Scholar]
  • 13.Meng F., Henson R., Wehbe-Janek H., Ghoshal K., Jacob S.T., Patel T. MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer. Gastroenterology. 2007;133:647–658. doi: 10.1053/j.gastro.2007.05.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Rask L., Balslev E., Jorgensen S., Eriksen J., Flyger H., Moller S., Hogdall E., Litman T., Nielsen B.S. High expression of miR-21 in tumor stroma correlates with increased cancer cell proliferation in human breast cancer. APMIS. 2011;119:663–673. doi: 10.1111/j.1600-0463.2011.02782.x. [DOI] [PubMed] [Google Scholar]
  • 15.Zaman M.S., Shahryari V., Deng G., Thamminana S., Saini S., Majid S., Chang I., Hirata H., Ueno K., Yamamura S., et al. Up-regulation of microRNA-21 correlates with lower kidney cancer survival. PLoS One. 2012;7:e31060. doi: 10.1371/journal.pone.0031060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Nadiminty N., Tummala R., Lou W., Zhu Y., Shi X.B., Zou J.X., Chen H., Zhang J., Chen X., Luo J., et al. MicroRNA let-7c is downregulated in prostate cancer and suppresses prostate cancer growth. PLoS One. 2012;7:e32832. doi: 10.1371/journal.pone.0032832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wang X.F., Shi Z.M., Wang X.R., Cao L., Wang Y.Y., Zhang J.X., Yin Y., Luo H., Kang C.S., Liu N., et al. MiR-181d acts as a tumor suppressor in glioma by targeting K-ras and Bcl-2. J. Cancer Res. Clin. Oncol. 2012;138:573–584. doi: 10.1007/s00432-011-1114-x. [DOI] [PubMed] [Google Scholar]
  • 18.Zhang Y., Yan L.X., Wu Q.N., Du Z.M., Chen J., Liao D.Z., Huang M.Y., Hou J.H., Wu Q.L., Zeng M.S., et al. miR-125b is methylated and functions as a tumor suppressor by regulating the ETS1 proto-oncogene in human invasive breast cancer. Cancer Res. 2011;71:3552–3562. doi: 10.1158/0008-5472.CAN-10-2435. [DOI] [PubMed] [Google Scholar]
  • 19.Sachdeva M., Mo Y.Y. MicroRNA-145 suppresses cell invasion and metastasis by directly targeting mucin 1. Cancer Res. 2010;70:378–387. doi: 10.1158/0008-5472.CAN-09-2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Guo X., Wu Y., Hartley R.S. MicroRNA-125a represses cell growth by targeting HuR in breast cancer. RNA Biol. 2009;6:575–583. doi: 10.4161/rna.6.5.10079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Jin H., Tuo W., Lian H., Liu Q., Zhu X.Q., Gao H. Strategies to identify microRNA targets: New advances. N. Biotechnol. 2010;27:734–738. doi: 10.1016/j.nbt.2010.09.006. [DOI] [PubMed] [Google Scholar]
  • 22.Friedman R.C., Farh K.K., Burge C.B., Bartel D.P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19:92–105. doi: 10.1101/gr.082701.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Huang J.C., Babak T., Corson T.W., Chua G., Khan S., Gallie B.L., Hughes T.R., Blencowe B.J., Frey B.J., Morris Q.D. Using expression profiling data to identify human microRNA targets. Nat. Methods. 2007;4:1045–1049. doi: 10.1038/nmeth1130. [DOI] [PubMed] [Google Scholar]
  • 24.Liu B., Li J., Tsykin A., Liu L., Gaur A.B., Goodall G.J. Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy. BMC Bioinforma. 2009;10:408. doi: 10.1186/1471-2105-10-408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Tseng C.W., Lin C.C., Chen C.N., Huang H.C., Juan H.F. Integrative network analysis reveals active microRNAs and their functions in gastric cancer. BMC Syst. Biol. 2011;5:99. doi: 10.1186/1752-0509-5-99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Farazi T.A., Horlings H.M., Ten Hoeve J.J., Mihailovic A., Halfwerk H., Morozov P., Brown M., Hafner M., Reyal F., van Kouwenhove M., et al. MicroRNA sequence and expression analysis in breast tumors by deep sequencing. Cancer Res. 2011;71:4443–4453. doi: 10.1158/0008-5472.CAN-11-0608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Derfoul A., Juan A.H., Difilippantonio M.J., Palanisamy N., Ried T., Sartorelli V. Decreased microRNA-214 levels in breast cancer cells coincides with increased cell proliferation, invasion and accumulation of the Polycomb Ezh2 methyltransferase. Carcinogenesis. 2011;32:1607–1614. doi: 10.1093/carcin/bgr184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Heyn H., Engelmann M., Schreek S., Ahrens P., Lehmann U., Kreipe H., Schlegelberger B., Beger C. MicroRNA miR-335 is crucial for the BRCA1 regulatory cascade in breast cancer development. Int. J. Cancer. 2011;129:2797–2806. doi: 10.1002/ijc.25962. [DOI] [PubMed] [Google Scholar]
  • 29.Sempere L.F., Christensen M., Silahtaroglu A., Bak M., Heath C.V., Schwartz G., Wells W., Kauppinen S., Cole C.N. Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res. 2007;67:11612–11620. doi: 10.1158/0008-5472.CAN-07-5019. [DOI] [PubMed] [Google Scholar]
  • 30.Zhu X.M., Wu L.J., Xu J., Yang R., Wu F.S. Let-7c microRNA expression and clinical significance in hepatocellular carcinoma. J. Int. Med. Res. 2011;39:2323–2329. doi: 10.1177/147323001103900631. [DOI] [PubMed] [Google Scholar]
  • 31.Iyevleva A.G., Kuligina E., Mitiushkina N.V., Togo A.V., Miki Y., Imyanitov E.N. High level of miR-21, miR-10b, and miR-31 expression in bilateral vs. unilateral breast carcinomas. Breast Cancer Res. Treat. 2012;131:1049–1059. doi: 10.1007/s10549-011-1845-z. [DOI] [PubMed] [Google Scholar]
  • 32.Rutnam Z.J., Yang B.B. The involvement of microRNAs in malignant transformation. Histol. Histopathol. 2012;27:1263–1270. doi: 10.14670/HH-27.1263. [DOI] [PubMed] [Google Scholar]
  • 33.Liu H. MicroRNAs in breast cancer initiation and progression. Cell Mol. Life Sci. 2012;69:3587–3599. doi: 10.1007/s00018-012-1128-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zhang Z.J., Ma S.L. miRNAs in breast cancer tumorigenesis (Review) Oncol. Rep. 2012;27:903–910. doi: 10.3892/or.2011.1611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Mathivanan S., Periaswamy B., Gandhi T.K., Kandasamy K., Suresh S., Mohmood R., Ramachandra Y.L., Pandey A. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinforma. 2006;7:S19. doi: 10.1186/1471-2105-7-S5-S19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Halbert C.H., Kumanyika S., Bowman M., Bellamy S.L., Briggs V., Brown S., Bryant B., Delmoor E., Johnson J.C., Purnell J., et al. Participation rates and representativeness of African Americans recruited to a health promotion program. Health Educ. Res. 2010;25:6–13. doi: 10.1093/her/cyp057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Nicolas Diaz-Chico B., German Rodriguez F., Gonzalez A., Ramirez R., Bilbao C., Cabrera de Leon A., Aguirre Jaime A., Chirino R., Navarro D., Diaz-Chico J.C. Androgens and androgen receptors in breast cancer. J. Steroid Biochem. Mol. Biol. 2007;105:1–15. doi: 10.1016/j.jsbmb.2006.11.019. [DOI] [PubMed] [Google Scholar]
  • 38.Sethi N., Kang Y. Dysregulation of developmental pathways in bone metastasis. Bone. 2011;48:16–22. doi: 10.1016/j.bone.2010.07.005. [DOI] [PubMed] [Google Scholar]
  • 39.Liao S.J., Zhou Y.H., Yuan Y., Li D., Wu F.H., Wang Q., Zhu J.H., Yan B., Wei J.J., Zhang G.M., et al. Triggering of Toll-like receptor 4 on metastatic breast cancer cells promotes alphavbeta3-mediated adhesion and invasive migration. Breast Cancer Res. Treat. 2011;133:853–863. doi: 10.1007/s10549-011-1844-0. [DOI] [PubMed] [Google Scholar]
  • 40.Jiang P., Enomoto A., Takahashi M. Cell biology of the movement of breast cancer cells: Intracellular signalling and the actin cytoskeleton. Cancer Lett. 2009;284:122–130. doi: 10.1016/j.canlet.2009.02.034. [DOI] [PubMed] [Google Scholar]
  • 41.Garcia D.M., Baek D., Shin C., Bell G.W., Grimson A., Bartel D.P. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat. Struct. Mol. Biol. 2011;18:1139–1146. doi: 10.1038/nsmb.2115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lewis B.P., Burge C.B., Bartel D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. doi: 10.1016/j.cell.2004.12.035. [DOI] [PubMed] [Google Scholar]
  • 43.Lall S., Grun D., Krek A., Chen K., Wang Y.L., Dewey C.N., Sood P., Colombo T., Bray N., Macmenamin P., et al. A genome-wide map of conserved microRNA targets in C. elegans. Curr. Biol. 2006;16:460–471. doi: 10.1016/j.cub.2006.01.050. [DOI] [PubMed] [Google Scholar]
  • 44.Krek A., Grun D., Poy M.N., Wolf R., Rosenberg L., Epstein E.J., MacMenamin P., da Piedade I., Gunsalus K.C., Stoffel M., et al. Combinatorial microRNA target predictions. Nat. Genet. 2005;37:495–500. doi: 10.1038/ng1536. [DOI] [PubMed] [Google Scholar]
  • 45.Betel D., Wilson M., Gabow A., Marks D.S., Sander C. The microRNA.org resource: Targets and expression. Nucleic Acids Res. 2008;36:D149–D153. doi: 10.1093/nar/gkm995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Tusher V.G., Tibshirani R., Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA. 2001;98:5116–5121. doi: 10.1073/pnas.091062498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Keshava Prasad T.S., Goel R., Kandasamy K., Keerthikumar S., Kumar S., Mathivanan S., Telikicherla D., Raju R., Shafreen B., Venugopal A., et al. Human Protein Reference Database—2009 update. Nucleic Acids Res. 2009;37:D767–D772. doi: 10.1093/nar/gkn892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Benjamini Y., Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 2001;29:1165–1188. [Google Scholar]
  • 49.Ringner M., Fredlund E., Hakkinen J., Borg A., Staaf J. GOBO: Gene expression-based outcome for breast cancer online. PLoS One. 2011;6:e17911. doi: 10.1371/journal.pone.0017911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Sing T., Sander O., Beerenwinkel N., Lengauer T. ROCR: Visualizing classifier performance in R. Bioinformatics. 2005;21:3940–3941. doi: 10.1093/bioinformatics/bti623. [DOI] [PubMed] [Google Scholar]
  • 51.Hanley J.A., McNeil B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36. doi: 10.1148/radiology.143.1.7063747. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Significantly differentially expressed miRNAs found in miRNA dataset in Farazi et al. [26]. There are 89 down-regulated miRNAs and 1 up-regulated miRNA in this list. Q-values reported by SAM were 0 for all miRNAs in this list.

miRBase Accession miRNA Name Fold Change
MIMAT0004761 hsa-miR-483-5p 0.01
MIMAT0004552 hsa-miR-139-3p 0.01
MIMAT0000738 hsa-miR-383 0.02
MIMAT0002856 hsa-miR-520d-3p 0.02
MIMAT0002811 hsa-miR-202-3p 0.03
MIMAT0002177 hsa-miR-486-5p 0.04
MIMAT0022721 hsa-miR-1247-3p 0.05
MIMAT0002175 hsa-miR-485-5p 0.06
MIMAT0000265 hsa-miR-204-5p 0.07
MIMAT0000752 hsa-miR-328 0.07
MIMAT0000421 hsa-miR-122-5p 0.07
MIMAT0000447 hsa-miR-134 0.08
MIMAT0000722 hsa-miR-370 0.09
MIMAT0004513 hsa-miR-101-5p 0.09
MIMAT0000446 hsa-miR-127-3p 0.10
MIMAT0000097 hsa-miR-99a-5p 0.10
MIMAT0004566 hsa-miR-218-2-3p 0.10
MIMAT0000729 hsa-miR-376a-3p 0.11
MIMAT0009197 hsa-miR-205-3p 0.11
MIMAT0004615 hsa-miR-195-3p 0.11
MIMAT0005899 hsa-miR-1247-5p 0.11
MIMAT0000720 hsa-miR-376c 0.12
MIMAT0000762 hsa-miR-324-3p 0.12
MIMAT0004679 hsa-miR-296-3p 0.12
MIMAT0004614 hsa-miR-193a-5p 0.12
MIMAT0003880 hsa-miR-671-5p 0.12
MIMAT0004795 hsa-miR-574-5p 0.12
MIMAT0004599 hsa-miR-143-5p 0.13
MIMAT0000423 hsa-miR-125b-5p 0.13
MIMAT0004957 hsa-miR-760 0.13
MIMAT0004911 hsa-miR-874 0.14
MIMAT0004603 hsa-miR-125b-2-3p 0.15
MIMAT0004952 hsa-miR-665 0.15
MIMAT0018205 hsa-miR-3928 0.15
MIMAT0004767 hsa-miR-193b-5p 0.15
MIMAT0002861 hsa-miR-518e-3p 0.15
MIMAT0004604 hsa-miR-127-5p 0.16
MIMAT0002807 hsa-miR-491-5p 0.16
MIMAT0004689 hsa-miR-377-5p 0.16
MIMAT0004762 hsa-miR-486-3p 0.16
MIMAT0000732 hsa-miR-378a-3p 0.17
MIMAT0017981 hsa-miR-3605-5p 0.18
MIMAT0004605 hsa-miR-129-2-3p 0.19
MIMAT0006789 hsa-miR-1468 0.20
MIMAT0000737 hsa-miR-382-5p 0.21
MIMAT0000077 hsa-miR-22-3p 0.21
MIMAT0000089 hsa-miR-31-5p 0.21
MIMAT0004612 hsa-miR-186-3p 0.21
MIMAT0004592 hsa-miR-125b-1-3p 0.22
MIMAT0001639 hsa-miR-409-3p 0.22
MIMAT0015032 hsa-miR-3158-3p 0.22
MIMAT0004496 hsa-miR-23a-5p 0.22
MIMAT0000690 hsa-miR-296-5p 0.22
MIMAT0000731 hsa-miR-378a-5p 0.23
MIMAT0000448 hsa-miR-136-5p 0.23
MIMAT0004796 hsa-miR-576-3p 0.23
MIMAT0010133 hsa-miR-2110 0.23
MIMAT0004951 hsa-miR-887 0.23
MIMAT0003239 hsa-miR-574-3p 0.25
MIMAT0005901 hsa-miR-1249 0.25
MIMAT0000510 hsa-miR-320a 0.26
MIMAT0002172 hsa-miR-376b 0.26
MIMAT0000250 hsa-miR-139-5p 0.27
MIMAT0005825 hsa-miR-1180 0.27
MIMAT0000437 hsa-miR-145-5p 0.28
MIMAT0004601 hsa-miR-145-3p 0.28
MIMAT0003322 hsa-miR-652-3p 0.28
MIMAT0000756 hsa-miR-326 0.28
MIMAT0000098 hsa-miR-100-5p 0.29
MIMAT0003296 hsa-miR-627 0.29
MIMAT0002820 hsa-miR-497-5p 0.31
MIMAT0004507 hsa-miR-92a-1-5p 0.31
MIMAT0000271 hsa-miR-214-3p 0.32
MIMAT0004702 hsa-miR-339-3p 0.33
MIMAT0004611 hsa-miR-185-3p 0.33
MIMAT0000064 hsa-let-7c 0.34
MIMAT0004673 hsa-miR-29c-5p 0.35
MIMAT0000733 hsa-miR-379-5p 0.35
MIMAT0004594 hsa-miR-132-5p 0.35
MIMAT0000765 hsa-miR-335-5p 0.35
MIMAT0002819 hsa-miR-193b-3p 0.36
MIMAT0000088 hsa-miR-30a-3p 0.36
MIMAT0005951 hsa-miR-1307-3p 0.36
MIMAT0004597 hsa-miR-140-3p 0.37
MIMAT0004556 hsa-miR-10b-3p 0.37
MIMAT0000272 hsa-miR-215 0.37
MIMAT0004511 hsa-miR-99a-3p 0.37
MIMAT0000443 hsa-miR-125a-5p 0.38
MIMAT0004482 hsa-let-7b-3p 0.38
MIMAT0000076 hsa-miR-21-5p 6.58

Table S2.

Down-regulated genes found in dataset GSE29174. There are 726 down-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene ID Gene Symbol Fold Change
2949 GSTM5 0.06
10894 LYVE1 0.06
5950 RBP4 0.07
762 CA4 0.09
54997 TESC 0.09
3489 IGFBP6 0.09
3952 LEP 0.09
213 ALB 0.09
3131 HLF 0.10
4023 LPL 0.10
10633 RASL10A 0.11
364 AQP7 0.11
1908 EDN3 0.11
1811 SLC26A3 0.11
91851 CHRDL1 0.11
729359 PLIN4 0.13
1149 CIDEA 0.13
5959 RDH5 0.13
5348 FXYD1 0.14
5346 PLIN1 0.14
10249 GLYAT 0.14
158800 RHOXF1 0.14
221476 PI16 0.14
3040 HBA2 0.14
6939 TCF15 0.14
79645 EFCAB1 0.14
80343 SEL1L2 0.14
9413 FAM189A2 0.15
26289 AK5 0.15
25891 PAMR1 0.15
3679 ITGA7 0.15
1264 CNN1 0.15
92304 SCGB3A1 0.15
2167 FABP4 0.15
23285 KIAA1107 0.15
7145 TNS1 0.16
4881 NPR1 0.16
1028 CDKN1C 0.16
1036 CDO1 0.16
130271 PLEKHH2 0.16
8736 MYOM1 0.16
8908 GYG2 0.16
619373 MBOAT4 0.17
130399 ACVR1C 0.17
1646 AKR1C2 0.17
80763 C12orf39 0.17
2159 F10 0.18
84889 SLC7A3 0.18
1308 COL17A1 0.18
83699 SH3BGRL2 0.18
84417 C2orf40 0.18
4081 MAB21L1 0.18
3484 IGFBP1 0.18
5239 PGM5 0.19
4969 OGN 0.19
2719 GPC3 0.19
116362 RBP7 0.19
948 CD36 0.19
5764 PTN 0.19
3043 HBB 0.19
56920 SEMA3G 0.20
94274 PPP1R14A 0.20
57447 NDRG2 0.20
84795 PYROXD2 0.20
84649 DGAT2 0.20
2690 GHR 0.20
22802 CLCA4 0.20
5179 PENK 0.20
6663 SOX10 0.20
6649 SOD3 0.21
54922 RASIP1 0.21
8406 SRPX 0.21
1446 CSN1S1 0.21
7123 CLEC3B 0.22
9647 PPM1F 0.22
1842 ECM2 0.22
3909 LAMA3 0.22
8639 AOC3 0.23
2934 GSN 0.23
9370 ADIPOQ 0.23
3202 HOXA5 0.23
9452 ITM2A 0.23
6290 SAA3P 0.23
4604 MYBPC1 0.23
79785 RERGL 0.16
221091 LRRN4CL 0.17
3991 LIPE 0.17
27175 TUBG2 0.24
1346 COX7A1 0.24
6376 CX3CL1 0.24
50486 G0S2 0.24
6285 S100B 0.24
443 ASPA 0.24
947 CD34 0.25
84632 AFAP1L2 0.25
3866 KRT15 0.25
147463 ANKRD29 0.25
2878 GPX3 0.25
7079 TIMP4 0.25
54345 SOX18 0.25
51277 DNAJC27 0.25
84870 RSPO3 0.25
55323 LARP6 0.25
6387 CXCL12 0.25
137835 TMEM71 0.25
5212 VIT 0.25
26577 PCOLCE2 0.25
845 CASQ2 0.25
6422 SFRP1 0.25
10351 ABCA8 0.26
10840 ALDH1L1 0.26
65983 GRAMD3 0.26
84327 ZBED3 0.26
57124 CD248 0.26
3235 HOXD9 0.26
2192 FBLN1 0.26
91653 BOC 0.26
4147 MATN2 0.26
126669 SHE 0.27
2788 GNG7 0.27
129804 FBLN7 0.27
270 AMPD1 0.27
79656 BEND5 0.27
58503 PROL1 0.27
3316 HSPB2 0.27
729440 CCDC61 0.27
54438 GFOD1 0.27
5243 ABCB1 0.27
1128 CHRM1 0.23
83878 USHBP1 0.24
63970 TP53AIP1 0.24
79192 IRX1 0.28
3400 ID4 0.28
57519 STARD9 0.29
57666 FBRSL1 0.29
3590 IL11RA 0.29
57664 PLEKHA4 0.29
197257 LDHD 0.29
66036 MTMR9 0.29
2321 FLT1 0.29
126 ADH1C 0.29
1363 CPE 0.29
56131 PCDHB4 0.29
22915 MMRN1 0.29
7069 THRSP 0.29
57161 PELI2 0.30
770 CA11 0.30
53342 IL17D 0.30
79987 SVEP1 0.30
857 CAV1 0.30
222166 C7orf41 0.30
27190 IL17B 0.30
116159 CYYR1 0.30
4487 MSX1 0.30
9068 ANGPTL1 0.30
10411 RAPGEF3 0.30
3199 HOXA2 0.30
2944 GSTM1 0.30
2920 CXCL2 0.30
201134 CEP112 0.31
220001 VWCE 0.31
83888 FGFBP2 0.31
6366 CCL21 0.31
6711 SPTBN1 0.31
85378 TUBGCP6 0.31
26040 SETBP1 0.31
4692 NDN 0.31
25890 ABI3BP 0.31
23531 MMD 0.31
30846 EHD2 0.31
6196 RPS6KA2 0.31
2009 EML1 0.31
810 CALML3 0.27
6898 TAT 0.27
5648 MASP1 0.28
25999 CLIP3 0.28
125875 CLDND2 0.28
7102 TSPAN7 0.28
1879 EBF1 0.28
23252 OTUD3 0.28
5493 PPL 0.28
83987 CCDC8 0.28
9073 CLDN8 0.28
221981 THSD7A 0.28
64102 TNMD 0.28
137872 ADHFE1 0.33
27151 CPAMD8 0.33
387923 SERP2 0.33
145581 LRFN5 0.33
6263 RYR3 0.33
2354 FOSB 0.33
51302 CYP39A1 0.33
4128 MAOA 0.34
117248 GALNTL2 0.34
10268 RAMP3 0.34
7730 ZNF177 0.34
10873 ME3 0.34
7461 CLIP2 0.34
7049 TGFBR3 0.34
79901 CYBRD1 0.34
5152 PDE9A 0.34
50805 IRX4 0.34
8644 AKR1C3 0.34
5915 RARB 0.34
2770 GNAI1 0.34
54996 2-Mar 0.35
79791 FBXO31 0.35
54776 PPP1R12C 0.35
9079 LDB2 0.35
57104 PNPLA2 0.35
30008 EFEMP2 0.35
91461 PKDCC 0.35
23368 PPP1R13B 0.35
23461 ABCA5 0.35
9572 NR1D1 0.35
23338 PHF15 0.35
6289 SAA2 0.31
345275 HSD17B13 0.31
2701 GJA4 0.32
112609 MRAP2 0.32
727 C5 0.32
477 ATP1A2 0.32
9627 SNCAIP 0.32
4435 CITED1 0.32
10974 C10orf116 0.32
11005 SPINK5 0.32
80325 ABTB1 0.33
221395 GPR116 0.33
10014 HDAC5 0.33
1489 CTF1 0.37
35 ACADS 0.37
3749 KCNC4 0.37
140738 TMEM37 0.37
2791 GNG11 0.37
23604 DAPK2 0.37
10217 CTDSPL 0.37
23550 PSD4 0.37
4306 NR3C2 0.37
119587 CPXM2 0.37
7942 TFEB 0.37
3815 KIT 0.37
1805 DPT 0.37
23242 COBL 0.37
4313 MMP2 0.37
4139 MARK1 0.37
9104 RGN 0.37
2329 FMO4 0.37
25802 LMOD1 0.38
4239 MFAP4 0.38
10392 NOD1 0.38
6794 STK11 0.38
85458 DIXDC1 0.38
4123 MAN2C1 0.38
54476 RNF216 0.38
9920 KBTBD11 0.38
6329 SCN4A 0.38
10253 SPRY2 0.38
1910 EDNRB 0.38
9249 DHRS3 0.38
22869 ZNF510 0.38
114800 CCDC85A 0.35
2550 GABBR1 0.35
4638 MYLK 0.35
2327 FMO2 0.35
139411 PTCHD1 0.35
10391 CORO2B 0.35
25854 FAM149A 0.35
55701 ARHGEF40 0.36
1759 DNM1 0.36
22849 CPEB3 0.36
57716 PRX 0.36
1628 DBP 0.36
80031 SEMA6D 0.36
259217 HSPA12A 0.36
6909 TBX2 0.36
1511 CTSG 0.36
79971 WLS 0.36
90865 IL33 0.36
11343 MGLL 0.36
55800 SCN3B 0.36
1949 EFNB3 0.36
284217 LAMA1 0.36
22927 HABP4 0.37
23645 PPP1R15A 0.39
342574 KRT27 0.39
83543 AIF1L 0.39
624 BDKRB2 0.39
347 APOD 0.39
84935 C13orf33 0.39
858 CAV2 0.39
5138 PDE2A 0.40
114928 GPRASP2 0.40
58190 CTDSP1 0.40
513 ATP5D 0.40
57684 ZBTB26 0.40
7041 TGFB1I1 0.40
5787 PTPRB 0.40
7294 TXK 0.40
56301 SLC7A10 0.40
55937 APOM 0.40
6368 CCL23 0.40
55020 TTC38 0.40
134265 AFAP1L1 0.40
4485 MST1 0.40
3384 ICAM2 0.38
8613 PPAP2B 0.38
1950 EGF 0.38
55273 TMEM100 0.38
6297 SALL2 0.38
9365 KL 0.38
8863 PER3 0.38
8404 SPARCL1 0.38
2202 EFEMP1 0.38
8369 HIST1H4G 0.38
5187 PER1 0.39
30815 ST6GALNAC6 0.39
256364 EML3 0.39
57381 RHOJ 0.39
761 CA3 0.39
83989 FAM172A 0.39
1408 CRY2 0.39
2281 FKBP1B 0.39
51222 ZNF219 0.39
54540 FAM193B 0.39
4053 LTBP2 0.39
55184 DZANK1 0.39
5740 PTGIS 0.39
84814 PPAPDC3 0.42
79365 BHLHE41 0.42
316 AOX1 0.42
23380 SRGAP2 0.42
84033 OBSCN 0.42
90353 CTU1 0.42
9013 TAF1C 0.42
474344 GIMAP6 0.42
84883 AIFM2 0.42
58480 RHOU 0.42
65982 ZSCAN18 0.42
666 BOK 0.42
79762 C1orf115 0.42
525 ATP6V1B1 0.42
4675 NAP1L3 0.42
3257 HPS1 0.43
55781 RIOK2 0.43
63947 DMRTC1 0.43
1969 EPHA2 0.43
25927 CNRIP1 0.43
57685 CACHD1 0.43
51559 NT5DC3 0.40
7169 TPM2 0.40
51705 EMCN 0.40
8938 BAIAP3 0.40
10365 KLF2 0.40
59 ACTA2 0.40
80309 SPHKAP 0.40
3779 KCNMB1 0.41
10826 C5orf4 0.41
219654 ZCCHC24 0.41
92162 TMEM88 0.41
7450 VWF 0.41
10266 RAMP2 0.41
25875 LETMD1 0.41
1938 EEF2 0.41
121551 BTBD11 0.41
2119 ETV5 0.41
9696 CROCC 0.41
1031 CDKN2C 0.41
9037 SEMA5A 0.41
3397 ID1 0.41
84707 BEX2 0.41
57616 TSHZ3 0.41
1471 CST3 0.41
55214 LEPREL1 0.41
3914 LAMB3 0.41
57478 USP31 0.41
3783 KCNN4 0.41
8839 WISP2 0.41
1583 CYP11A1 0.42
10124 ARL4A 0.42
738 C11orf2 0.42
29800 ZDHHC1 0.42
23135 KDM6B 0.44
171024 SYNPO2 0.44
10350 ABCA9 0.44
3691 ITGB4 0.44
2348 FOLR1 0.44
11145 PLA2G16 0.44
554 AVPR2 0.45
64072 CDH23 0.45
80177 MYCT1 0.45
5957 RCVRN 0.45
408 ARRB1 0.45
29997 GLTSCR2 0.43
26051 PPP1R16B 0.43
83604 TMEM47 0.43
2308 FOXO1 0.43
55225 RAVER2 0.43
54839 LRRC49 0.43
122953 JDP2 0.43
29775 CARD10 0.43
166 AES 0.43
25924 MYRIP 0.43
2852 GPER 0.43
51421 AMOTL2 0.43
124936 CYB5D2 0.43
1294 COL7A1 0.43
127435 PODN 0.43
84952 CGNL1 0.43
83483 PLVAP 0.43
1958 EGR1 0.43
230 ALDOC 0.43
65987 KCTD14 0.43
4804 NGFR 0.44
64852 TUT1 0.44
84253 GARNL3 0.44
5866 RAB3IL1 0.44
10608 MXD4 0.44
4211 MEIS1 0.44
83547 RILP 0.44
9172 MYOM2 0.44
57192 MCOLN1 0.44
255877 BCL6B 0.44
56904 SH3GLB2 0.44
51285 RASL12 0.44
3425 IDUA 0.44
402117 VWC2L 0.46
81490 PTDSS2 0.46
283748 PLA2G4D 0.46
23523 CABIN1 0.46
6146 RPL22 0.46
85360 SYDE1 0.46
60468 BACH2 0.46
57451 ODZ2 0.46
4013 VWA5A 0.46
339768 ESPNL 0.46
3860 KRT13 0.46
144699 FBXL14 0.45
83719 YPEL3 0.45
22841 RAB11FIP2 0.45
283927 NUDT7 0.45
293 SLC25A6 0.45
90507 SCRN2 0.45
37 ACADVL 0.45
112744 IL17F 0.45
6709 SPTAN1 0.45
8086 AAAS 0.45
7423 VEGFB 0.45
64221 ROBO3 0.45
7273 TTN 0.45
2657 GDF1 0.45
59271 C21orf63 0.45
132160 PPM1M 0.45
27244 SESN1 0.45
51310 SLC22A17 0.45
4828 NMB 0.45
54360 CYTL1 0.45
203245 NAIF1 0.45
23166 STAB1 0.45
2121 EVC 0.45
116496 FAM129A 0.45
23239 PHLPP1 0.45
51673 TPPP3 0.45
64094 SMOC2 0.45
6383 SDC2 0.45
2180 ACSL1 0.45
23770 FKBP8 0.45
55901 THSD1 0.46
25895 METTL21B 0.46
23731 C9orf5 0.46
126393 HSPB6 0.46
4056 LTC4S 0.46
79825 CCDC48 0.46
10810 WASF3 0.46
29911 HOOK2 0.46
583 BBS2 0.46
28984 C13orf15 0.46
1465 CSRP1 0.46
55258 THNSL2 0.46
161198 CLEC14A 0.46
3699 ITIH3 0.48
7094 TLN1 0.46
4232 MEST 0.46
1410 CRYAB 0.46
57452 GALNTL1 0.47
63935 PCIF1 0.47
25873 RPL36 0.47
9812 KIAA0141 0.47
51665 ASB1 0.47
64123 ELTD1 0.47
6122 RPL3 0.47
222962 SLC29A4 0.47
23102 TBC1D2B 0.47
3476 IGBP1 0.47
93408 MYL10 0.47
5310 PKD1 0.47
4628 MYH10 0.47
221935 SDK1 0.47
23328 SASH1 0.47
8522 GAS7 0.47
10023 FRAT1 0.47
7301 TYRO3 0.47
2767 GNA11 0.47
9457 FHL5 0.47
4094 MAF 0.47
65268 WNK2 0.47
54585 LZTFL1 0.47
375449 MAST4 0.47
138311 FAM69B 0.47
160622 GRASP 0.47
22837 COBLL1 0.47
51435 SCARA3 0.47
217 ALDH2 0.47
6236 RRAD 0.47
8322 FZD4 0.47
653275 CFC1B 0.47
10908 PNPLA6 0.47
57526 PCDH19 0.47
8424 BBOX1 0.47
9905 SGSM2 0.48
10435 CDC42EP2 0.48
23087 TRIM35 0.48
60314 C12orf10 0.48
1073 CFL2 0.48
5256 PHKA2 0.49
92922 CCDC102A 0.48
65057 ACD 0.48
9095 TBX19 0.48
6441 SFTPD 0.48
22846 VASH1 0.48
51066 C3orf32 0.48
23179 RGL1 0.48
4664 NAB1 0.48
50511 SYCP3 0.48
6430 SRSF5 0.48
11078 TRIOBP 0.48
78991 PCYOX1L 0.48
6623 SNCG 0.48
23384 SPECC1L 0.48
53826 FXYD6 0.48
9397 NMT2 0.48
6041 RNASEL 0.48
113510 HELQ 0.48
64788 LMF1 0.48
2217 FCGRT 0.48
79720 VPS37B 0.48
6764 ST5 0.48
252969 NEIL2 0.48
8987 STBD1 0.48
41 ACCN2 0.48
7905 REEP5 0.48
5919 RARRES2 0.48
10544 PROCR 0.48
6876 TAGLN 0.48
8436 SDPR 0.49
23500 DAAM2 0.49
130132 RFTN2 0.49
80310 PDGFD 0.49
4215 MAP3K3 0.49
282775 OR5J2 0.49
51161 C3orf18 0.49
29098 RANGRF 0.49
53336 CPXCR1 0.49
9081 PRY 0.49
9459 ARHGEF6 0.49
2995 GYPC 0.49
23057 NMNAT2 0.49
4669 NAGLU 0.49
6452 SH3BP2 0.49
6237 RRAS 0.49
5288 PIK3C2G 0.49
10252 SPRY1 0.49
79026 AHNAK 0.49
9693 RAPGEF2 0.49
51226 COPZ2 0.49
158326 FREM1 0.49
1956 EGFR 0.49
5360 PLTP 0.49
290 ANPEP 0.49
1756 DMD 0.49
5118 PCOLCE 0.49
56654 NPDC1 0.49
9254 CACNA2D2 0.49
55536 CDCA7L 0.49
124975 GGT6 0.49
1906 EDN1 0.49
81029 WNT5B 0.49
2646 GCKR 0.49
9811 CTIF 0.50
145376 PPP1R36 0.50
222865 TMEM130 0.50
92999 ZBTB47 0.50
168002 DACT2 0.50
6829 SUPT5H 0.50
9992 KCNE2 0.50
58509 C19orf29 0.50
79706 PRKRIP1 0.50
1153 CIRBP 0.50
9639 ARHGEF10 0.50
4054 LTBP3 0.50
1120 CHKB 0.50
286046 XKR6 0.50
9590 AKAP12 0.50
64115 C10orf54 0.50
2067 ERCC1 0.50
7507 XPA 0.50
22897 CEP164 0.50
652 BMP4 0.50
55702 CCDC94 0.50
57613 KIAA1467 0.50
28514 DLL1 0.50
169270 ZNF596 0.50
83982 IFI27L2 0.50
51458 RHCG 0.49
1112 FOXN3 0.49
29954 POMT2 0.49
9612 NCOR2 0.49
3198 HOXA1 0.49
5311 PKD2 0.49
2946 GSTM2 0.49
2109 ETFB 0.49
56062 KLHL4 0.49
6915 TBXA2R 0.50
64288 ZNF323 0.50
5195 PEX14 0.50
84557 MAP1LC3A 0.50
6164 RPL34 0.50
8835 SOCS2 0.50
2735 GLI1 0.50
26022 TMEM98 0.50
3908 LAMA2 0.50
1825 DSC3 0.50
5730 PTGDS 0.50
162515 SLC16A11 0.51
274 BIN1 0.51
79654 HECTD3 0.51
22863 ATG14 0.51
25949 SYF2 0.51
84872 ZC3H10 0.51
23187 PHLDB1 0.51
5434 POLR2E 0.51
6181 RPLP2 0.51
6141 RPL18 0.51
84747 UNC119B 0.51
23399 CTDNEP1 0.51
599 BCL2L2 0.51
197258 FUK 0.51
5207 PFKFB1 0.51
8131 NPRL3 0.51
25839 COG4 0.51
10816 SPINT3 0.51
60485 SAV1 0.51
5681 PSKH1 0.51
80318 GKAP1 0.51
57088 PLSCR4 0.51
93129 ORAI3 0.51
5829 PXN 0.51
2247 FGF2 0.50
26248 OR2K2 0.50
84303 CHCHD6 0.50
3615 IMPDH2 0.50
1813 DRD2 0.50
80148 PQLC1 0.50
390081 OR52E4 0.50
352954 GATS 0.50
90871 C9orf123 0.50
50945 TBX22 0.52
5204 PFDN5 0.52
5338 PLD2 0.52
94 ACVRL1 0.52
54039 PCBP3 0.52
7691 ZNF132 0.52
338 APOB 0.52
84658 EMR3 0.52
283232 TMEM80 0.52
5430 POLR2A 0.52
54623 PAF1 0.52
11070 TMEM115 0.52
10395 DLC1 0.52
57140 RNPEPL1 0.52
79781 IQCA1 0.52
1838 DTNB 0.52
51386 EIF3L 0.52
56919 DHX33 0.52
57542 KLHDC5 0.52
3628 INPP1 0.52
4520 MTF1 0.52
8547 FCN3 0.52
60401 EDA2R 0.52
8082 SSPN 0.52
80755 AARSD1 0.52
710 SERPING1 0.52
56246 MRAP 0.52
10555 AGPAT2 0.52
949 SCARB1 0.52
23743 BHMT2 0.52
3910 LAMA4 0.52
60370 AVPI1 0.52
5021 OXTR 0.52
55997 CFC1 0.52
23144 ZC3H3 0.52
56776 FMN2 0.51
85456 TNKS1BP1 0.51
283 ANG 0.51
7035 TFPI 0.51
51232 CRIM1 0.51
112616 CMTM7 0.51
22981 NINL 0.51
8727 CTNNAL1 0.51
9902 MRC2 0.51
10900 RUNDC3A 0.51
51299 NRN1 0.51
79632 FAM184A 0.52
80820 EEPD1 0.52
150709 ANKAR 0.52
6591 SNAI2 0.52
10129 FRY 0.52
5166 PDK4 0.52
146433 IL34 0.52
118812 MORN4 0.53
10516 FBLN5 0.53
9463 PICK1 0.53
127495 LRRC39 0.53
7753 ZNF202 0.53
79827 CLMP 0.53
203260 CCDC107 0.53
83657 DYNLRB2 0.53

Table S1.

Up-regulated genes found in dataset GSE29174. There are 437 up-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene ID Gene Symbol Fold Change
1300 COL10A1 42.74
3007 HIST1H1D 29.72
8366 HIST1H4B 25.58
6286 S100P 25.19
1301 COL11A1 24.72
3627 CXCL10 17.83
4283 CXCL9 15.88
1387 CREBBP 12.83
27299 ADAMDEC1 12.78
54986 ULK4 12.46
55771 PRR11 12.02
54790 TET2 11.25
6241 RRM2 10.60
3433 IFIT2 10.49
6999 TDO2 9.73
1656 DDX6 9.72
55088 C10orf118 9.37
9648 GCC2 9.24
6696 SPP1 8.92
2803 GOLGA4 8.57
83540 NUF2 7.73
10112 KIF20A 7.66
9833 MELK 7.59
55165 CEP55 7.50
10142 AKAP9 7.44
9447 AIM2 7.42
54443 ANLN 5.79
6710 SPTB 5.71
7272 TTK 5.64
10635 RAD51AP1 5.49
4069 LYZ 5.37
55183 RIF1 5.34
891 CCNB1 5.34
91543 RSAD2 5.31
81610 FAM83D 5.24
64581 CLEC7A 5.10
10051 SMC4 5.02
4085 MAD2L1 4.96
55872 PBK 4.83
991 CDC20 4.82
9221 NOLC1 4.74
2124 EVI2B 4.66
375248 ANKRD36 4.66
1164 CKS2 4.64
1230 CCR1 4.62
890 CCNA2 4.56
127933 UHMK1 4.49
10274 STAG1 4.45
597 BCL2A1 4.43
55355 HJURP 4.41
54210 TREM1 4.36
253558 LCLAT1 4.26
2706 GJB2 7.33
6498 SKIL 7.13
219285 SAMD9L 7.06
10261 IGSF6 7.01
2335 FN1 6.95
699 BUB1 6.75
1058 CENPA 6.75
332 BIRC5 6.73
51203 NUSAP1 6.59
259266 ASPM 6.54
1063 CENPF 6.49
165918 RNF168 6.44
9232 PTTG1 6.34
5996 RGS1 6.07
29089 UBE2T 5.96
22974 TPX2 5.94
4321 MMP12 5.91
983 CDK1 5.89
85444 LRRCC1 5.87
29121 CLEC2D 3.83
4090 SMAD5 3.80
2123 EVI2A 3.80
57695 USP37 3.79
133418 EMB 3.76
4131 MAP1B 3.76
9787 DLGAP5 3.75
9768 KIAA0101 3.74
54625 PARP14 3.73
2215 FCGR3B 3.71
9134 CCNE2 3.70
3117 HLA-DQA1 3.68
10380 BPNT1 3.67
79056 PRRG4 3.63
10673 TNFSF13B 3.63
8467 SMARCA5 3.61
115908 CTHRC1 3.61
3428 IFI16 3.61
1520 CTSS 3.61
10797 MTHFD2 3.57
55681 SCYL2 3.57
9749 PHACTR2 3.57
94240 EPSTI1 3.56
64151 NCAPG 3.51
25879 DCAF13 3.51
1033 CDKN3 4.24
79801 SHCBP1 4.23
126731 C1orf96 4.21
6772 STAT1 4.20
55729 ATF7IP 4.14
6713 SQLE 4.14
157570 ESCO2 4.10
79871 RPAP2 4.09
9493 KIF23 4.09
4751 NEK2 4.05
10631 POSTN 4.03
23515 MORC3 4.02
7153 TOP2A 4.02
10403 NDC80 4.00
10915 TCERG1 3.99
57650 KIAA1524 3.99
23049 SMG1 3.93
80231 CXorf21 3.87
5111 PCNA 3.86
79682 MLF1IP 3.11
29123 ANKRD11 3.09
5429 POLH 3.09
701 BUB1B 3.07
200030 NBPF11 3.06
55677 IWS1 3.06
160418 TMTC3 3.04
9147 NEMF 3.04
11320 MGAT4A 3.04
5238 PGM3 3.03
2820 GPD2 3.02
388886 FAM211B 3.01
7852 CXCR4 3.00
57082 CASC5 2.99
22926 ATF6 2.98
7594 ZNF43 2.98
968 CD68 2.97
7171 TPM4 2.96
11004 KIF2C 2.96
10808 HSPH1 2.95
84909 C9orf3 2.94
1894 ECT2 2.93
1629 DBT 2.92
116969 ART5 2.90
3227 HOXC11 2.88
116064 LRRC58 3.47
29899 GPSM2 3.47
135114 HINT3 3.45
27333 GOLIM4 3.43
55839 CENPN 3.43
23213 SULF1 3.41
81671 VMP1 3.39
9889 ZBED4 3.36
3092 HIP1 3.34
51512 GTSE1 3.34
92797 HELB 3.34
51426 POLK 3.30
5611 DNAJC3 3.30
6596 HLTF 3.28
9910 RABGAP1L 3.25
528 ATP6V1C1 3.23
3833 KIFC1 3.23
197131 UBR1 3.20
29923 HILPDA 3.20
28998 MRPL13 3.19
58527 C6orf115 3.19
79000 C1orf135 3.19
9857 CEP350 3.18
84296 GINS4 3.18
81034 SLC25A32 3.15
55723 ASF1B 3.14
7110 TMF1 3.14
84081 NSRP1 3.14
23075 SWAP70 3.12
6726 SRP9 2.69
55215 FANCI 2.68
57590 WDFY1 2.67
55142 HAUS2 2.66
23047 PDS5B 2.66
5373 PMM2 2.66
11065 UBE2C 2.66
23085 ERC1 2.66
389197 C4orf50 2.65
11260 XPOT 2.65
29980 DONSON 2.65
64399 HHIP 2.64
6453 ITSN1 2.63
29108 PYCARD 2.63
9877 ZC3H11A 2.62
3149 HMGB3 2.87
10437 IFI30 2.87
57489 ODF2L 2.87
2151 F2RL2 2.86
23215 PRRC2C 2.85
128710 C20orf94 2.85
23594 ORC6 2.84
5205 ATP8B1 2.83
51430 C1orf9 2.80
57405 SPC25 2.80
112401 BIRC8 2.80
3606 IL18 2.80
115362 GBP5 2.80
50515 CHST11 2.79
83461 CDCA3 2.79
10744 PTTG2 2.78
51765 MST4 2.77
10926 DBF4 2.76
27125 AFF4 2.75
10615 SPAG5 2.75
55143 CDCA8 2.74
51602 NOP58 2.74
51478 HSD17B7 2.73
2209 FCGR1A 2.73
9958 USP15 2.72
5469 MED1 2.72
8813 DPM1 2.70
6731 SRP72 2.70
9991 PTBP3 2.70
79866 BORA 2.41
7072 TIA1 2.40
55632 G2E3 2.40
2213 FCGR2B 2.40
3987 LIMS1 2.39
829 CAPZA1 2.39
26973 CHORDC1 2.38
435 ASL 2.38
29979 UBQLN1 2.38
8548 BLZF1 2.37
9694 TTC35 2.37
55055 ZWILCH 2.36
4481 MSR1 2.36
10213 PSMD14 2.35
9966 TNFSF15 2.35
81624 DIAPH3 2.62
79723 SUV39H2 2.61
55789 DEPDC1B 2.61
10097 ACTR2 2.59
23036 ZNF292 2.58
22936 ELL2 2.57
8477 GPR65 2.57
23397 NCAPH 2.57
3015 H2AFZ 2.54
55749 CCAR1 2.53
25937 WWTR1 2.52
360023 ZBTB41 2.51
5080 PAX6 2.51
4193 MDM2 2.51
24137 KIF4A 2.51
9212 AURKB 2.51
168850 ZNF800 2.50
55109 AGGF1 2.49
23185 LARP4B 2.49
51571 FAM49B 2.49
51077 FCF1 2.49
23167 EFR3A 2.49
23468 CBX5 2.48
5396 PRRX1 2.48
10096 ACTR3 2.47
10308 ZNF267 2.47
6782 HSPA13 2.47
3832 KIF11 2.47
917 CD3G 2.47
80821 DDHD1 2.46
52 ACP1 2.46
4179 CD46 2.46
10499 NCOA2 2.44
60558 GUF1 2.44
55676 SLC30A6 2.43
6646 SOAT1 2.43
5440 POLR2K 2.43
84955 NUDCD1 2.42
54739 XAF1 2.42
84295 PHF6 2.23
7295 TXN 2.23
2710 GK 2.23
10905 MAN1A2 2.22
6780 STAU1 2.22
51582 AZIN1 2.35
54843 SYTL2 2.34
9039 UBA3 2.33
933 CD22 2.33
5685 PSMA4 2.33
9885 OSBPL2 2.33
9262 STK17B 2.33
56942 C16orf61 2.32
10767 HBS1L 2.32
87178 PNPT1 2.32
6303 SAT1 2.32
7316 UBC 2.32
4205 MEF2A 2.32
85465 EPT1 2.31
84640 USP38 2.31
5810 RAD1 2.30
64397 ZFP106 2.29
5706 PSMC6 2.29
22948 CCT5 2.29
10672 GNA13 2.29
339344 MYPOP 2.28
7292 TNFSF4 2.28
57103 C12orf5 2.28
388403 YPEL2 2.28
54876 DCAF16 2.27
113235 SLC46A1 2.27
11177 BAZ1A 2.27
339175 METTL2A 2.26
26586 CKAP2 2.26
55785 FGD6 2.26
24145 PANX1 2.25
253461 ZBTB38 2.25
23232 TBC1D12 2.25
995 CDC25C 2.25
55974 SLC50A1 2.25
472 ATM 2.25
23008 KLHDC10 2.24
10024 TROAP 2.24
9521 EEF1E1 2.24
7402 UTRN 2.09
55589 BMP2K 2.08
158747 MOSPD2 2.08
56886 UGGT1 2.07
203100 HTRA4 2.07
10282 BET1 2.22
134430 WDR36 2.21
4299 AFF1 2.21
6747 SSR3 2.21
7334 UBE2N 2.21
5965 RECQL 2.21
4605 MYBL2 2.2
6093 ROCK1 2.19
161725 OTUD7A 2.19
23518 R3HDM1 2.18
2239 GPC4 2.18
28977 MRPL42 2.18
64859 OBFC2A 2.18
3845 KRAS 2.18
51388 NIP7 2.18
7586 ZKSCAN1 2.18
10762 NUP50 2.17
7328 UBE2H 2.17
10730 YME1L1 2.17
23093 TTLL5 2.17
6790 AURKA 2.17
22889 KIAA0907 2.17
10875 FGL2 2.17
23161 SNX13 2.17
9169 SCAF11 2.16
1788 DNMT3A 2.15
9088 PKMYT1 2.15
23033 DOPEY1 2.13
89882 TPD52L3 2.13
6556 SLC11A1 2.13
64216 TFB2M 2.13
3071 NCKAP1L 2.13
51068 NMD3 2.13
509 ATP5C1 2.13
953 ENTPD1 2.13
51105 PHF20L1 2.13
5062 PAK2 2.13
9205 ZMYM5 2.12
55157 DARS2 2.12
8520 HAT1 2.11
79739 TTLL7 2.11
9495 AKAP5 2.10
3181 HNRNPA2B
1
2.10
55279 ZNF654 2.07
54499 TMCO1 2.07
81930 KIF18A 2.07
142686 ASB14 2.06
55209 SETD5 2.06
9736 USP34 2.04
116285 ACSM1 2.04
2201 FBN2 2.04
963 CD53 2.04
55159 RFWD3 2.03
9871 SEC24D 2.03
9887 SMG7 2.02
23376 UFL1 2.02
79646 PANK3 2.01
50613 UBQLN3 2.00
201595 STT3B 2.00
59345 GNB4 1.99
5876 RABGGTB 1.99
79820 CATSPERB 1.99
6637 SNRPG 1.99
51330 TNFRSF12A 1.99
9928 KIF14 1.99
286097 EFHA2 1.98
9131 AIFM1 1.98
488 ATP2A2 1.98
23042 PDXDC1 1.98
7114 TMSB4X 1.98
9123 SLC16A3 1.98
54454 ATAD2B 1.97
23143 LRCH1 1.97
4212 MEIS2 1.97
1457 CSNK2A1 1.97
80012 PHC3 1.97
128497 SPATA25 1.96
186 AGTR2 1.96
53981 CPSF2 1.96
56996 SLC12A9 1.96
1584 CYP11B1 1.96
133619 PRRC1 1.96
4288 MKI67 1.96
9014 TAF1B 1.96
55858 TMEM165 1.96
2212 FCGR2A 1.96
389898 UBE2NL 2.10
29850 TRPM5 2.10
3070 HELLS 2.10
331 XIAP 2.09
55751 TMEM184C 2.09
2146 EZH2 2.09
26057 ANKRD17 1.95
128061 C1orf131 1.95
64090 GAL3ST2 1.94
130507 UBR3 1.93
2298 FOXD4 1.93
123169 LEO1 1.93
57187 THOC2 1.93
148789 B3GALNT2 1.93
58508 MLL3 1.92
5701 PSMC2 1.92
148066 ZNRF4 1.92
6670 SP3 1.92
10075 HUWE1 1.96
220988 HNRNPA3 1.96
80146 UXS1 1.95
122011 CSNK1A1L 1.95
150468 CKAP2L 1.95
84624 FNDC1 1.95
7332 UBE2L3 1.92
3336 HSPE1 1.92
54800 KLHL24 1.92
2290 FOXG1 1.91
50848 F11R 1.91
10627 MYL12A 1.91
5074 PAWR 1.91
6476 SI 1.91
1009 CDH11 1.90
29066 ZC3H7A 1.90
51319 RSRC1 1.90

Table S4.

Result of ROC curve analysis on our miRNA array data. ROC analysis was done to validate the diagnostic value of the miRNA in the miRNA-regulated PINs.

miRBase Accession miRNA name AUC p-value
MIMAT0002856 hsa-miR-520d-3p 0.49 0.549112
MIMAT0000265 hsa-miR-204-5p 0.98 6.47 × 10−10 ***
MIMAT0000272 hsa-miR-215 0.21 0.999782
MIMAT0000271 hsa-miR-214-3p 0.68 0.010387 *
MIMAT0002820 hsa-miR-497-5p 0.99 2.75 × 10−10 ***
MIMAT0000076 hsa-miR-21-5p 0.78 0.000184 ***
MIMAT0000738 hsa-miR-383 0.60 0.106284
MIMAT0000423 hsa-miR-125b-5p 0.99 2.48 × 10−10 ***
MIMAT0000064 hsa-let-7c 0.93 3.79 × 10−8 ***
MIMAT0000089 hsa-miR-31-5p 0.80 8.63 × 10−5 ***
MIMAT0000077 hsa-miR-22-3p 0.27 0.99749
MIMAT0000098 hsa-miR-100-5p 0.98 5.55 × 10−10 ***
MIMAT0000097 hsa-miR-99a-5p 0.99 2.55 × 10−10 ***
MIMAT0000443 hsa-miR-125a-5p 0.31 0.990694
MIMAT0002819 hsa-miR-193b-3p 0.41 0.86128
MIMAT0000250 hsa-miR-139-5p 0.99 2.42 × 10−10 ***
MIMAT0000437 hsa-miR-145-5p 0.96 3.14 × 10−9 ***
MIMAT0000421 hsa-miR-122-5p 0.48 0.597483

AUC: area under (ROC) curve;

*

p-value < 0.05;

***

p-value < 0.001.

Table S5.

Summary of constructed miRNA-regulated networks. L0 gene: genes connected directly to the miRNA (i.e., direct target of miRNA); L1 gene: genes not connected directly to the miRNA.

miRBase Accession miR name Total gene count L0 count L1 count
MIMAT0002819 hsa-miR-193b-3p 16 1 15
MIMAT0000250 hsa-miR-139-5p 28 10 18
MIMAT0000437 hsa-miR-145-5p 86 22 64
MIMAT0000423 hsa-miR-125b-5p 211 16 195
MIMAT0000443 hsa-miR-125a-5p 206 14 192
MIMAT0000097 hsa-miR-99a-5p 14 1 13
MIMAT0000265 hsa-miR-204-5p 64 18 46
MIMAT0000076 hsa-miR-21-5p 91 16 75
MIMAT0000064 hsa-let-7c 96 20 76
MIMAT0000421 hsa-miR-122-5p 5 3 2
MIMAT0000098 hsa-miR-100-5p 14 1 13
MIMAT0000272 hsa-miR-215 3 3 0
MIMAT0000271 hsa-miR-214-3p 14 8 6
MIMAT0000738 hsa-miR-383 34 3 31
MIMAT0002856 hsa-miR-520d-3p 146 23 123
MIMAT0000077 hsa-miR-22-3p 46 11 35
MIMAT0002820 hsa-miR-497-5p 267 32 235
MIMAT0000089 hsa-miR-31-5p 34 3 31

Table S6.

Specific enriched GO terms of each miRNA-regulated PINs. Genes annotated with the specific GO term in the PIN were also listed in this table. Adj. p-value: multiple-test adjusted p-value calculated by the method described in the work of Benjamini and Yekutieli [48].

MIMAT0002856(hsa-miR-520d-3p)

GO term Genes Adj. p-value
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway SH3KBP1, HDAC2, RET, ABI1, LYN, GRB2, SORBS1, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, RPS6, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, EIF4G1, HDAC1, ECT2, MKNK1, CASP3, PRKACA, ADRB2, PRKAR2A, EIF4B, SHC1, RAC1 2.77 × 10−29

GO:0048011, Nerve growth factor receptor signaling pathway HDAC2, GRB2, CLTC, CLTA, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, HDAC1, ECT2, CASP3, PRKACA, PRKAR2A, SHC1, RAC1 5.09 × 10−24

GO:0007173, Epidermal growth factor receptor signaling pathway SH3KBP1, GRB2, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, CDK1, SH3GL2, PRKACA, PRKAR2A, SHC1 2.96 × 10−22

GO:0043067, Regulation of programmed cell death HDAC2, STK17B, ESR1, ABL1, LYN, TP53, GABRB3, PAK2, LCK, CASP9, RAF1, PLK1, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, IFT57, ARHGAP10, ARHGEF6, CDK1, APAF1, HDAC1, ECT2, CASP3, SOX10, EP300, ARAF, TFAP2A, ADRB2, HCK, KLHL20, CASP8, HIP1, RAC1 4.76 × 10−19

GO:0042058, Regulation of epidermal growth factor receptor signaling pathway SH3KBP1, ESR1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL2, SHC1 2.36 × 10−12

GO:0008543, Fibroblast growth factor receptor signaling pathway GRB2, CASP9, RAF1, SRC, MAPK3, PRKCA, PRKAR2B, MAPK1, CDK1, MKNK1, PRKACA, PRKAR2A, SHC1 2.39 × 10−12

GO:0043068, Positive regulation of programmed cell death STK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC1 3.09 × 10−12

GO:0010942, Positive regulation of cell death STK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC1 4.49 × 10−12

GO:0006917, Induction of apoptosis STK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC1 6.91 × 10−11

GO:0012502, Induction of programmed cell death STK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC1 7.42 × 10−11

GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway SH3KBP1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL2 8.63 × 10−11

GO:0015630, Microtubule cytoskeleton STMN1, SORBS1, SMAD4, CLTC, CDC42, LCK, RACGAP1, PLK1, PRKAR2B, YES1, MAPK1, IFT57, CDK1, ECT2, PRKACA, RB1, EP300, CCNB1, CHAF1B, TFAP2A, CASP8, PRKAR2A 4.75 × 10−10

GO:0060548, Negative regulation of cell death HDAC2, ESR1, TP53, SMAD4, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL20 6.27 × 10−8

GO:0008286, Insulin receptor signaling pathway GRB2, SORBS1, RAF1, MAPK3, RPS6, MAPK1, CDK1, EIF4G1, EIF4B, SHC1 2.15 × 10−7

GO:0043069, Negative regulation of programmed cell death HDAC2, ESR1, TP53, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL20 3.13 × 10−7

GO:0008284, Positive regulation of cell proliferation HDAC2, ESR1, LYN, CDC42, E2F1, PRKCA, MAPK1, CDK1, RHOG, HDAC1, NCK1, SOX10, CCNB1, ADRB2, HCK, SHC1 8.34 × 10−7

GO:0051988, Regulation of attachment of spindle microtubules to kinetochore CDC42, RACGAP1, ECT2, CCNB1 3.27 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals ABL1, TP53, CASP9, APAF1, CASP3, EP300, CASP8 5.83 × 10−5

MIMAT0002820(hsa-miR-497-5p)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death ESR1, MEN1, ABL1, HIPK3, PPARGC1A, SIAH1, SH3RF1, PAK2, LCK, MED1, PPARG, CBX4, ARHGEF7, YWHAB, RXRA, ACVR1, MAPK1, CASP3, CASP6, AR, PTPRF, MDM2, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, VHL, SKI, NR4A1, LYN, TP53, PSMC2, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, PSMD11, HOXA13, RPS6, ESR2, ARHGAP10, ARHGEF6, SMAD3, SKIL, RYR2, PSEN1, HCK, TRAF2 2.67 × 10−25

GO:0043068, Positive regulation of programmed cell death MEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 1.74 × 10−17

GO:0010942, Positive regulation of cell death MEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 3.08 × 10−17
GO:0008285, Negative regulation of cell proliferation MEN1, MED1, PPARG, RXRA, CASP3, AR, PTPRF, VDR, VHL, SKI, LYN, TP53, TOB1, GATA1, GATA3, RAF1, HNF4A, CDKN1B, BRD7, MED25, ESR2, ABI1, SMAD1, SMAD2, SMAD3, SMAD4, SOX7 3.85 × 10−14

GO:0015629, Actin cytoskeleton ABL1, SORBS1, FLNA, SEPT7, ANLN, MACF1, HAP1, SH3PXD2A, IQGAP2, BRCA1, ACTC1, ACTA1, MYL2, MYLK, SORBS2, ARPC4, ARPC5, ACTR2, ACTR3, ARPC1B, WASF1, WASF2, HCK 2.52 × 10−13

GO:0006917, Induction of apoptosis ABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 9.69 × 10−11

GO:0012502, Induction of programmed cell death ABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF2 1.06 × 10−10

GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway ACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, GATA4, HNF4A, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, RYR2 1.22 × 10−10

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway SORBS1, CDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, EIF4G1, RAC1, SH3KBP1, NR4A1, LYN, GRB2, RAF1, CDKN1B, RPS6, ABI1, ARHGEF6, MKNK1, PSEN1, EIF4B 1.67 × 10−10

GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway MEN1, ACVR1, SMURF2, SKI, GDF6, TP53, BMP6, GATA4, GATA6, HOXA13, SMAD2, SMAD3, SMAD4, SKIL 7.51 × 10−10

GO:0030509, BMP signaling pathway ACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, SMAD1, SMAD4, SMAD5, RYR2 3.25 × 10−9

GO:0060548, Negative regulation of cell death ESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, SMAD4, PSEN1, HCK 7.88 × 10−9

GO:0007173, Epidermal growth factor receptor signaling pathway CDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, MDM2, SH3KBP1, NR4A1, GRB2, RAF1, CDKN1B 9.22 × 10−9

GO:0030521, Androgen receptor signaling pathway PPARGC1A, MED14, MED1, AR, BRCA1, MED12, PIAS1, RAN, NR1I3 1.42 × 10−8

GO:0043069, Negative regulation of programmed cell death ESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, PSEN1, HCK 2.44 × 10−8

GO:0048011, Nerve growth factor receptor signaling pathway SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, RAC1, NR4A1, GRB2, RAF1, CDKN1B, ARHGEF6, PSEN1 2.64 × 10−8

GO:0032956, Regulation of actin cytoskeleton organization ABL1, LRP1, ARPC4, ARPC5, ACTR3, ARPC1B, SMAD3, NCK1, SORBS3, HCK, LIMK1 6.42 × 10−6

GO:0008543, Fibroblast growth factor receptor signaling pathway SRC, MAPK3, YWHAB, MAPK1, MDM2, NR4A1, GRB2, RAF1, CDKN1B, MKNK1 9.06 × 10−6

GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway CDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN1 1.11 × 10−5

GO:0042058, Regulation of epidermal growth factor receptor signaling pathway ESR1, CDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN1 1.17 × 10−5

GO:0007179, Transforming growth factor beta receptor signaling pathway ACVR1, SMURF2, SKI, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5 1.67 × 10−5

GO:0015630, Microtubule cytoskeleton STMN1, RIF1, SORBS1, CDC42, LCK, RACGAP1, YES1, YWHAB, MAPK1, SEPT7, KIF23, CDC16, MACF1, BRCA1, FEZ1, NCOR1, PLK1, CHD3, SMAD4, CEP350, CDC27, PSEN1 2.14 × 10−5

GO:0017015, Regulation of transforming growth factor beta receptor signaling pathway MEN1, SMURF2, SKI, TP53, SMAD2, SMAD3, SMAD4, SKIL 2.30 × 10−5

GO:0070302, Regulation of stress-activated protein kinase signaling cascade MEN1, ZEB2, HIPK3, SH3RF1, CDC42, MAPK3, MAPK1, LYN, NCOR1, TRAF2 2.74 × 10−5

GO:0001959, Regulation of cytokine-mediated signaling pathway HSP90AB1, MED1, PPARG, PTPRF, NR1H2, PIAS1, IL36RN, HIPK1 6.35 × 10−5

GO:0008284, Positive regulation of cell proliferation ESR1, CDC42, MED1, RARA, MAPK1, AR, MDM2, NR4A1, LYN, FZR1, BMP6, GATA1, GATA4, GATA6, CDKN1B, NCK1, HCLS1, HCK 7.29 × 10−5

MIMAT0000423(hsa-miR-125b-5p)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death HMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B1 4.56 × 10−24

GO:0060548, Negative regulation of cell death HMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B1 8.04 × 10−17

GO:0008284, Positive regulation of cell proliferation HMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN 2.20 × 10−15
GO:0043069, Negative regulation of programmed cell death HMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B1 3.62 × 10−15

GO:0043068, Positive regulation of programmed cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF2 4.51 × 10−14

GO:0010942, Positive regulation of cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF2 7.15 × 10−14

GO:0006916, Anti-apoptosis PRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B1 1.25 × 10−10

GO:0008285, Negative regulation of cell proliferation SERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN 2.08 × 10−9

GO:0015630, Microtubule cytoskeleton STMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B1 2.24 × 10−9

GO:0048011, Nerve growth factor receptor signaling pathway HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA 1.99 × 10−8

GO:0050678, Regulation of epithelial cell proliferation SERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN 3.41 × 10−8

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway FGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP1 6.39 × 10−8
GO:0006917, Induction of apoptosis PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.68 × 10−7

GO:0012502, Induction of programmed cell death PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.81 × 10−7

GO:0035666, TRIF-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.40 × 10−6

GO:0034138, Toll-like receptor 3 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.71 × 10−6

GO:0051693, Actin filament capping SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 3.43 × 10−6
GO:0002756, MyD88- independent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 3.82 × 10−6

GO:0034134, Toll-like receptor 2 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 5.33 × 10−6

GO:0034130, Toll-like receptor 1 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 5.33 × 10−6

GO:0030835, Negative regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 5.58 × 10−6

GO:0015629, Actin cytoskeleton WAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB49 5.58 × 10−6

GO:0002755, MyD88-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 7.66 × 10−6

GO:0034142, Toll-like receptor 4 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 1.14 × 10−5

GO:0050679, Positive regulation of epithelial cell proliferation FGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1 1.14 × 10−5

MIMAT0000076(hsa-miR-21-5p)

GO term Genes Adj.p-value

GO:0030834, Regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 1.27 × 10−5

GO:0030837, Negative regulation of actin filament polymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.71 × 10−5

GO:0002224, Toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.96 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals PML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI16 3.52 × 10−5

GO:0043067, Regulation of programmed cell death SPRY2, TP53, ADAMTSL4, ETS1, TDGF1, RAF1, HOXA5, HOXA13, MSX1, MSX2, NKX2-5, CBL, INHBB, COL4A3, ACVR1C, TRAF2 1.92 × 10−5
GO:0007173, Epidermal growth factor receptor signaling pathway SPRY2, SPRY1, GRB2, PTPN11, TDGF1, RAF1, CBL 9.44 × 10−5

MIMAT0000250(hsa-miR-139-5p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta LTBP1, FBN1, FBN2 2.61 × 10−5

MIMAT0000089(hsa-miR-31-5p)

GO term Genes Adj.p-value

GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messenger GNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR4 8.74 × 10−7

GO:0019935, Cyclic-nucleotidemediated signaling GNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR4 1.60 × 10−6

GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messenger GNA12, GNA13, DRD5, S1PR3, TSHR, S1PR4 6.82 × 10−6

GO:0048011, Nerve growth factor receptor signaling pathway ARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF11 6.93 × 10−6

GO:0019933, CAMP-mediated signaling GNA12, GNA13, DRD5, S1PR3, TSHR, S1PR4 1.05 × 10−5

GO:0043067, Regulation of programmed cell death PTK2B, PRKCD, TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11, F2R 1.25 × 10−5

GO:0003376, Sphingosine-1- phosphate signaling pathway S1PR3, S1PR2, S1PR4 3.22 × 10−5

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway PTK2B, ARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF11 5.20 × 10−5

GO:0043068, Positive regulation of programmed cell death TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 7.67 × 10−5

GO:0010942, Positive regulation of cell death TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 8.78 × 10−5

GO:0006917, Induction of apoptosis TGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 9.32 × 10−5
GO:0012502, Induction of programmed cell death TGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF11 9.51 × 10−5

MIMAT0000437(hsa-miR-145-5p)

GO term Genes Adj.p-value

GO:0030509, BMP signaling pathway BMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF6 1.37 × 10−11

GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway BMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF6 2.56 × 10−8

GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway MEN1, TP53, BMP6, HOXA13, ACVR1, SMAD4, SULF1, SMURF2, GDF6 8.22 × 10−8

MIMAT0000064(hsa-let-7c)

GO term Genes Adj.p-value

GO:0043067, Regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, PSMA3, BIRC5, ACTN2, HOXA13, IRS2, FASTK, VAV1, PSMB6, BCL2, CDK1, HDAC1, SOX10, TIA1, AKT1, AURKB 3.43 × 10−8

GO:0043069, Negative regulation of programmed cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 6.58 × 10−6

GO:0060548, Negative regulation of cell death RRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB 8.92 × 10−6

GO:0015630, Microtubule cytoskeleton INCENP, SNTB2, SEPT1, TACC1, BIRC5, RACGAP1, PIN4, CDCA8, CDK1, PHF1, AKT1, AURKB, NINL, CCDC85B 5.15 × 10−5

GO:0043067, Regulation of programmed cell death HMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B1 1.62 × 10−23

GO:0060548, Negative regulation of cell death HMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B1 4.53 × 10−17

GO:0008284, Positive regulation of cell proliferation HMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN 1.23 × 10−15

MIMAT0000443(hsa-miR-125a-5p)

GO term Genes Adj.p-value

GO:0043069, Negative regulation of programmed cell death HMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B1 2.02 × 10−15

GO:0043068, Positive regulation of programmed cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF2 2.80 × 10−13

GO:0010942, Positive regulation of cell death HMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF2 4.35 × 10−13

GO:0006916, Anti-apoptosis PRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B1 9.04 × 10−11

GO:0008285, Negative regulation of cell proliferation SERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN 1.32 × 10−9

GO:0015630, Microtubule cytoskeleton STMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B1 1.32 × 10−9

GO:0048011, Nerve growth factor receptor signaling pathway HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA 1.50 × 10−8

GO:0050678, Regulation of epithelial cell proliferation SERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN 2.66 × 10−8

GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway FGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP1 4.37 × 10−8

GO:0006917, Induction of apoptosis PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.22 × 10−7

GO:0012502, Induction of programmed cell death PML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF2 1.31 × 10−7

GO:0035666, TRIF-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.01 × 10−6

GO:0034138, Toll-like receptor 3 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.27 × 10−6

GO:0051693, Actin filament capping SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.97 × 10−6

GO:0002756, MyD88- independent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 3.20 × 10−6

GO:0015629, Actin cytoskeleton WAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB49 4.25 × 10−6

GO:0034134, Toll-like receptor 2 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 4.43 × 10−6

GO:0034130, Toll-like receptor 1 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 4.43 × 10−6

GO:0030835, Negative regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 4.72 × 10−6

GO:0002755, MyD88-dependent toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 6.41 × 10−6

GO:0050679, Positive regulation of epithelial cell proliferation FGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1 9.31 × 10−6

GO:0034142, Toll-like receptor 4 signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 9.31 × 10−6

GO:0030834, Regulation of actin filament depolymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 1.09 × 10−5

GO:0030837, Negative regulation of actin filament polymerization SPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB49 2.37 × 10−5

GO:0002224, Toll-like receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 2.48 × 10−5

GO:0008629, Induction of apoptosis by intracellular signals PML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI16 2.92 × 10−5

GO:0050851, Antigen receptor-mediated signaling pathway WAS, MEF2C, RELA, PSEN2, PTPRC, PAK1, PTEN, NFKBIA 8.55 × 10−5

GO:0002221, Pattern recognition receptor signaling pathway ATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA 9.15 × 10−5

GO:0001936, Regulation of endothelial cell proliferation FGF2, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, JUN 9.47 × 10−5

MIMAT0000077(hsa-miR-22-3p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta FBN2, FBN1, LTBP1 1.45 × 10−5

MIMAT0000265(hsa-miR-204-5p)

GO term Genes Adj.p-value

GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta FBN1, FBN2, LTBP1 6.31 × 10−5

Table S7.

GOBO survival analysis results. Genes which was annotated with the specified GO term of proteins in the PIN would be used as input gene set for GOBO analysis.

miRNA GO term p-value
MIMAT0000064(hsa-let-7c) GO:0015630, Microtubule cytoskeleton 9.97 × 106***
GO:0043067, Regulation of programmed cell death 0.268067
GO:0043069, Negative regulation of programmed cell death 0.0390439*
GO:0060548, Negative regulation of cell death 0.0390439*

MIMAT0000076(hsa-miR-21-5p) GO:0007173, Epidermal growth factor receptor signaling pathway 0.721139
GO:0043067, Regulation of programmed cell death 0.266312

MIMAT0000077(hsa-miR-22-3p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000089(hsa-miR-31-5p) GO:0003376, Sphingosine-1-phosphate signaling pathway 0.062202
GO:0006917, Induction of apoptosis 0.048584*
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.050408
GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messenger 0.289466
GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messenger 0.687572
GO:0010942, Positive regulation of cell death 0.356228
GO:0012502, Induction of programmed cell death 0.048584*
GO:0019933, CAMP-mediated signaling 0.687572
GO:0019935, Cyclic-nucleotide-mediated signaling 0.289466
GO:0043067, Regulation of programmed cell death 0.694486
GO:0043068, Positive regulation of programmed cell death 0.356228
GO:0048011, Nerve growth factor receptor signaling pathway 0.154543

MIMAT0000250(hsa-miR-139-5p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000265(hsa-miR-204-5p) GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta 0.940727

MIMAT0000423(hsa-miR-125b-5p) GO:0002224, Toll-like receptor signaling pathway 0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway 0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway 0.380928
GO:0006916, Anti-apoptosis 0.0593
GO:0006917, Induction of apoptosis 0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.776269
GO:0008284, Positive regulation of cell proliferation 0.882324
GO:0008285, Negative regulation of cell proliferation 0.883393
GO:0008629, Induction of apoptosis by intracellular signals 0.073118
GO:0010942, Positive regulation of cell death 0.972892
GO:0012502, Induction of programmed cell death 0.618064
GO:0015629, Actin cytoskeleton 0.596528
GO:0015630, Microtubule cytoskeleton 0.028245*
GO:0030834, Regulation of actin filament depolymerization 0.654383
GO:0030835, Negative regulation of actin filament depolymerization 0.654383
GO:0030837, Negative regulation of actin filament polymerization 0.654383
GO:0034130, Toll-like receptor 1 signaling pathway 0.380928
GO:0034134, Toll-like receptor 2 signaling pathway 0.380928
GO:0034138, Toll-like receptor 3 signaling pathway 0.380928
GO:0034142, Toll-like receptor 4 signaling pathway 0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway 0.380928
GO:0043067, Regulation of programmed cell death 0.643418
GO:0043068, Positive regulation of programmed cell death 0.972892
GO:0043069, Negative regulation of programmed cell death 0.492576
GO:0048011, Nerve growth factor receptor signaling pathway 0.171634
GO:0050678, Regulation of epithelial cell proliferation 0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation 0.205483
GO:0051693, Actin filament capping 0.654383
GO:0060548, Negative regulation of cell death 0.413665

MIMAT0000437(hsa-miR-145-5p) GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway 0.196953
GO:0030509, BMP signaling pathway 0.196953
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 0.843529

MIMAT0000443(hsa-miR-125a-5p) GO:0001936, Regulation of endothelial cell proliferation 0.115146
GO:0002221, Pattern recognition receptor signaling pathway 0.380928
GO:0002224, Toll-like receptor signaling pathway 0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway 0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway 0.380928
GO:0006916, Anti-apoptosis 0.0593
GO:0006917, Induction of apoptosis 0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.776269
GO:0008284, Positive regulation of cell proliferation 0.882324
GO:0008285, Negative regulation of cell proliferation 0.883393
GO:0008629, Induction of apoptosis by intracellular signals 0.073118
GO:0010942, Positive regulation of cell death 0.972892
GO:0012502, Induction of programmed cell death 0.618064
GO:0015629, Actin cytoskeleton 0.596528
GO:0015630, Microtubule cytoskeleton 0.028245*
GO:0030834, Regulation of actin filament depolymerization 0.654383
GO:0030835, Negative regulation of actin filament depolymerization 0.654383
GO:0030837, Negative regulation of actin filament polymerization 0.654383
GO:0034130, Toll-like receptor 1 signaling pathway 0.380928
GO:0034134, Toll-like receptor 2 signaling pathway 0.380928
GO:0034138, Toll-like receptor 3 signaling pathway 0.380928
GO:0034142, Toll-like receptor 4 signaling pathway 0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway 0.380928
GO:0043067, Regulation of programmed cell death 0.643418
GO:0043068, Positive regulation of programmed cell death 0.972892
GO:0043069, Negative regulation of programmed cell death 0.492576
GO:0048011, Nerve growth factor receptor signaling pathway 0.171634
GO:0050678, Regulation of epithelial cell proliferation 0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation 0.205483
GO:0050851, Antigen receptor-mediated signaling pathway 0.103325
GO:0051693, Actin filament capping 0.654383
GO:0060548, Negative regulation of cell death 0.413665

MIMAT0002820(hsa-miR-497-5p) GO:0001959, Regulation of cytokine-mediated signaling pathway 0.06699
GO:0006917, Induction of apoptosis 0.142401
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.837635
GO:0007173, Epidermal growth factor receptor signaling pathway 0.387447
GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway 0.240167
GO:0007179, Transforming growth factor beta receptor signaling pathway 0.017876*
GO:0008284, Positive regulation of cell proliferation 0.430255
GO:0008285, Negative regulation of cell proliferation 0.149994
GO:0008543, Fibroblast growth factor receptor signaling pathway 0.521978
GO:0010942, Positive regulation of cell death 0.237692
GO:0012502, Induction of programmed cell death 0.142401
GO:0015629, Actin cytoskeleton 0.228804
GO:0015630, Microtubule cytoskeleton 0.18331
GO:0017015, Regulation of transforming growth factor beta receptor signaling pathway 0.128773
GO:0030509, BMP signaling pathway 0.39837
GO:0030521, Androgen receptor signaling pathway 0.383811
GO:0032956, Regulation of actin cytoskeleton organization 0.91762
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway 0.934045
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway 0.789492
GO:0043067, Regulation of programmed cell death 0.111544
GO:0043068, Positive regulation of programmed cell death 0.237692
GO:0043069, Negative regulation of programmed cell death 0.856892
GO:0048011, Nerve growth factor receptor signaling pathway 0.471986
GO:0060548, Negative regulation of cell death 0.667437
GO:0070302, Regulation of stress-activated protein kinase signaling cascade 0.561032
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway 0.182314

MIMAT0002856(hsa-miR-520d-3p) GO:0006917, Induction of apoptosis 0.489781
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway 0.171689
GO:0007173, Epidermal growth factor receptor signaling pathway 0.449696
GO:0008284, Positive regulation of cell proliferation 0.05916
GO:0008286, Insulin receptor signaling pathway 0.237933
GO:0008543, Fibroblast growth factor receptor signaling pathway 0.159318
GO:0008629, Induction of apoptosis by intracellular signals 0.502822
GO:0010942, Positive regulation of cell death 0.076906
GO:0012502, Induction of programmed cell death 0.489781
GO:0015630, Microtubule cytoskeleton 0.000292***
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway 0.762633
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway 0.826854
GO:0043067, Regulation of programmed cell death 0.740092
GO:0043068, Positive regulation of programmed cell death 0.076906
GO:0043069, Negative regulation of programmed cell death 0.067499
GO:0048011, Nerve growth factor receptor signaling pathway 0.014926*
GO:0051988, Regulation of attachment of spindle microtubules to kinetochore 7.48 × 106***
GO:0060548, Negative regulation of cell death 0.213035
*

p < 0.05,

**

p < 0.01;

***

p < 0.001.

Table S8.

Pathophysiogical characteristics of miRNA array data used in ROC curve analysis.

Sample Name ER PR HER TNM Stage Grade
S621T1 0 0 0 pT1N0M0 I 2
S434T1 1 0 1 T2N1M0 IIB 3
S403T1 0 1 0 T2N1M0 IIB 2
S459T1 1 0 0 T4N0M1 IV 1
S455N1 1 0 0 T3N3M1 IV 3
S545T1 0 0 0 pT2N0M0 IIA 3
S173N1 0 0 1 T2N3M0 IIIC 3
S363T1 1 1 0 T2N1M0 IIB 1
S909T1 1 1 1 pT3N3aM0 IIIC (Unknown)
S645T1 0 1 1 pT1bN0M0 I 3
S898T1 1 0 0 pT2N0(i-)M0 IIA (Unknown)
S201T1 1 0 0 T1N1M0 IIA 2
S631T1 1 1 1 T2N3aM1 IV 2
S303T1 0 0 1 T2N0M0 IIA 2
S502T1 0 0 0 pT3N0M0 IIB 3
S498N1 1 1 1 pT1cN1aM0 IIA 2
S536T1 1 0 1 T1cN1miM0 IIA 2
S660T1 0 0 1 T1N0M0 I 3
S358N1 0 0 1 T2pN2M0 IIIA 2
S665T1 0 0 0 T2N3M0 IIIC 3
S475T1 0 0 1 pT1cN0M0 I 3
S423T1 1 1 0 T2N3M0 IIIC 1
S507T1 0 0 0 pT1cN1aM0 IIA 3
S891T1 1 0 0 pT2NxM0 IIA 2
S422T1 1 1 0 T2N0M0 IIA 1
S961T1 0 0 1 T2N2aM0 IIIA 2
S622T1 0 0 0 pT2N0M0 IIA 2
S454T1 0 0 0 T1cN0M0 I 2
S433T1 1 0 0 T2N0M0 IIA 1
S673T1 0 0 0 pT2N0M0 IIA 2
S450T1 0 0 1 T2N1M0 IIB 3
S430T1 1 1 0 T2N3M0 IIIC 2
S437T1 1 0 0 T3N1M0 IIIA 3
S574T1 0 0 0 T2N0M0 IIA 2
S401T1 1 1 0 T1N0M0 I 1
S427N1 0 0 0 T3N1M0 IIIA 2
S894T1 0 0 0 pT1cN0M0 I 2
S929T1 1 0 0 pT2N0M0 IIA 2
S173T1 0 0 1 T2N3M0 IIIC 3
S622N1 0 0 0 pT2N0M0 IIA 2
S562T1 1 1 1 pT1aN0M0 I 3
S602T1 0 0 0 pT1cN0M0 I 3
S490T1 1 1 1 pT2N0M0 IIA 2
S677T1 0 0 0 pT2N1M0 IIB 3
S881T1 0 0 0 pT2N1aM0 IIB 3
S619T1 0 0 0 pT2N0M0 IIA 3
S446T1 0 0 0 T2N2M0 IIIA 3
S446T2 0 0 0 T2N2M0 IIIA 3
S453T1 1 1 1 T2N1M0 IIB 2
S562N1 1 1 1 pT1aN0M0 I 3
S557T1 0 0 0 pT2N0M0 IIA 3
S594T1 1 1 1 pT2N3aM0 IIIC 3
S582T1 0 0 0 pT2N0M0 IIA 3
S358T1 0 0 1 T2pN2M0 IIIA 2
S368T1 0 0 1 T3N3M0 IIIC 2
S175T1 1 1 0 T3N3Mx IIIC 3
S357T1 0 0 0 T2pN1M0 IIIC 3
S653T1 0 0 0 pT3N3aM0 IIIC 2
S722T1 1 1 1 pT1N0M0 I 3
S593T1 0 0 0 pT1N0M0 I 3
S543T1 1 1 1 pT2N1M0 IIB 2
S498T1 1 1 1 pT1cN1aM0 IIA 2
S389T1 1 0 0 T2N1M0 IIB 3
S614T1 0 1 1 TxN0M1 IIIA (Unknown)
S536N1 1 0 1 T1cN1miM0 IIA 2
S462T1 1 1 0 T3N3M0 IIIC 2
S477T1 0 0 0 pT1cN0M0 I 3
S917T1 0 0 0 T2N0M0 IIA (Unknown)
S213T1 0 0 1 T1cN1M0 IIA 3
S628T1 0 0 0 T1N0M0 I 2
S291T1 0 0 0 T3pN2M0 IIIA 3
S593N1 0 0 0 pT1N0M0 I 3
S418T1 1 1 0 T2N3M0 IIIC 2
S420T1 1 1 0 T1N0M0 I 2
S363N1 1 1 0 T2N1M0 IIB 1
S629T1 1 0 1 pT1N0M0 I 2
S586T1 1 1 1 pT2N3aM0 IIIC 2
S415T1 0 1 0 T2N2M0 IIIA 2
S439T1 1 1 0 T2N2M0 IIIA 2
S941N1 0 0 0 pT1cN1aM0 IIA 3
S893T1 0 0 0 pT2N1micM0 IIB 3
S380T1 1 0 0 T2N1M0 IIB 2
S906N1 1 1 1 pT3N3aM0 IIIC 2
S918T1 0 0 0 pT2N0M0 IIA 3
S400T1 0 0 1 T2N0M0 IIA 2
S328T1 0 0 0 T1cpN0M0 I 3
S367T1 1 0 0 T1N1M0 IIIA 1
S420N1 1 1 0 T1N0M0 I 2
S922T1 0 0 0 pT1cN0(i-)M0 I 3
S896T1 0 0 1 pT2N1aM0 IIB 2
S410T1 0 1 1 T1N0M0 I 3
S572T1 0 0 1 T4N1aM0 IIIC 3
S448T1 0 0 0 T1N0M0 I 3
S207T1 0 0 1 T2N0M0 IIA 3
S604T1 0 0 1 pT2N1M0 IIB 3
S379T1 0 0 1 T2N2M0 IIIA 2
S906T1 1 1 1 pT3N3aM0 IIIC 2
S941T2 0 0 0 pT1cN1aM0 IIA 3
S941T1 0 0 0 pT1cN1aM0 IIA 3
S375T1 1 1 0 T1N0M0 I 2
S427T1 0 0 0 T3N1M0 IIIA 2
S417T1 0 0 0 pT2N0M0 IIA 3
S180T1 1 0 0 T4NxM0 IIIC
S455T1 1 0 0 T3N3M1 IV
S887T1 0 0 0 pT2N0M0 IIA
S445T1 1 0 1 T2N1M0 IIB
S469T1 1 1 0 T2N0M0 IIA
S469T2 1 1 0 T2N0M0 IIA
S483T1 1 1 0 T2N3M0 IIIC
S444T1 1 1 0 T1NxM0 (Unknown)
S909N1 1 1 1 pT3N3aM0 IIIC
S464T1 1 1 0 T1N0M0 I
S894N1 0 0 0 pT1cN0M0 I
S698T1 1 1 1 pT1cN0M0 I
S452T1 0 0 0 T1N0M0 I
S474T1 (Unknown) (Unknown) (Unknown) (Unknown) (Unknown)
ijms-14-11560-s001.pdf (1.2MB, pdf)

Articles from International Journal of Molecular Sciences are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES