Abstract
Osteosarcoma remains a leading cause of cancer death in children and young adolescents. Although the introduction of multiagent chemotherapy, survival rates have not improved in two decades. Therefore, it is urgently needed to know the details regarding molecular etiology to driving therapeutic inroads for this disease. In this study we performed an integrated analysis of miRNA and mRNA expression data to explore the dysregulation of miRNA and miRNA-target gene regulatory network underlying OS. 59 differentially expressed miRNAs were identified, with 28 up-regulated and 31 down-regulated miRNAs by integrating OS miRNA expression data sets available. Using miRWalk databases prediction, we performed an anticorrelated analysis of miRNA and genes expression identified by a integrated analysis of gene expression data to identify 109 differently expressed miRNA target genes. A novel miRNA-target gene regulatory network was constructed with the miRNA-target gene pairs. miR-19b-3p, miR-20a-5p, miR-124-3p and their common target CCND2, the nodal points of regulatory network, may play important roles in OS. Bioinformatics analysis of biological functions and pathways demonstrated that target genes of miRNAs are highly correlated with carcinogenesis. Our findings may help to understand the molecular mechanisms of OS and identify targets of effective targeted therapies for OS.
Keywords: Integrated analysis, miRNA expression data, osteosarcoma, miRNA target genes
Introduction
Osteosarcoma (OS) is the most common primary bone malignancy in children and young adolescents characterized by malignant osteoid production and osteoblastic differentiation. After the introduction of multiagent chemotherapy in the 1980s, the 5-year survival rate has increased to approximate 60%-65% for patients without evidence of metastasis [1]. However, for patients with recurrent or metastatic OS, the prognosis is still very poor [2]. Therefore, it is urgently needed to identify the details regarding tumor progression and to discover new insights into novel therapy strategies for this disease.
MicroRNA (miRNA) are small (~22 nucleotides) non-coding RNAs, which negatively regulates gene expression by binding to the 3’-untranslated region (3’-UTR) of their target mRNA [3]. Thus, over-expression of miRNAs usually gives rise to the deceased expression of target genes. Amounts of evidence show that miRNA are deregulated in various types of cancer and play crucial roles in tumor formation and development [4,5]. miRNAs is still considered to be applied in diagnosis and prognosis as well as eventual therapy of malignant neoplasm [6].
Complex genomic aberrations and highly variable patterns of gene expression were detected in conventional OS [7]. With advances in molecular biology, emerging evidence using microarray-based approaches shows that miRNAs were deregulated in human OS compared to bone, osteoblasts and mesenchymal stem cells [8-11]. In addition, some studies identified their important role in the development of OS. miR-21 has been indicated to induce invasion and migration of the OS cell line, MG-63, by negatively regulating RECK, a tumor suppressor gene. miR-20a promotes OS metastasis by down-regulating Fas expression [12]. miR-155 involves in oncogenic regulation of OS progression such as proliferation, invasion and migration [13]. Despite these findings, the progress and development of the disease are still not clearly elucidated.
High-throughput technologies could be used for systematic researches on complex molecular processes in diseases, such as OS. Over the last two decades, many mRNA and miRNA expression studies have been performed by using microarray, a high-throughput technology to more comprehensively increase knowledge about the cellular and molecular changes in OS. However, miRNA-mRNA regulatory networks based on miRNA and mRNA expression data has not been previously elucidated. In this study, we integrated miRNA dysregulation and altered mRNA expression that occur in OS to construct identify miRNA-mRNA regulatory networks, which may provide novel insights for innovative diagnostic and treatment strategies of OS, In addition, our study would help to understand the pathology of OS.
Materials and methods
Eligible miRNA expression profiling and data preprocessing
We searched the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo) and ArrayExpress (http://www.ebi.ac.uk/arrayexpress/), for miRNA expression profiling studies in OS. We only retained miRNA expression profiling studies between OS and normal tissues by microarray. The raw microarray data was firstly downloaded from GEO and Array Express. The log2 transformation, background correction and Quantile normalization were performed for the downloaded original microarray data by MATrix LABoratory (MATLAB) software.
Differential analysis of miRNA
Based on the pretreatment results of miRNA expression values, two-tailed Student’s t-test was used to identify the differently expressed miRNA in OS compared to the normal tissues. P-values and effect sizes of individual microarray study were obtained. P-values from multiple studies were combined by Fisher’s method, and effect sizes from multiple studies were combined by the random effects model. The thresholds for differentially expressed miRNAs were P-value < 0.01.
Bioinformatics prediction of miRNA targets
As miRNAs function by down-regulating the expression of target genes, bioinformatics prediction of miRNA targets is important for the research of miRNA function. The target genes of differentially expressed miRNAs were predicted by 6 bioinformatic algorithms (DIANAmT, miRanda, miRDB, miRWalk, PICTAR and Targetscan) by the online tools of miRWalk (http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/) [14].
Combining predicted targets with gene expression profiling
Due to the reversely correlated expression between miRNA and its target genes, we combined predicted targets with gene expression profiling which was available in an recently published integrated analysis of 8 microarray datasets (PMID: 25023069) [15]. The target genes recorded by ≥ 4 algorithms were selected to compare with the gene expression profiling data, and we selected microRNA-target gene pairs with opposing expression patterns to subject to further investigation [16-18].
Constructing regulatory network between miRNAs and their targets
The posttranscriptional regulatory network is defined as a directed and bipartite graph in which expressions of miRNA-target gene interacting pairs are reversely correlated. We conducted a regulatory network of miRNAs and genes in OS with the identified miRNA-target gene interacting pairs, and visualized with Cytoscape [19].
Functional annotation
To gain insights into the biological functions of miRNA target genes, we performed Gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. GO provides a common descriptive framework and functional annotation and classification for analyze the gene sets data. KEGG pathway database is a recognized and comprehensive database including all kinds of biochemistry pathways [20]. The online based software GENECODIS was utilized in this analysis [21].
Results
Differentially expressed miRNAs in OS
In this work, we collected one expression profiling study respectively in GEO database (Accession: GSE28425) and ArrayExpress (Accession: E-MTAB-1136), including 16 samples of OS and 56 samples of normal control. After normalization of the original miRNA expression profiling, we performed miRNA differential expressed analysis between OS and normal control samples using MATLAB. Finally, 59 miRNAs were regarded as significantly differentially expressed under the threshold of P-value < 0.01, with 28 up-regulated and 31 down-regulated miRNAs (Table 1).
Table 1.
List of differentially expressed miRNAs
| miRNA | P-value | Effective_size |
|---|---|---|
| Up-regulated miRNAs | ||
| hsa-miR-9-3p | 4.33E-09 | 0.852555909 |
| hsa-miR-15a-3p | 3.86E-06 | 0.709492785 |
| hsa-miR-518b | 7.81E-06 | 0.850142061 |
| hsa-miR-106b-3p | 1.82E-05 | 0.331329545 |
| hsa-miR-149-5p | 2.20E-05 | 0.929734799 |
| hsa-miR-646 | 4.10E-05 | 0.85623655 |
| hsa-miR-137 | 7.91E-05 | 0.606474533 |
| hsa-miR-182-5p | 2.03E-04 | 0.353731074 |
| hsa-miR-323a-3p | 4.28E-04 | 0.724130102 |
| hsa-miR-624-5p | 9.64E-04 | 0.338806539 |
| hsa-miR-657 | 1.02E-03 | 0.26330906 |
| hsa-miR-769-5p | 1.11E-03 | 0.948959053 |
| hsa-miR-20a-5p | 1.60E-03 | 0.594581368 |
| hsa-miR-181a-2-3p | 1.82E-03 | 0.72873165 |
| hsa-miR-608 | 2.04E-03 | 0.836196694 |
| hsa-miR-29b-3p | 2.61E-03 | 0.104500708 |
| hsa-miR-153-3p | 2.91E-03 | 0.520093828 |
| hsa-miR-758-3p | 3.29E-03 | 0.801531307 |
| hsa-miR-17-3p | 3.55E-03 | 0.693026831 |
| hsa-miR-100-3p | 4.03E-03 | 0.165013099 |
| hsa-miR-19b-3p | 4.53E-03 | 0.540592183 |
| hsa-miR-650 | 5.46E-03 | 0.180498019 |
| hsa-miR-124-3p | 5.68E-03 | 0.566669559 |
| hsa-miR-17-5p | 7.50E-03 | 0.523690599 |
| hsa-miR-106a-3p | 8.43E-03 | 0.207385799 |
| hsa-miR-16-2-3p | 8.55E-03 | 0.432417158 |
| hsa-miR-148b-3p | 9.14E-03 | 0.622875552 |
| hsa-miR-135b-5p | 9.42E-03 | 0.15981984 |
| Down-regulated miRNAs | ||
| hsa-miR-223-3p | 3.59E-13 | -1.845850571 |
| hsa-miR-126-5p | 8.43E-13 | -1.053842174 |
| hsa-miR-126-3p | 1.36E-12 | -1.967999713 |
| hsa-miR-610 | 4.86E-10 | -1.534542956 |
| hsa-miR-671-5p | 5.33E-10 | -0.97111795 |
| hsa-miR-195-5p | 3.09E-08 | -1.268764741 |
| hsa-miR-638 | 1.36E-07 | -1.024613101 |
| hsa-miR-142-5p | 3.07E-07 | -1.834983092 |
| hsa-miR-451a | 7.62E-07 | -1.232024612 |
| hsa-miR-663a | 1.12E-05 | -1.234272536 |
| hsa-miR-144-5p | 4.66E-05 | -1.088489066 |
| hsa-miR-486-5p | 4.72E-05 | -1.116914298 |
| hsa-miR-623 | 6.04E-05 | -0.986359598 |
| hsa-miR-16-5p | 7.02E-05 | -3.67194014 |
| hsa-miR-572 | 8.81E-05 | -1.401081994 |
| hsa-miR-139-5p | 2.11E-04 | -1.170260462 |
| hsa-miR-34c-5p | 3.40E-04 | -0.242122703 |
| hsa-miR-32-3p | 3.45E-04 | -0.95081882 |
| hsa-miR-557 | 4.81E-04 | -0.985588105 |
| hsa-miR-146b-5p | 5.77E-04 | -1.562558191 |
| hsa-miR-135a-3p | 1.27E-03 | -1.183892229 |
| hsa-miR-150-5p | 1.37E-03 | -1.277301371 |
| hsa-miR-26b-5p | 2.15E-03 | -1.943465941 |
| hsa-miR-302d-3p | 2.90E-03 | -3.123108199 |
| hsa-miR-659-3p | 3.94E-03 | -1.194627283 |
| hsa-miR-335-5p | 4.09E-03 | -2.224509509 |
| hsa-miR-217 | 4.14E-03 | -2.969063299 |
| hsa-miR-200a-5p | 4.74E-03 | -1.031405471 |
| hsa-miR-148a-3p | 7.52E-03 | -0.15955827 |
| hsa-miR-652-3p | 9.43E-03 | -2.274455133 |
| hsa-miR-135b-3p | 9.72E-03 | -2.59797485 |
Identification of differently expressed miRNA target genes
To know target genes of differentially expressed miRNA in OS, bioinformatics prediction was performed by miRWalk database. In addition we compared the predicted target genes recorded by ≥ 4 algorithms, to gene expression profiling data from an integrated analysis conducted by Zuozhang Yang. As a result, we identified 158 miRNA-target gene pairs for 10 up-regulated miRNA, and 15 miRNA-target gene pairs for 7 down-regulated miRNA (Table 2).
Table 2.
The 109 miRNA-target gene pairs reversely correlated with the expressions of 17 differentailly expression miRNAs
| miRNA | Regulation (miRNA) | Count of targets | Target Genes |
|---|---|---|---|
| hsa-miR-124-3p | up | 22 | AMOTL1, BCL11A, CCND2, DDX26B, EYA4, GLI3, GNAI2, HECTD2, ITGA3, LMO4, MITF, MST4, OSBPL10, PGM1, RAB34, RARG, ROR2, SEMA6D, SERTAD4,TEAD1, VAMP3, ZFP36L1 |
| hsa-miR-137 | up | 12 | ABHD6, ALDH1A2, ATP1B1, DEXI, MITF, NRXN3, PLXNA2, PPARGC1A, PTGFRN, SYT1, TRPS1, ZBTB4 |
| hsa-miR-139-5p | down | 1 | GALNT7 |
| hsa-miR-148a-3p | down | 3 | CADM1, ELAVL2, ZNF217 |
| hsa-miR-148b-3p | up | 6 | BTBD3, CFL2, KIAA1217, NPTN, PRICKLE2, RAB34 |
| hsa-miR-149-5p | up | 1 | EXT1 |
| hsa-miR-153-3p | up | 8 | ACTN4, AUTS2, DDIT4, EXT1, FGFR2, NPTN, PPARGC1A, ZCCHC14 |
| hsa-miR-16-5p | down | 1 | SPTBN2 |
| hsa-miR-17-5p | up | 21 | ACSL4, C14orf28, CCND2, CFL2,EZH1, FRMD6, HABP4, JAZF1, LAMA3, NRP2, PRRX1, PTGFRN, RAB12, RAPGEFL1, SH3PXD2A, SMOC1, SORL1, TBL1X, TRIP10, TRPS1, ZBTB4 |
| hsa-miR-182-5p | up | 9 | BCL11A, BDNF, ISL1, JAZF1, KIAA1217, MITF, VAMP3, ZCCHC14, ZFP36L1 |
| hsa-miR-195-5p | down | 1 | SPTBN2 |
| hsa-miR-19b-3p | up | 37 | ABR, ACSL4, ACTN1, BLCAP, CALM1, CBX7, CCND2, CLIP4, DOCK3, DTNA, ETV5, FAT3, FOXP2, JAZF1, KIAA1217, LRCH2, MID1, MPPED2, MST4, NHS, NPTN, NRP2, PCDH10, PRICKLE2, RAB34, RAPGEFL1, RBMS3, SMOC1, SPRYD3, SRGAP3, ST3GAL5, SYT1, TRPS1, TSHZ3, WDR1, ZBTB4, ZNF516 |
| hsa-miR-20a-5p | up | 26 | ACSL4, ANO6, C14orf28, CCND2, CFL2, CSRNP3, EZH1, FRMD6, HABP4, HECTD2, JAZF1, LAMA3, MFN2, NRP2, PLSCR4, PRRX1, PTGFRN, RAB12, RAPGEFL1, SH3PXD2A, SMOC1, SORL1, TBL1X, TRIP10, TRPS1, ZBTB4 |
| hsa-miR-26b-5p | down | 6 | DAPK1, ELAVL2, SLC7A11, TBC1D4, YPEL1, ZNF217 |
| hsa-miR-29b-3p | up | 16 | AMOT, ATP1B1, ATP2B4, BCL11A, CCND2, COL4A5, DGKH, GRIP1, ISL1, KIRREL, NAV2, RAB12, ROBO1, TPM1, TRIB2, ZFP36L1 |
| hsa-miR-34c-5p | down | 2 | E2F5, GALNT7 |
| hsa-miR-486-5p | down | 1 | ELAVL2 |
Regulatory network of miRNAs and target genes
The miRNA-target genes regulatory network in OS was constructed with the miRNA-target gene pairs by Cytoscape software. As a result, 17 miRNAs and 109 differentially expressed genes formed 173 miRNA-target gene pairs with an inverse correlation of expression (Figure 1). Among all the differentially expressed miRNAs, miR-19b-3p had the most regulatory target genes (37 target genes) and miR-20a-5p and miR-124-3p targeted 26 and 22 differentially expressed genes. Additionally among the differentially expressed genes, CCND2 had most regulatory miRNAs (5 potential controlling miRNAs) and ZBTB4, TRPS1 and JAZF1 were regulated by 4 miRNAs. Those miRNAs targeting multiple genes and those genes targeted by multiple miRNAs, which demonstrated the nodal points of regulatory network, may play more significant roles in OS.
Figure 1.
Significantly enriched functional annotation of differently expressed miRNA target genes. A. The top 10 enriched GO categories for biological process; B. The significantly enriched KEGG pathway.
GO classification and KEGG pathways of miRNA target genes
GO functional and KEGG pathway enrichment analyses were performed for the 109 target gens. We found that regulation of transcription, DNA-dependent (GO: 0006355, P = 1.14E-03) and apoptotic process (GO: 0006915, P = 4.16E-02) were significantly enriched for biological processes. While for molecular functions, transcription factor activity (GO: 0003700, P = 4.46E-05) and transcription regulator activity (GO: 0030528, P = 4.63E-05) were significantly enriched, and for cellular component, cell junction (GO: 0030054, P = 1.85E-03) and cell projection (GO: 0042995, P = 2.28E-03) were significantly enriched (Table 3, Figure 2A).
Table 3.
GO functional annotation of differentially expression miRNA target genes (Top 15)
| GO ID | GO Term | Count | % | FDR |
|---|---|---|---|---|
| Biological process | ||||
| GO: 0006355 | regulation of transcription, DNA-dependent | 17 | 0.1574074 | 1.14E-03 |
| GO: 0006915 | apoptotic process | 6 | 0.0555556 | 4.16E-02 |
| GO: 0007165 | signal transduction | 10 | 0.0925926 | 3.05E-02 |
| GO: 0007155 | cell adhesion | 8 | 0.0740741 | 1.43E-02 |
| GO: 0060763 | mammary duct terminal end bud growth | 1 | 0.0092593 | 3.90E-02 |
| GO: 0007399 | nervous system development | 7 | 0.0648148 | 1.29E-02 |
| GO: 0015014 | heparan sulfate proteoglycan biosynthetic process, polysaccharide chain biosynthetic process | 1 | 0.0092593 | 3.90E-02 |
| GO: 0030318 | melanocyte differentiation | 2 | 0.0185185 | 3.28E-02 |
| GO: 0045944 | positive regulation of transcription from RNA polymerase II promoter | 10 | 0.0925926 | 1.28E-03 |
| GO: 0001755 | neural crest cell migration | 2 | 0.0185185 | 3.79E-02 |
| GO: 0007389 | pattern specification process | 3 | 0.0277778 | 3.05E-02 |
| GO: 0000122 | negative regulation of transcription from RNA polymerase II promoter | 9 | 0.0833333 | 1.05E-03 |
| GO: 0043065 | positive regulation of apoptotic process | 3 | 0.0277778 | 4.63E-02 |
| GO: 0030324 | lung development | 3 | 0.0277778 | 2.75E-02 |
| GO: 0007275 | multicellular organismal development | 14 | 0.1296296 | 3.34E-04 |
| Molecular function | ||||
| GO: 0003700 | transcription factor activity | 23 | 16.083916 | 4.46E-05 |
| GO: 0030528 | transcription regulator activity | 30 | 20.979021 | 4.63E-05 |
| GO: 0008134 | transcription factor binding | 13 | 9.0909091 | 2.09E-03 |
| GO: 0003682 | chromatin binding | 7 | 4.8951049 | 2.25E-03 |
| GO: 0043565 | sequence-specific DNA binding | 14 | 9.7902098 | 2.96E-03 |
| GO: 0042802 | identical protein binding | 14 | 9.7902098 | 4.64E-03 |
| GO: 0046982 | protein heterodimerization activity | 7 | 4.8951049 | 1.09E-02 |
| GO: 0016564 | transcription repressor activity | 8 | 5.5944056 | 2.32E-02 |
| GO: 0003779 | actin binding | 8 | 5.5944056 | 2.69E-02 |
| GO: 0005509 | calcium ion binding | 15 | 10.48951 | 3.44E-02 |
| GO: 0003677 | DNA binding | 30 | 20.979021 | 3.50E-02 |
| Cellular component | ||||
| GO: 0030054 | cell junction | 12 | 8.3916084 | 1.85E-03 |
| GO: 0042995 | cell projection | 14 | 9.7902098 | 2.28E-03 |
| GO: 0031252 | cell leading edge | 6 | 4.1958042 | 3.97E-03 |
| GO: 0001725 | stress fiber | 3 | 2.0979021 | 1.41E-02 |
| GO: 0005856 | cytoskeleton | 19 | 13.286713 | 1.51E-02 |
| GO: 0005886 | plasma membrane | 40 | 27.972028 | 1.57E-02 |
| GO: 0032432 | actin filament bundle | 3 | 2.0979021 | 1.65E-02 |
| GO: 0042641 | actomyosin | 3 | 2.0979021 | 1.77E-02 |
| GO: 0016323 | basolateral plasma membrane | 6 | 4.1958042 | 1.91E-02 |
| GO: 0005604 | basement membrane | 4 | 2.7972028 | 2.14E-02 |
| GO: 0044459 | plasma membrane part | 26 | 18.181818 | 2.17E-02 |
| GO: 0005667 | transcription factor complex | 6 | 4.1958042 | 2.18E-02 |
| GO: 0044451 | nucleoplasm part | 10 | 6.993007 | 2.49E-02 |
| GO: 0017053 | transcriptional repressor complex | 3 | 2.0979021 | 3.86E-02 |
| GO: 0005925 | focal adhesion | 4 | 2.7972028 | 4.24E-02 |
Figure 2.
Regulatory network between miRNAs and target genes in osteosarcoma. The diamonds and ellipses represent the miRNAs and genes, respectively. The red and green colors represent the relatively high and low expression, respectively. The larger geometric drawing indicates the more miRNAs or genes interacted with it.
We also performed the KEGG pathway enrichment analysis for differently expressed miRNA target genes. Hypergeometric test with P value < 0.05 were used as the criteria for pathway detection. The most significant pathway in our analysis was focal adhesion (P = 1.34E-04). Furthermore, axon guidance (P = 3.14E-03) and pathways in cancer (P = 1.28E-02) are also highly enriched (Table 4, Figure 2B).
Table 4.
KEGG pathway enrichment analysis of differential expression miRNA target genes (Top 15)
| KEGG ID | KEGG term | Count | FDR | Genes |
|---|---|---|---|---|
| hsa04510 | Focal adhesion | 9 | 1.34E-04 | TLN1, LAMA3, ACTN4, CCND2, BCL2, ACTN1, ITGA3, ITGB3, COL4A6 |
| hsa04360 | Axon guidance | 6 | 3.14E-03 | GNAI2, SEMA6D, ROBO1, PLXNA2, CFL2, SRGAP3 |
| hsa05200 | Pathways in cancer | 8 | 1.28E-02 | FGFR2, LAMA3, BCL2, MITF, RUNX1T1, ITGA3, GLI3, COL4A6 |
| hsa05412 | Arrhythmogenic right ventricular cardiomyopathy (ARVC) | 4 | 2.13E-02 | ACTN4, ACTN1, ITGA3, ITGB3 |
| hsa04810 | Regulation of actin cytoskeleton | 6 | 2.54E-02 | FGFR2, ACTN4, CFL2, ACTN1, ITGA3, ITGB3 |
| hsa05222 | Small cell lung cancer | 4 | 2.77E-02 | LAMA3, BCL2, ITGA3, COL4A6 |
| hsa04512 | ECM-receptor interaction | 4 | 2.77E-02 | LAMA3, ITGA3, ITGB3, COL4A6 |
| hsa04530 | Tight junction | 4 | 8.73E-02 | GNAI2, ACTN4, ACTN1, AMOTL1 |
Discussion
By mediating the expression of target genes, miRNA play a critical role in the regulation of cellular biology of development and cancer. Along with bioinformatics prediction, we integrated miRNA/mRNA expression data available to generate miRNA-mRNA regulatory networks. In the present study, 59 miRNAs were found to be significantly differentially expressed in the OS by integrating the acquired 2 data sets. The up-regulated miRNA with the lowest P-value was miR-9-3p, which has been found to regulate osteoblastic differentiation of mouse induced pluripotent stem (iPS) cells [22]. The expression of miR-9-3p altered in multiple cancers such as neuroblastoma, [23] colorectal cancer [24] and breast cancer [25], suggesting miR-9-3p was of potential importance in tumor formation and development. The down-regulated gene with the lowest P-value was miR-223-3p, which was significantly associated with a higher risk for progression of non-small cell lung carcinoma [26].
In addition, there are evidences showing that some genes implicated in the development of OS. Sun XH et al. discovered the altered expression of miR-646 in OS cell lines and OS tissues compared with normal osteoblast cell line. In vitro experiments showed that miR-646 regulated OS cells proliferation, migration, and invasion by targeting FGF2 [27]. miR-100-3p and miR-135b-5p were expressed differentially in OS cell lines and may be associated with the metastatic capacity of the disease [28]. Jones KB et al. found that miR-142-5p exhibit reduced expression in human OS tissues [10]. miR-486-5p was found to be down-regulated in OS cell lines relative to normal bone [29]. Shen L et al. identified a tumor-suppressive role of miR-217 in OS tumorigenesis through targeting WASF3 [30].
MiRNAs fulfil their regulatory function via targeting to corresponding genes, thus it is necessary to learn about target genes of miRNA to understand the biological functions of miRNAs. In this study we combined mRNA and miRNA expression data with bioinformatics predictions of miRNA targets via the miRWalk database to construct novel regulatory network between miRNAs and mRNAs. Consequently, 17 miRNAs and 109 genes formed 173 miRNA-target gene pairs with an inverse correlation of expression. miR-19b-3p was connected with the most regulatory target genes. miR-20a-5p and miR-124-3p regulated 26 and 22 target genes. Leung CM demonstrated that miR-19b-3p and miR-20a-5p, members of miR-17-92 cluster which has been determined to play an oncogenic role in tumorigenesis, exhibited differential responses to single-dose (SD) or multifractionated radiation in human breast cancer cells [31]. miR-124-3p exhibited altered expression in several kinds of cancers including glioma, oral squamous cell carcinomas, hepatocellular carcinoma and breast cancer [32-35], suggesting that its function is related to carcinogenesis.
Additionally in the miRNAs and mRNA regulatory network, CCND2 had most regulatory miRNAs including miR-124-3p, miR-17-5p, miR-19b-3p, miR-20a-5p, miR-29b-3p and the top 3 miRNA with the most regulatory target genes were contained. CCND2, located at chromosome 12p13, plays a key role in cell cycle G1/S transition by regulating phosphorylation of the tumor suppressor protein Rb [36]. DNA copy number alterations of CCND2 showed remarkable enhancement in OS metastatic lesion compared to a primary lesion by array comparative genomic hybridization analysis, leading to overexpression of CCND2 in OS [37,38]. CCND2 may be considered a therapy target for OS.
Finally, through GO and pathway analysis of putative targets of miRNA we found that some of the biological function may be cancer-related including regulation of transcription, DNA-dependent, apoptotic process, signal transduction and cell adhesion. The most significant pathway in our analysis was focal adhesion, which plays a fundamental role in carcinogenesis, tumor progression and metastasis. Aounts of focal adhesion molecules including integrins, integrin-associated proteins and growth factor were found to be deregulated in several kinds of cancer [39]. Many of the differently expressed target genes identified in this study were involved in pathways in cancer, including FGFR2, LAMA3, BCL2, MITF, RUNX1T1, ITGA3, GLI3 and COL4A6 as cancer suppressors or oncogenes.
In this study, differentially expressed miRNA were identified between OS and normal tissues by combining OS miRNA expression data sets available. Based on a published integrated study, 109 miRNA target genes found to be anticorrelated with miRNA expressions in OS. A novel miRNA-target gene regulatory network was constructed with the miRNA-target gene pairs. miR-19b-3p, miR-20a-5p, miR-124-3p and their common target CCND2, the nodal points of regulatory network, may play important roles in OS. Bioinformatics analysis of biological functions and pathways demonstrated that target genes of miRNAs are highly correlated with carcinogenesis. Our findings may help to understand the molecular mechanisms of OS and identify targets of effective targeted therapies for OS. Further functional experiments may provide additional insights into the role of the differentially regulated miRNAs in the development of OS.
Acknowledgements
The study was supported by the Natural Science Foundation of Jiangxi Province (no. 20122BAB205050) and a project from the Health and Family Planing Commission of Jiangxi Province (no. 20121091) and a project from the Department of Science & Technology of Jiangxi Province (no. 20133BBG70079).
Disclosure of conflict of interest
None.
References
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