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. 2020 Sep 4;3(6):e1286. doi: 10.1002/cnr2.1286

Identification of dysregulated miRNAs‐genes network in ovarian cancer: An integrative approach to uncover the molecular interactions and oncomechanisms

Sepideh Kadkhoda 1, Farzaneh Darbeheshti 1,2, Javad Tavakkoly‐Bazzaz 1,✉
PMCID: PMC7941472  PMID: 32886452

Abstract

Background

Ovarian (OV) cancer is considered as one of the most deadly malignancies in women, since it is unfortunately diagnosed in advanced stages. Nowadays, the importance of bioinformatics tools and their frequent usage in tracking dysregulated cancer‐related genes and pathways have been highlighted in researches.

Aim

The aim of this study is to investigate dysregulated miRNAs‐genes network and its function in OV tumors based on the integration of microarray data through a system biology approach.

Methods

Two microarray data (GSE119056 and GSE4122) were analyzed to explore the differentially expressed miRNAs (DEmiRs) and genes among OV tumors and normal tissues. Then, through the help of TargetScan, miRmap, and miRTarBase databases, the dysregulated miRNA‐gene network in OV tumors was constructed by Cytoscape. In the next step, co‐expression and protein‐protein interaction networks were made using GEPIA and STRING databases. Moreover, the functional analysis of the hub genes was done by DAVID, KEGG, and Enrichr databases. Eventually, the regulatory network of TF‐miRNA‐gene was constructed.

Results

The potential dysregulated miRNAs‐genes network in OV tumors has been constructed, including 109 differentially expressed genes (DEGs), 25 DEmiRs, and 213 interactions. Two down‐regulated microRNAs, miR‐660‐3p and hsa‐miR‐4510, have the most interactions with up‐expressed oncogenic DEGs. CDK1, PLK1, CCNB1, CCNA2, and EZH2 are involved in protein module, which show significant overexpression in OV tumors according to The Cancer Genome Atlas (TCGA) data. EZH2 shows amplification in OV tumors with remarkable percentage. The transcription factors TFAP2C and GATA4 have the pivotal regulatory functions in oncotranscriptomic profile of OV tumors.

Conclusion

In current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs‐DEGs and TF‐miRNA‐gene networks reveal the potential interactions that could be a significant piece of the OV onco‐puzzle.

Keywords: bioinformatics, microarray data, miRNA, ovarian cancer, systems biology

1. INTRODUCTION

Ovarian (OV) cancer shows the seventh most frequency among the malignancies in women. 1 In 2018, around the world, 295 414 new cases of this disease and 184 799 OV cancer‐related death approximately registered. 2 Unfortunately, this cancer is diagnosed very late, and this factor strongly decreases the lifetime of patients. However, early diagnosis and effective treatment strategies could notably improve the patient's lifetime. 3

Increasing evidence indicates that miRNAs, as a category of short noncoding RNAs, alongside coding RNAs play pivotal roles in cancer initiation and progression. It is well established that microRNAs are involved in complex posttranscriptional gene regulatory network in both roles of tumor suppressor or oncogenic miRNAs (oncomiRs). 4

The incredible improvements in high‐throughput technologies such as microarray and RNA‐seq as well as bioinformatics tools generate huge data concerning coding and noncoding transcripts in the field of cancer research. Although, interpretation and intersection of available data are main challenges in this context. 5 Despite the extensive studies about molecular mechanisms in pathogenesis of OV cancer, the molecular interactions of coding and noncoding transcripts in the complex gene regulatory network have not completely figured out.

The purpose of this study is the combination of microarray data and bioinformatics analyses for a better understanding of dysregulated key genes and miRNAs in OV cancer compared with normal tissues. In this study, we constructed dysregulated miRNAs‐genes, co‐expression, and protein‐protein interactions (PPIs) networks. The identified hub genes and their interactions could clarify underlying factors in OV cancer development and present efficient therapeutic targets.

2. METHODS

2.1. Differentially expressed genes and miRNAs (DEmiRs) identification

Two Gene Expression Omnibus (GEO) datasets including GSE119056 (GPL19615 platform) with six tumor and three normal samples as well as GSE4122 with 32 tumor and 14 normal specimens were selected and analyzed. The Benjamini and Hochberg false discovery rate (FDR) method was used to adjust the P‐value to obtain the multiple testing adjusted q‐value. All the data are normalized and cross‐comparable (Figure S1). For selecting differentially expressed genes (DEGs) in OV tumors compared with normal tissues, adj. P‐value < .05 and log FC ≥ |2| were set. Then the common genes between both GSEs that show the same expression direction (up‐ or down‐expression) were chosen for the next step.

GSE119056 (GPL21572 platform) for investigation of DEmiRs between OV cancer and nontumor OV tissues was analyzed. Similar to the prior step, adj. P‐value < .05 and log FC ≥ |2| criteria were considered.

2.2. DEG‐DEmiR interactions

In the next step, the bioinformatically predicted and experimentally validated DEG‐DEmiR interactions were obtained using TargetScan, 6 miRmap, 7 and miRTarBase 8 databases. It should be noted that the predicted interactions that exist in both TargetScan and miRmap were selected. Finally, the dysregulated miRNAs‐genes network was constructed including experimentally validated DEG‐DEmiR interactions based on miRTarBase database and predicted interactions by Cytoscape v3.6.1. 9

2.3. Co‐expression and protein‐protein interaction networks

The co‐expression network was visualized based on the top 20 genes that are co‐expressed with each of DEGs according to TCGA data; GEPIA web server was used to retrieve the co‐expressed genes of DEGs. 10 The PPI network including DEGs and their co‐expressed genes was constructed using String database. 11 The genes with the higher betweenness centrality and degree were determined as the most important genes in these networks (hub genes). Eventually, the functions of the hub genes were identified by DAVID 12 and Enrichr 13 databases.

To detect the main protein module in PPI network, MCODE app, 14 the Cytoscape plugin, was run and the pathway in which this protein complex is involved was detected by KEGG mapper. 15

2.4. Oncogenomic and oncotranscriptomic analysis

The genetic alterations of the PPI network hub genes in the 606 OV tumors were retrieved from cBioPortal database (TCGA provisional). 16 To compare expression of hub genes between OV tumors and adjacent normal tissues based on TCGA data as well as ovarian normal tissues based on the Genotype‐Tissue Expression (GTEx) data, GEPIA web server was used. The method for differential analysis is one‐way ANOVA; genes with |log2FC| >1 and q‐value <0.01 are considered DEGs.

2.5. Drug‐protein network

To investigate the potential of hub genes to becoming therapeutic targets, the drugs‐protein interactions were detected by Cytarget linker. 17

2.6. Identification of TF‐miRNA‐gene axes

We used HOCOMOCO v11 database (comprehensive collection of human transcription factor [TF]) to identify our DE genes that act as TF. 18 Then TransmiR v2.0 (an experimentally supported TF‐miRNA regulatory relationship database) was used to identify TF‐miRNA interactions. 19 TRRUST v2 (transcriptional regulatory relationships unraveled by sentence‐based text mining) 20 and RegNetwork (an integrated database of transcriptional and posttranscriptional regulatory networks in human and mouse) 21 databases were applied to assess the TF‐TF and TF‐gene interactions.

3. RESULTS

3.1. DEG‐DEmiR network

As it was mentioned before, DEGs in OV cancer samples compared with normal tissues were identified through analyzing two GSE 119056 and GSE4122. The number of common DEGs with adj. P‐value < .05 and log FC ≥ |2| is 109 which shows same direction of expression in two datasets (Table S1). Also, after analyzing of GSE119056, 25 DEmiRs with adj. P‐value < .05 and log FC ≥ |2| in OV cancer tissues were screened (Table S2).

To obtain dysregulated miRNA‐mRNA axes in OV tumors, three databases (TargetScan and miRmap for predicted interactions and miRTarBase for experimentally validated interactions) were applied. A total of 176 common predicted interactions in two mentioned databases and 46 validated interactions were identified. Taken together, 213 interactions have been detected (Table S3). Finally, DEG‐DEmiR network in OV tumors has been visualized by Cytoscape (Figure 1).

FIGURE 1.

FIGURE 1

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Dysregulated miRNA‐gene network. The cyan triangles and purple ellipses present miRNAs and genes, respectively. The potential molecular interactions between miRNAs and mRNAs are shown by lines

3.2. Co‐expression network

The 20 top co‐expressed genes of each DEGs were retrieved from GEPIA web server. Co‐expression network consists of DEGs along with their co‐expressed genes. ABCA8, ECM2, KIFC1, MAGI1, and NAV3 genes with the highest degree and betweenness centrality are selected as hub genes (Figure 2). The expression of these hubs in OV cancer based on TCGA and GTEx data shows that all of them are significantly dysregulated in tumors compared with normal tissues (Figure 3).

FIGURE 2.

FIGURE 2

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The co‐expression network including DEGs and their top 20 co‐expressed genes in OV cancer. The color and size of nodes indicate betweenness centrality and degree, respectively. Degree has a range from 1 to 26 for GSTM5 gene. The width of the edges has been adjusted according to PCC. The nodes with a higher size and stronger color are considered as hub genes. DEG, differently expressed gene; OV, ovarian; PCC, Pearson correlation coefficient

FIGURE 3.

FIGURE 3

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The boxplots of the expression changes of co‐expression network hub genes (ABCA8, ECM2, NAV3, CDK1, KIFC1, and MAGI1) in the OV tumor tissues compared with normal samples based on TCGA and GTEx data. The pink and yellow boxes show the tumor and nontumor tissues, respectively. These data have been obtained from GEPIA web server. Genes with |log2FC| > 1 and q‐values < 0.01 are considered differentially expressed genes. The red star shows a significant difference between the tumors and nontumor tissues. GTEx, Genotype‐Tissue Expression; OV, ovarian; TCGA, The Cancer Genome Atlas

3.3. Protein‐protein interaction network

In the next step, for preparing of PPI network, the STRING database was run. CDK1, PLK1, CCNB1, CCNA2, EZH2, and AURKA show the ability to be hub genes because of having a higher degree and betweenness centrality (Figure 4A). Then the box plots related to expression difference of these six hub genes between OV tumors and normal samples were analyzed by GEPIA database based on TCGA and GTEx data (Figure 5). All the hub genes show the significant expression differences between two sample groups. The genetic alterations of hub genes in 606 OV cancer samples were investigated by cBioPortal database. EZH2 and AURKA have a considerable mutation rate (9% and 11% of ovarian tumors, respectively), which most of them are gene amplification. Functional annotation according to Enrichr revealed the role of hub genes in cyclin‐dependent protein kinase activity and transition of the cell cycle from G2 to M phase (Figure 6).

FIGURE 4.

FIGURE 4

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A, Protein‐protein interaction network based on DEGs and their co‐expressed genes in OV tumors. The color and size of nodes refer to betweenness centrality and degree, respectively. Degree has a range from 1 to 114 for CDK1 gene. The width of edges was adjusted based on the combined scores. B, The main protein module of PPI according to MCODE app. DEG, differently expressed gene; OV, ovarian; PPI, protein‐protein interaction

FIGURE 5.

FIGURE 5

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The boxplots of expression of PPI network hub genes (AURKA, CCNA2, CCNB1, CDK1, PLK1, and EZH2) in OV tumors compared with normal samples. The pink and yellow boxes represent the tumor and normal tissues, respectively. These data obtained from GEPIA database based on TCGA and GTEx data. Genes with |log2FC| > 1 and q‐values < 0.01 are considered differentially expressed genes. The red star shows a significant difference between tumors and matched nontumor tissues (P‐value ≤ .05). GTEx, Genotype‐Tissue Expression; OV, ovarian; PPI, protein‐protein interaction; TCGA, The Cancer Genome Atlas

FIGURE 6.

FIGURE 6

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The bar graphs related to, A, GO‐biological processes and B, GO‐molecular functions of CDK1, PLK1, CCNB1, CCNA2, EZH2, and AURKA according to combined score. The graphs were obtained from Enrichr database. The longer and brighter bars show a more significant level. GO, gene ontology

For assessing the highly interconnected area in PPI network, MCODE, a Cytoscape app, was used, and the main protein module was created (Figure 4B). In addition, KEGG mapper shows the role of this module in the cell cycle (Figure 7).

FIGURE 7.

FIGURE 7

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The role of 15 genes of MCODE module in different steps of the cell cycle obtained from KEGG mapper

3.4. Drug‐protein network

The drug‐protein network has been constructed based on DrugBank by CyTargetLinker app. The genes as CDK1, PLK1, CCNA2, and AURKA in this network are influenced by different kinase inhibitors (Figure 8).

FIGURE 8.

FIGURE 8

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The drug‐protein network. The gray and purple ellipses show the genes and drugs, respectively. Data have been visualized by Cytarget linker app

3.5. Dysregulated TF‐miRNA‐gene network

Among 74 DE genes in miRNA‐gene network, 11 genes (VDR, HOXB2, GATA4, FOSB, GATA6, KLF4, HLF, FOXA2, SOX9, HMGA1, and TFAP2C) were identified as TFs by HOCOMOCO database. Twenty‐one validated interactions between TFs and miRNAs have been uncovered by running TransmiR database (Table S6). Also, 21 interactions between TF‐TF and TF‐gene have been detected by TRUST and RegNetwork databases (Table S6). Eventually, dysregulated TF‐miRNA‐gene network in OV tumors has been visualized by Cytoscape (Figure 9).

FIGURE 9.

FIGURE 9

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A, The dysregulated TF‐miRNA‐gene network in ovarian cancer. The nodes in green, yellow, and pink are TF, miRNAs, and genes, respectively. The blue edges show validated interactions between TFs and their targets, and the black dash lines represent predicted ones. The red edges show validated interactions between miRNAs and their targets. The Inline graphic sign represents activating interaction. The Inline graphic and Inline graphic demonstrate repressing and unknown interactions, respectively. The size of the nodes is adjusted according to the degree. The black borders of nodes represent up‐expression, but nodes without border demonstrate down‐expression. B, General overview of the regulatory relationships between TF, miRNA, and gene. C, Some extracted feedback and feedforward loops from TF‐miRNA‐gene network. TF, transcription factor

4. DISCUSSION

Among the cancer‐related deaths in women, OV cancer is ranked fifth. The contribution of genetic factors and family history to this cancer is well known. 22 Nowadays, the importance of noncoding RNAs in various malignancies is becoming more and more defined. The role of miRNAs as a tumor suppressors or oncogenes has been ascertained in tumor initiation and progression. 23 Despite their short length (up to 24 nucleotides), they are involved in the regulation of many genes expression. 24 In this study, through analyzing two microarray data (GSE119056 and GSE4122), 109 common DEGs in OV tumors compared with normal tissues were obtained with adj. P‐value < .05 and log FC ≥ |2|. Moreover, according to GSE119056, 25 DEmiRs with adj. P‐value < .05 and log FC ≥ |2| were detected in OV cancer. The DEGs‐DEmiRs network in OV tumors has been constructed through an integrative approach. This network consists of 213 mRNA‐miRNA axes.

The DEGs‐DEmiRs network uncovers the potential tumor suppressor miRNAs that have not been reported in OV cancer so far. Two down‐regulated miRNAs named miR‐660‐3p and hsa‐miR‐4510 show the most interactions with DEGs. Both of them in our study could target some studied oncogenes in other studies including ADAM28, 25 HMGA1 26 and SLC34A2. 27

According to Shyian et al report, the relationship between expression of SLC34A2 and differentiation level of OV tumor cells was confirmed. So, it can be used as a possible marker in determination of cell differentiation. 28 Also, the direct link of HMGA1 expression with OV tumor grade and proliferation rate in related cell lines was shown by several techniques. 29 Furthermore, miR‐660‐3p is probably involved in miRNA/FOXA2 and SOX9 axes. Our analyses indicate the significant up‐expression of these two oncogenes 30 , 31 in OV tumors. It is suggested that down‐expressed miR‐660‐3p in OV tumors results in overexpression of FOXA2 and SOX9 in malignant cells. The role of FOXA2 in loss of differentiation and regeneration of OV tumors is well known. 32 It has been indicated that SOX9 could act as a TF for some aggressive markers in OV tumors such as TUBB3 through EPAS1/Hif‐2α/SOX9/TUBB3 axis. 33

Another interesting axis, which has been detected by our exploration, is miR‐4510/BIRC5. BIRC5 (Survivin) is a member of the apoptosis inhibitory gene family and prevents apoptotic cell death. The oncogenic role of BIRC5 has been well documented in different malignancies. 34 Our data indicate the significant up‐expression of BIRC5 in OV tumors and suggest that it is a target for miR‐4510. Although, theses interactions should be functionally proven in OV tumor cells. In 2018, it has been shown that BIRC5 down‐regulation results in metastasis inhibition of OV tumor cells. This investigation introduces the regulatory miR‐203/BIRC5 axis in OV cancer. 35

The summary of previous studies concerning dysregulation of DEmiRs that have been already reported in OV shows in Table 1. Microarray data of DEmiRs expression are almost consistent with previous reports except miRNA‐328‐3p.

TABLE 1.

Summary of dysregulated miRNAs in different studies related to OV cancer

Transcript Study Dysregulation Methods
miRNA‐328‐3p Wang et al 36 Up qRT‐PCR
Srivastava et al 37 Up qPCR, microarray analysis, luciferase reporter assay, immunoblotting, ALDH analyses, sphere‐forming assay, xenograft tumor study
miRNA‐21‐3p Báez‐Vega et al 38 Up qPCR, colony formation assays, in vitro invasion assay, cell viability assay, western blot analysis, luciferase assays, immunohistochemistry
miRNA‐133p Liu et al 39 Down RT‐PCR, cell viability assay, cell proliferation assay, transwell invasion assay, western blot analysis, luciferase reporter assay
Yang et al 40 Down RT‐PCR, western blot assay, invasive and migration assays
miRNA‐1294 Guo et al 41 Down qRT‐PCR, cell counting kit‐8 assay, cell cycle analysis
Zhang et al 42 Down qRT‐PCR, MTT assays, wound healing, tumor invasion assays, western blot
miRNA‐383‐5p Jiang et al 43 Down qRT‐PCR, western blot assay, IHC, dual‐luciferase reporter assay, cell proliferation assay, Edu incorporation assay, animal models
miRNA‐532‐3p Huang et al 44 Down RT‐qPCR, cell proliferation assay, colony formation assay, EdU incorporation assay, transwell assay, scratch wound assay, luciferase reporter gene assay, RIP assay
miRNA‐500b‐3p Pandey et al 45 Down qRT‐PCR
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Abbreviations: ALDH, aldehyde dehydrogenase; IHC, immunohistochemistry; OV, ovarian; qPCR, quantitative PCR; RIP, RNA immunoprecipitation; RT‐PCR, real‐time polymerase chain reaction.

The co‐expression network, including 109 DEGs and their top 20 co‐expressed genes in OV tumors, indicates that ABCA8, KIFC1, ECM2, NAV3, and MAGI1 genes are most important genes in oncotranscriptomic profile. The role of these genes in pathogenesis of different cancers has been proven. ATP‐binding cassette subfamily A member 8 (ABCA8) in accordance to DAVID database is a component of plasma and mitochondrial inner membranes and participates in drug transmembrane, lipid, and xenobiotic transports (Table S4). Our in silico analysis revealed that ABCA8 is down‐regulated in OV cancer (Figure 3). Previous studies revealed ATP‐binding cassette (ABC) transporters have pivotal roles in different cancers and drug resistance. Dysregulation of ABCA8 has been documented in OV, 46 , 47 breast, and prostate cancer 48 as well as esophageal squamous cell carcinoma 49 and breast cancer. 50 Januchowski et al demonstrated that ABCA8 is down‐regulated in several drug‐resistant OV cancer cell lines. 51

Kinesin family member C1 (KIFC1) plays an indispensable performance in mitotic spindle assembly, mitotic sister chromatid segregation, and cell division (Table S4). According to previous reports, KIFC1 in people with OV cancer is overexpressed and significantly related to advanced stage and grade of tumors and patient's poor survival. 52 , 53 Also, the oncogenic role of KIFC1 in breast cancer was clarified. The suppression of this gene by poly ADP‐ribose polymerase in breast cancer cell lines and decreasing of their livability is the evidence of this claim. 54 These findings and our analyses indicate the remarkable roles of KIFC1 in oncotranscriptomic profile of ovarian cancer and its value to becoming therapeutic target. Interestingly, Zhang et al introduced an inhibitor of KIFC1 for the interdicting of its role in centrosome clustering in malignant cells that this finding accelerates the targeted cancer cell death without normal cell affection. 55

According to DAVID, the extracellular matrix protein 2 (ECM2) participates in positive regulation of cell‐matrix adhesion, axonogenesis, and extracellular matrix organization (Table S4). The high expression of ECM2 in normal OV epithelium and its down‐expression in OV tumors have been identified through gene expression profiling. 56 Result of our analysis according to TCGA and GTEx data is consistent with previous reports.

Another hub gene in the co‐expression network is neuron navigator 3 (NAV3). Its suppression in the OV cancer cell lines by miRNA‐21‐3p results in cisplatin resistance. 57 Moreover, in another study, NAV3 was identified as a target of miRNA‐429. In OV cell lines, which are transfected by miRNA‐429, NAV3 showed down‐expression and the metastatic ability of tumors reversed. 58 Our results show that this gene is significantly down‐regulated in OV cancers. Furthermore, in colon and squamous cell cancers, NAV3 copy number variations (deletion and amplification) are a common phenomenon and affects cell invasion. 59 , 60

Membrane‐associated guanylate kinase (MAGI1) plays important roles in protein complex assembly, cell adhesion, and cell surface receptor signaling pathway (Table S4). This gene shows the significant overexpression in OV tumors according to TCGA data. However, Kori et al identified MAGI1 as one of down‐expressed DEGs in their microarray findings in OV cancer samples. 61

Taken together, in oncotranscriptomic profile of OV tumors, two down‐expressed hub genes including ABCA8 and NAV3 have the key role in drug‐resistant tumors and should be monitored as long as cancer therapy is concerned.

In current study, the PPI network has been constructed which consists of DEGs in two GEO datasets and their co‐expressed genes. In this network, CDK1, PLK1, CCNB1, CCNA2, EZH2, and AURKA are considered as hub genes. All of these genes have a higher expression in OV tumors relative to normal tissues according to TCGA and GTEx data. The common targets that are present in both DEG PPI and miRNA networks were collected in Table S5. In 2017, Zhang et al showed CDK1 inhibition results in repression of cell proliferation of OV cancer cell lines. 62 Moreover, it was demonstrated that CDK1 is a target of miRNA‐490‐3p. This miRNA plays a tumor suppressive role in OV cancer cell line through inhibiting CDK1. 63 Another hub gene in the PPI network is PLK1 protein. When PLK1 is suppressed, OV cell propagation and proliferation are decreased. 64 In addition, the roles of PLK1 in the elevation of autophagy and drug resistance were elucidated in ovarian clear cell carcinoma. 65 According to Jiang et al study, the attenuation of FOXM1 and PLK1 results in reinforcing P21 amount and finally OV cell apoptosis. 66 PLK1 and CCNB1 have been introduced as downstream targets of FOXM1 TF, and all of them are overexpressed in OV tumors. 67 Both of the CCNB1 and CCNA2 are members of cyclin family genes and promote transition through G1/S and G2/M in the cell cycle. CCNA2 shows high expression in OV tumors and association with insensitivity to chemotherapy. 68 AURKA gene product is a protein kinase that shows to be involved in microtubule formation during chromosome segregation. The role of AURKA gene in the migration of OV cancer cells has been uncovered. 69 The EZH2 gene codes a histone methyltransferase which participates in histone methylation and consequently in transcriptional activity. Gain‐of‐function mutations and overexpression of EZH2 have been linked to many forms of cancer. 70 , 71

It is shown that one reason of resistance to cisplatin in OV cancer patients is activation of the cMyc/miRNA‐137/EZH2 axis. In normal cells, miR‐137 suppresses EZH2, but cMyc by trying to neutralize this inhibition reinforces the ability of resistance in malignant cells. 72 Interestingly, our investigation concerning EZH2 genetic alterations in OV tumors reveals an amplification in remarkable percentage of patients (11%). These data could justify EZH2 overexpression in a portion of OV cancer patients. Moreover, the detected protein module contains CDK1, PLK1, CCNB1, CCNA2, and EZH2. We show that this module is involved in the progression of different phases of the cell cycle. All the evidence considered, we demonstrated that CDK1, PLK1, CCNB1, CCNA2, AURKA, and EZH2 not only are hub nodes in PPI network but also are significantly overexpressed in OV tumors and could function as oncogenes. These proteins might become the efficient therapeutic targets in treatment‐resistant OV tumors.

Alisertib (MLN8237), AT9283, and CYC116 in our protein‐drug network classified in kinase inhibitors category. These anticancer compounds are able to inhibit AURKA protein. On the other hand, cyclin‐dependent kinases (CDK1) could be inhibited by Flavopiridol, Alsterpaullone, SU9516, AT7519, Olomoucine, Hymenialdisine, and indirubine‐3′‐monoxime.

In this study, we conducted a novel regulatory network of TF‐miRNA‐gene interactions in OV tumors. It reveals the role of regulatory feedback and feedforward loops in great complexity of OV tumors. This network indicates that TFs TFAP2C and GATA4 have the most interactions and are involved in feedback loops with miR‐26b. According to Cai et al report, GATA4 has lost its expression in ovarian tissues during cellular transformation and progression to malignancy. 73 In our study, for the first time, TFAP2C has been identified to be an important TF in ovarian cancer and might become both biomarker and therapeutic target. Interestingly, the expression level of miR‐26b in patients with ovarian cancer shows a significant reduction and correlates with the stage of tumors and patient's life expectancy. 74

In conclusion, in current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs‐DEGs and dysregulated TF‐miRNA‐gene network reveal the potential interactions that could be significant pieces of OV tumors onco‐puzzle.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

AUTHOR CONTRIBUTIONS

All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Conceptualization, S.K.; Software, S.K., F.D.; Resources, S.K., F.D.; Writing ‐ Original Draft, S.K.; Writing ‐ Review & Editing, F.D.; Supervision, J.T.‐B.; Project Administration, J.T.‐B.

ETHICAL STATMENT

Not applicable.

Supporting information

Figure S1. Boxplots of normalized data.

Click here for additional data file. (893.5KB, docx)

Table S1. The common DEGs in two datasets (GSE 119056 and GSE4122) with adj. P.value <0.05 and log FC ≥|2| which show same direction of expression.

Table S2. The red lines show DEmiRs with adj. P.value <0.05 and log FC ≥|2| in OV cancer tissues.

Table S3. Interactions of differential expressed (DE) miRNAs with DE mRNAs. The stars show applied databases for each interaction.

Table S4. Functional annotation of hub genes in co‐expression network by DAVID database.

Table S5. The common genes DEG PPI network and miRNA network.

Table S6. Dysregulated TF‐miR‐gene interations in ovarian tumors.

Click here for additional data file. (32.1KB, xlsx)

ACKNOWLEDGMENTS

We would like to thank the GEO, GEPIA, STRING, Enrichr, DAVID, cBioPortal, TargetScan, miRmap, KEGG mapper, miRTarBase, HOCOMOCO, TRUST, TransmiR, and RegNetwork databases, as well as Cytoscape software for free availability.

Kadkhoda S, Darbeheshti F, Tavakkoly‐Bazzaz J. Identification of dysregulated miRNAs‐genes network in ovarian cancer: An integrative approach to uncover the molecular interactions and oncomechanisms. Cancer Reports. 2020;3:e1286. 10.1002/cnr2.1286

DATA AVAILABILITY STATEMENT

The required data are provided in the supplementary tables.

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Associated Data

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

Supplementary Materials

Figure S1. Boxplots of normalized data.

Click here for additional data file. (893.5KB, docx)

Table S1. The common DEGs in two datasets (GSE 119056 and GSE4122) with adj. P.value <0.05 and log FC ≥|2| which show same direction of expression.

Table S2. The red lines show DEmiRs with adj. P.value <0.05 and log FC ≥|2| in OV cancer tissues.

Table S3. Interactions of differential expressed (DE) miRNAs with DE mRNAs. The stars show applied databases for each interaction.

Table S4. Functional annotation of hub genes in co‐expression network by DAVID database.

Table S5. The common genes DEG PPI network and miRNA network.

Table S6. Dysregulated TF‐miR‐gene interations in ovarian tumors.

Click here for additional data file. (32.1KB, xlsx)

Data Availability Statement

The required data are provided in the supplementary tables.

Articles from Cancer Reports are provided here courtesy of Wiley

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