Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2018.8961 mmr-18-01-0230 Articles A gene interaction network-based method to measure the common and heterogeneous mechanisms of gynecological cancer WangMingyuan LiLiping LiuJinglan WangJinjin The Affiliated Zhuzhou Hospital Xiangya Medical College CSU, Zhuzhou, Hunan 412000, P.R. China Correspondence to: Dr Jinjin Wang, The Affiliated Zhuzhou Hospital Xiangya Medical College CSU, 116 Changjiang South Road, Zhuzhou, Hunan 412000, P.R. China, E-mail: wangjinjin412000@163.com 072018 03052018 18 1 230 242 26112017 01032018 Copyright: © Wang et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Gynecological malignancies are a leading cause of mortality in the female population. The present study intended to identify the association between three severe types of gynecological cancer, specifically ovarian cancer, cervical cancer and endometrial cancer, and to identify the connective driver genes, microRNAs (miRNAs) and biological processes associated with these types of gynecological cancer. In the present study, individual driver genes for each type of cancer were identified using integrated analysis of multiple microarray data. Gene Ontology (GO) has been used widely in functional annotation and enrichment analysis. In the present study, GO enrichment analysis revealed a number of common biological processes involved in gynecological cancer, including ‘cell cycle’ and ‘regulation of macromolecule metabolism’. Kyoto Encyclopedia of Genes and Genomes pathway analysis is a resource for understanding the high-level functions and utilities of a biological system from molecular-level information. In the present study, the most common pathway was ‘cell cycle’. A protein-protein interaction network was constructed to identify a hub of connective genes, including minichromosome maintenance complex component 2 (MCM2), matrix metalloproteinase 2 (MMP2), collagen type I α1 chain (COL1A1) and Jun proto-oncogene AP-1 transcription factor subunit (JUN). Survival analysis revealed that the expression of MCM2, MMP2, COL1A1 and JUN was associated with the prognosis of the aforementioned gynecological cancer types. By constructing an miRNA-driver gene network, let-7 targeted the majority of the driver genes. In conclusion, the present study demonstrated a connection model across three types of gynecological cancer, which was useful in identifying potential diagnostic markers and novel therapeutic targets, in addition to in aiding the prediction of the development of cancer as it progresses.

 gynecological cancer driver gene connection model bioinformatics Introduction

Gynecological malignancies, particularly ovarian cancer, cervical cancer and endometrial cancer, are serious medical conditions in women and have been leading causes of cancer mortality in recent years. However, the use of cancer markers for early and progressive detection remain lacking (1). In addition, research has demonstrated that there are close associations across the three aforementioned types of cancer. It has been demonstrated that the progress and the development of the three aforementioned types of cancer are similar, which may be useful when diagnosing any one of these three cancer types. In the case of endometrial cancer, prior to the development of endometrial carcinoma, the endometrium undergoes progressive neoplastic alterations in a parallel fashion to the premalignant alterations observed in the cervix prior to the development of cervical carcinoma (2). The rationale of oophorectomy in surgical management is that endometrial cancer may metastasize to the ovary, in which women with endometrial cancer are at risk for synchronous and metachronous ovarian cancer, and the source of estrogen may be eliminated by oophorectomy (3,4). In cancer cells, oncogenic transformation is associated with major alterations in gene expression (5). With the advent of large-scale screening of cancer genomes, hundreds of genes with alterations in different types of tumors from patients with cancer have been identified (6–10), which revealed that cancer is a complex disease caused by genetic alterations in multiple genes (11,12). In order to elucidate the cancer marker genes and biological processes associated with each type of gynecological tumor, and the potential underlying mechanism of associations among gynecological tumors, the contribution of identified differentially expressed genes (DEGs) to the pathogenesis of gynecological tumors must be understood.

To analyze different DEGs, high-throughput experimental methods, including microarray analysis, have been widely used in a number of studies (13,14). A vast quantity of microarray data has been produced and deposited in publicly-available data repositories, including the Gene Expression Omnibus (GEO) (15). With the methods of integrated bioinformatics analysis, researchers have been able to advance the identification of genetic signatures. This may provide insights into the underlying biological mechanisms of the development of gynecological tumors.

Chung et al (16) revealed that microRNA (miRNA)-200b/a is a direct transcriptional target of grainyhead like transcription factor 2, which is associated with development and overall survival in epithelial ovarian cancer. Halabi et al (17) demonstrated that 41 genes, including matrix metalloproteinase (MMP)7 and tumor protein 53, were involved in the potential underlying mechanisms of ovarian cancer. Espinosa et al (18) revealed that six genes encoding cyclin B2, cell-division cycle protein 20, protein regulator of cytokinesis 1, synaptonemal complex protein 2, nucleolar and spindle associated protein 1 and cyclin-dependent kinase inhibitor 2 belonging to the mitosis pathway, were potential markers for screening or therapeutic targets of cervical cancer. However, biomarkers which were identified in this way have had poor translation into actual clinical practices. Results have been non-concordant among studies due to small sample sizes. In addition, the studies into the associations of biomarker genes (driver genes) remain lacking among the different types of gynecological tumors.

A robust driver gene biomarker signature may be beneficial for the diagnosis and targeted treatment of gynecological tumors. In the present study, in order to identify a driver gene biomarker signature for the three types of gynecological tumors, data from the Metabolic Gene Rapid Visualizer database (MERAV, which is derived from GEO) was used (19). In MERAV, microarrays were normalized together to eliminate systematic errors caused by different batch experiments.

The present study devised a target network for ovarian cancer, cervical cancer and endometrial cancer using the selected driver genes, and further investigated the identified DEGs via functional enrichment analysis, pathway enrichment analysis and protein-protein interaction (PPI) networks. In addition, the present study extracted clinical information of ovarian cancer, cervical cancer and endometrial cancer from The Cancer Genome Atlas (TCGA) data portal. Subsequently, driver genes in each type of cancer were analyzed. It was important to investigate the underlying mechanism of each gynecological tumor and whether the identified driver genes contributed to these diseases. Subsequently, a network was generated between the miRNAs and the identified driver genes, using the method of mining the Mir2 disease and Tarbase databases which provide information on miRNAs, diseases and the interactions between miRNAs and genes. Finally, the present study determined hub-genes and hub-miRNAs across the gynecological tumors to study the potential underlying mechanisms of the developments of gynecological tumors, which may shed light on different strategies for the design of biological targets for cancer therapies.

 Materials and methods <sec> <title>Identification of gene expression datasets

In the present study, DEGs were identified between normal tissues and tumors extracted from the MERAV database from the National Center for Biotechnology Information GEO database (MERAV, http://merav.wi.mit.edu). The experimental samples for the present study are presented in Tables I and II. The following information was extracted from each identified study: GEO accession number, sample type, number of cases and controls, and gene expression data. Studies in which the microarray data were uncertain were excluded. The experimental protocol for the present study is presented in Fig. 1.

 Integrated analysis of DEGs identified in the extracted databases

Information was extracted from the microarray datasets in MERAV which are presented in Tables I and II, respectively. Following the intersection of the microarray datasets, the DEGs were established between the normal and cancer tissues. In the present study, the degree of differential gene expression was measured by fold-change based on the Student's t-test. A fold-change value >2 or <0.5 and t-test P<0.01 for a gene was considered to be significant. The differential expression analysis was conducted using the Linear Models for Microarray Data package in R (20).

 Protein interaction network

The DEGs were subsequently applied to the Human Protein Reference Database (21) (HPRD, www.hprd.org), to identify the more complex functional interactive driver genes of separate cancer types. Genes with interactions with each other were extracted from the DEGs as mentioned above (presented in Tables III–X). The PPI network is a useful research tool for investigating the cellular networks of protein interactions, and was downloaded from the HPRD. Cancer-associated gene-gene interaction networks were constructed by mapping the DEGs into the HPRD PPI network for each cancer (cervix tumor, ovarian tumor and endometrium tumor). To make it easier to identify the driver genes, the present study calculated the lines attached to each node, which was defined as the degree of the node. The nodes that exhibited degrees ≥4 were defined as driver genes. The nodes whose degree was ≥4 were considered to serve more complex roles in the development of the diseases of interest. These nodes were then extracted for the PPI network (Fig. 2). The present study constructed a connected network which contained the driver genes across the three cancer types. Through this method, it was determined whether the driver genes of the separate cancer types had any interaction with each other. The networks were constructed using Cytoscape version 3.3.0 (www.cytoscape.org).

 miRNAs regulating gene network construction

The present study analyzed the association between miRNAs and the identified driver genes (Fig. 3). This process was performed by extracting a list of miRNAs which were associated with the type of cancer (cervical tumor, ovarian tumor or endometrial tumor) from the Mir2 Disease database (www.mir2disease.org) (22). Following this step, a network was created regarding the regulatory associations between the miRNA and the specific driver gene of each type of cancer in order to identify the hub-miRNAs of the gynecological tumors. The associations of the regulation were extracted from Tarbase (diana.cslab.ece.ntua.gr/tarbase) (23).

 Functional and pathway enrichment analysis

In order to assess the functional relevance of the aforementioned DEGs, a pathway analysis was created based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) (24). DAVID provides a useful tool to analyze large gene lists, including gene ontology (GO) and pathway analysis. DEGs in different diseases were applied to this database in order to detect potentially represented functions. GO-categories were organized based on the GO database (25) (www.geneontology.org). In addition, pathway analysis was based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (26) (genome.jp/kegg). Significant categories were identified by expression analysis systematic explorer scores, a modified Fisher's exact P-value. The threshold for significance for a category was considered to be P<0.01, with >4 genes for the corresponding term.

 Survival analysis

The present study used TCGA database to extract clinical information and gene expression profile information. At the start of the analysis, the expression values of each driver gene were listed, which were identified via the PPI network. To find the median level of gene expression, the samples were divided into two groups by median of expression (high expression group and low expression group). Additionally, the corresponding clinical information of each sample was extracted. Survival data representing time between initial diagnosis and mortality were downloaded directly from TCGA data portal (tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp) (27). With this information, the present study was able to estimate the association between the identified driver genes of the three types of cancer mentioned above and the survival rates of patients. All analyses were conducted using custom-written code in R (www.r-project.org).

 Results <sec> <title>Integrated analysis of multiple studies to establish the driver genes in cancer

There are multiple genes that contribute to the cause of the aforementioned cancer types and, therefore, no single gene is a determining factor in diagnosis. It was identified that each type of cancer was driven by different variations of genes that serve key roles during the development of pathology. However, no single gene may explain the heterogeneity of each type of cancer. In the case of cervical cancer, 186 genes in squamous cell carcinoma of the cervix (Table III), 107 genes in keratinized squamous cell carcinoma of the cervix (Table IV), 96 genes in cervical adenocarcinoma Grade 3 (Table V), 133 genes in non-keratinized squamous cell carcinoma of the cervix (Table VI) and 203 genes in cervical adenocarcinoma Grade 2 (Table VII) were identified to be important. In addition, 120 genes and 76 genes were established, respectively, in adenocarcinoma of the ovary Grade 2 and Grade 3 (Tables VIII and IX). A total of 168 genes were established in endometrial carcinoma (Table X).

 Integrated PPI (protein-protein interactions) network construction

Based on the HPRD, the interaction network of the identified driver genes was constructed, which consisted of 101 nodes (genes that form associations) and 185 edges (biological association) (Fig. 2). Genes with a higher degree of association (degree ≥4) were observed to be larger in size, and included the genes CDK1, CAV1, ZBTB16, Jun proto-oncogene AP-1 transcription factor subunit (JUN), RAF1, RB1, minichromosome maintenance complex component 2 (MCM2), AR, ABL1, LMNA, FLNA, DCN, FYN, SMAD1, LRP1, PSEN1, EP300, CTNNB1, collagen type I α1 chain (COL1A1) and FOS. Through this method, it was identified that driver genes in each gynecological cancer have contact interactions.

 Comprehensive analysis of miRNA regulation and the selected driver genes

Fig. 3 illustrates that certain miRNAs serve important roles in regulating the driver genes. In the present study, it was demonstrated that a number of miRNAs regulate separate networks [for example the let7 family, miRNA (miR)-23b, miR-21, miR-214 and miR-218]. miRNAs that were confirmed to be significant in cervical cancer, including let7c and let7b, are also found to be associated with the other two cancers in this study. This information may be important in establishing the connections between the three gynecological cancer types, which may be used in the development of targets for further research and diagnosis.

 Functional and pathway enrichment analysis

GO analysis revealed that the identified genes of cervical tumors, ovarian tumors and endometrial tumors were predominantly involved in the illustrated biological processes (Fig. 4). The top three significant biological processes of cervical cancer were ‘mitotic cell cycle’, ‘cell cycle’ and ‘cell cycle process’, while for ovarian cancer, the biological processes consisted of ‘cell cycle process’, ‘cell cycle phase’ and ‘macromolecule metabolic process’. For the progression of endometrial cancer, the top three biological processes observed to be at fault for cancer progression were ‘response to organic substance’, ‘regulation of cell proliferation’ and ‘skeletal system development’.

Using the method of pathway analysis, it was revealed that genes in cervical cancer were significantly enriched in ‘cell cycle’, ‘pathways in cancer’ and ‘DNA replication’. Ovarian cancer was observed to be significantly enriched in ‘MAPK signaling pathway’, ‘cell cycle’ and ‘oocyte maturation’. Endometrial cancer was observed to be significantly enriched in ‘pathways in cancer’, ‘focal adhesion’ and ‘complement and coagulation cascades’ (Fig. 5).

 Survival analysis of patients with gynecological tumor

Fig. 6 illustrates the association between survival time and survival rate in the high and low expression groups. The genes MCM2, MMP2, COL1A1 and JUN are presented in the figure, and it was observed that the driver genes of the expression groups were able to divide each of the target cancer types into two groups, one of which contained the high expression group with the other containing the low expression group. Therefore, in order to determine whether the driver genes had a key role in the development of gynecological tumors and the connective function of separate cancer types, the present study aimed to identify the association between the target cancer driver genes and other types of gynecological cancer.

 Discussion

The principal challenge of high-throughput cancer genomics is to identify specific driver genes and the underlying mechanisms of carcinogenesis, apart from the vast quantity of heterogeneous genomic alteration data. Numerous studies have focused on identifying individual functional modules or pathways involved in cancer (28–30). Based on this methodology, the analysis of the present study focused specifically on DEGs in order to reveal the transcriptional responses of gynecological tumors. The results of this analysis suggested that the common biological processes of cancer of the cervix, ovary and endometrium were those involved in the cell cycle and the regulation of macromolecule metabolism.

The cell cycle is the progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication. Research has shown that interference with cell cycle components may lead to tumor formation (31). Certain cell cycle inhibitors, including retinoblastoma protein and tumor protein 53 may mutate during replication, causing the cell to proliferate uncontrollably, ultimately resulting in a tumor. Furthermore, the proportion of active cell division in tumors is much higher compared with the rate in normal tissue.

To clarify the hub genes in ovarian cancer, cervical cancer and endometrial cancer, DEGs were predicted to be biomarkers for each cancer using PPI networks. It is considered that hub nodes are genes that are highly connected with other genes and have been predicted to serve key roles in numerous networks. In addition, highly connected hub genes were proposed to have a considerable role in biological development. Hub nodes have more complex interactions compared with those of other nodes, which indicates that they have pivotal roles in the underlying mechanisms of disease. In addition, certain identified biomarkers of each type of cancer were extracted from each network and these driver genes were placed into one PPI network with the duplication hub genes eliminated. Therefore, the particular hub genes of each gynecological cancer and the connection nodes across the three types of cancers may be identified. Accordingly, the identification of hub genes and hub connected genes involved in each gynecological cancer may lead to the discovery of the association across ovarian cancer, cervical cancer and endometrial cancer, and may lead to the development of effective diagnostic and therapeutic approaches.

In order to ascertain a causal association across the three types of gynecological cancer, the present study extracted clinical information and gene expression profile information from TCGA database, and used the hub connected genes identified in the PPI network to perform survival analysis. In the present study, four noteworthy genes were identified, including MCM2, MMP2, COL1A1 and JUN.

The present study demonstrated that MCM2 may serve a key role in cervical cancer. A poor prognosis was associated with lower expression. Furthermore, MCM2 was highly connected with ovarian cancer and endometrial cancer. The results suggested that MCM2 is a component of the DNA replication licensing complex, with a rich binding surface that directs multiple regulatory interactions of cancer significance, marking DNA replication origins during the G1 phase of the cell cycle for use in the subsequent S-phase. A deficiency of MCM2 results in death or morbidity in the absence of an overt tumor (32). These processes of DNA replication have been studied and used as therapeutic targets. Simon and Schwacha (33) suggested that MCM2 was a promising target for blocking the proliferation of cancerous and precancerous cells.

In the present study, MMP2 was identified to be essential in causing cervical cancer. MMPs are zinc-containing endopeptidases with an extensive range of substrate specificities. These enzymes are able to degrade various components of extracellular matrix (ECM) proteins. In photocarcinogenesis, degradation of the ECM is the initial step towards tumor cell invasion, to intrude in the basement membrane and the surrounding stroma that primarily comprises fibrillary collagens. Additionally, MMP2 is involved in angiogenesis, which promotes cancer cell growth and migration (34).

COL1A1 and COL1A2 encode the α1 and α2 chains of type I collagen, respectively (35). The primary constituents of the ECM are collagens, adhesive glycoproteins and proteoglycans (36). Specific interactions between cells and ECM-mediated cell-surface-associated components and transmembrane molecules result in the control of cellular activities, including adhesion and migration (37). Collagen is the primary component of the ECM, which serves pivotal roles in maintaining skin and vessel elasticity, and increasing cartilage lubricity (38). Upregulation of type II collagen expression may contribute to ovarian cancer metastasis and biological processes, including cell proliferation, invasion and migration (39). The oncogene JUN is the putative transforming gene of avian sarcoma virus 17, which is the most extensively studied protein of the activator protein-1 complex and is involved in numerous cell activities, including proliferation, apoptosis, survival, tumorigenesis and tissue morphogenesis. The present study identified that COL1A1 was important in ovarian cancer, which was highly connected with cervical and endometrial cancer. Therefore, COL1A1 and JUN may be potentially important associated genes of the three types of gynecological malignancies.

miRNAs are small noncoding regulatory RNAs that downregulate transcription by targeting specific mRNAs. Furthermore, the present study identified that certain miRNAs were highly associated with hub connected genes, including let7, which is one of the founding members of the miRNA family. This miRNA was first identified in Caenorhabditis elegans. Lee and Dutta (40) identified six functional let7 target sites in the 3′-untranslated region of high mobility group AT-hook 2 (HMGA2), which reduced HMGA2 expression and cell proliferation in a lung cancer cell line. Using genome-wide mRNA expression analysis, Mi et al (41) identified that miRNA let7B was downregulated in acute lymphoblastic leukemia (ALL) compared with acute myeloid leukemia (AML). Quantitative polymerase chain reaction analysis confirmed the downregulation of let7B in ALL samples compared with AML samples and normal controls.

The present study identified that let7a, let7b and let7c had strong connections with the hub genes and that these miRNAs may serve an important part of the potential mechanism, which may explain the connections across the hub genes.

Overall, the present study identified a number of DEGs associated with gynecological cancer, in addition to the functions and signaling pathways in which these genes were involved. Comprehensive network analyses of the dysregulated gene expression in gynecological cancers identified a series of hub genes and the connection genes across ovarian cancer, cervical cancer and endometrial cancer in a PPI network. Subsequently, this study confirmed the driver genes by survival analysis using the TCGA database. Comprehensive network analyses of miRNAs and connection driver genes identified certain miRNAs which may be potential therapeutic and prevention targets of gynecological cancer. In addition, the present study demonstrated the associations across the different gynecological cancers, which may be useful for identifying potential useful diagnostic markers and novel therapeutic targets. The results of this study may provide an insight into the underlying mechanism of the aforementioned gynecological cancers and may lead to further improvement in diagnosis and treatment of them.

 Acknowledgements

The authors would like to thank Professor Yunyan Gu (College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.) for technical support and critically reviewing the manuscript.

 Funding

The present study was supported by grant no. RC2013QN004112 from Harbin Science and Technology Innovation Talents, China.

 Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

 Authors' contributions

MY and JW conceived and designed the study; MY, LL and JL performed the experiments and analyzed the data. MY wrote the paper, and JW revised the manuscript and gave final approval of the version to be published.

 Ethics approval and consent to participate

The present study was approved by the Clinical Research Ethics Committee of the Affiliated Zhuzhou Hospital Xiangya Medical College CSU (Zhuzhou, China), and written informed consent was obtained from all participants.

 Consent for publication

Written informed consent was obtained from all volunteers for the publication of any associated data.

 Competing interests

The authors declare that they have no competing interests.

 Abbreviations miRNAs

microRNAs

 GO

gene ontology

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 PPI

Protein-protein Interaction

 DEGs

differentially expressed genes

 GEO

Gene Expression Omnibus

 MERAV

Metabolic Gene Rapid Visualizer Database

 HPRD

Human Protein Reference Database

 MMPs

matrix metalloproteinases

 ECM

extracellular matrix

 References LiXYWangXThe role of human cervical cancer oncogene in cancer progressionInt J Clin Exp Med8836383682015263094894538137 NiskerJAScreening for endometrial cancerCan Fam Physician299619651983212833742153853 SetiawanVWYangHPPikeMCMcCannSEYuHXiangYBWolkAWentzensenNWeissNSWebbPMType I and II endometrial cancers: Have they different risk factors?J Clin Oncol3126072618201310.1200/JCO.2012.48.2596237337713699726 WrightJDTake 'em or leave 'em: Management of the ovaries in young women with endometrial cancerGynecol Oncol131287288201310.1016/j.ygyno.2013.09.02624139060 LukkMKapusheskyMNikkiläJParkinsonHGoncalvesAHubeWUkkonenEBrazmaAA global map of human gene expressionNat Biotechnol28322324201010.1038/nbt0410-322203791722974261 Cancer Genome Atlas Research NetworkMcLendonRFriedmanABignerDVan MeirEGBratDJMastrogianakisGMOlsonJJMikkelsenTLehmanNComprehensive genomic characterization defines human glioblastoma genes and core pathwaysNature45510611068200810.1038/nature07385187728902671642 Cancer Genome Atlas Research Network: Integrated genomic analyses of ovarian carcinomaNature474609615201110.1038/nature10166217203653163504 GuichardCAmaddeoGImbeaudSLadeiroYPelletierLMaadIBCalderaroJBioulac-SagePLetexierMDegosFIntegrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinomaNat Genet44694698201210.1038/ng.2256225615173819251 LiuPMorrisonCWangLXiongDVedellPCuiPHuaXDingFLuYJamesMIdentification of somatic mutations in non-small cell lung carcinomas using whole-exome sequencingCarcinogenesis3312701276201210.1093/carcin/bgs148225102803499051 Nik-ZainalSAlexandrovLBWedgeDCVan LooPGreenmanCDRaineKJonesDHintonJMarshallJStebbingsLAMutational processes molding the genomes of 21 breast cancersCell149979993201210.1016/j.cell.2012.04.024226080843414841 BaudotARealFXIzarzugazaJMValenciaAFrom cancer genomes to cancer models: Bridging the gapsEMBO Rep10359366200910.1038/embor.2009.46193053882672900 KreegerPKLauffenburgerDACancer systems biology: A network modeling perspectiveCarcinogenesis3128201010.1093/carcin/bgp26119861649 LeeSStewartSNagtegaalILuoJWuYColditzGMedinaDAllredDCDifferentially expressed genes regulating the progression of ductal carcinoma in situ to invasive breast cancerCancer Res7245744586201210.1158/0008-5472.CAN-12-0636227514643899801 WrayCJKoTCTanFKSecondary use of existing public microarray data to predict outcome for hepatocellular carcinomaJ Surg Res188137142201410.1016/j.jss.2013.12.01324560427 BarrettTTroupDBWilhiteSELedouxPEvangelistaCKimIFTomashevskyMMarshallKAPhillippyKHShermanPMNCBI GEO: Archive for functional genomics data sets-10 years onNucleic Acids Res39(Database Issue)D1005D1010201110.1093/nar/gkq118421097893 ChungVYTanTZTanMWongMKKuayKTYangZYeJMullerJKohCMGuccioneEGRHL2-miR-200-ZEB1 maintains the epithelial status of ovarian cancer through transcriptional regulation and histone modificationSci Rep619943201610.1038/srep19943268879774757891 HalabiNMMartinezAAl-FarsiHMeryEPuydenusLPujolPKhalakHGMcLurcanCFerronGQuerleuDPreferential allele expression analysis identifies shared germline and somatic driver genes in advanced ovarian cancerPLoS Genet12e1005892201610.1371/journal.pgen.1005892268901594759456 EspinosaAMAlfaroARoman-BasaureEGuardado-EstradaMPalmaÍSerraldeCMedinaIJuárezEBermúdezMMárquezEMitosis is a source of potential markers for screening and survival and therapeutic targets in cervical cancerPLoS One8e55975201310.1371/annotation/36613e7a-41fa-4199-87eb-40214100b4cb234052413566100 ShaulYDYuanBThiruPNutter-UphamAMcCallumSLanzkronCBellGWSabatiniDMMERAV: A tool for comparing gene expression across human tissues and cell typesNucleic Acids Res44D560D566201610.1093/nar/gkv133726626150 R Development Core TeamR: A language and environment for statistical computingR Foundation for Statistical ComputingVienna, Austria2017 PrasadKeshava TSGoelRKandasamyKKeerthikumarSKumarSMathivananSTelikicherlaDRajuRShafreenBVenugopalAHuman protein reference database-2009 updateNucleic Acids Res37(Database Issue)D767D772200910.1093/nar/gkn89218988627 JiangQWangYHaoYJuanLTengMZhangXLiMWangGLiuYmiR2Disease: A manually curated database for microRNA deregulation in human diseaseNucleic Acids Res37(Database Issue)D98D104200910.1093/nar/gkn71418927107 VlachosISParaskevopoulouMDKaragkouniDGeorgakilasGVergoulisTKanellosIAnastasopoulosILManiouSKarathanouKKalfakakouDDIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA:mRNA interactionsNucleic Acids Res43(Database Issue)D153D159201510.1093/nar/gku121525416803 DennisGJrShermanBTHosackDAYangJGaoWLaneHCLempickiRADAVID: Database for annotation, visualization, and integrated discoveryGenome Biol4P3200310.1186/gb-2003-4-5-p312734009 KaimalVBardesEETabarSCJeggaAGAronowBJToppCluster: A multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systemsNucleic Acids Res38W96W102201010.1093/nar/gkq418204843712896202 AltermannEKlaenhammerTRPathwayVoyager: Pathway mapping using the Kyoto encyclopedia of genes and genomes (KEGG) databaseBMC Genomics660200510.1186/1471-2164-6-60158697101112592 StamoulisCBetenskyRAA novel signal processing approach for the detection of copy number variations in the human genomeBioinformatics2723382345201110.1093/bioinformatics/btr402217528003157926 MillerCASettleSHSulmanEPAldapeKDMilosavljevicADiscovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumorsBMC Med Genomics434201110.1186/1755-8794-4-34214893053102606 CirielloGCeramiESanderCSchultzNMutual exclusivity analysis identifies oncogenic network modulesGenome Res22398406201210.1101/gr.125567.111219087733266046 VandinFUpfalERaphaelBJDe novo discovery of mutated driver pathways in cancerGenome Res22375385201210.1101/gr.120477.111216532523266044 TsanirasChamperis SKanellakisNSymeonidouIENikolopoulouPLygerouZTaravirasSLicensing of DNA replication, cancer, pluripotency and differentiation: An interlinked world?Semin Cell Dev Biol30174180201410.1016/j.semcdb.2014.03.01324641889 PruittSCBaileyKJFreelandAReduced Mcm2 expression results in severe stem/progenitor cell deficiency and cancerStem Cells2531213132200710.1634/stemcells.2007-048317717065 SimonNESchwachaAThe Mcm2-7 replicative helicase: A promising chemotherapeutic targetBiomed Res Int2014549719201410.1155/2014/549719252431494163376 PittayapruekPMeephansanJPrapapanOKomineMOhtsukiMRole of matrix metalloproteinases in photoaging and photocarcinogenesisInt J Mol Sci17pii: E868201610.3390/ijms17060868 ChanTFPoonABasuAAddlemanNRChenJPhongAByersPHKleinTEKwokPYNatural variation in four human collagen genes across an ethnically diverse populationGenomics91307314200810.1016/j.ygeno.2007.12.008182723252737816 BosmanFTStamenkovicIFunctional structure and composition of the extracellular matrixJ Pathol200423428200310.1002/path.143712845610 UittoVJLarjavaHExtracellular matrix molecules and their receptors: An overview with special emphasis on periodontal tissuesCrit Rev Oral Biol Med2323354199110.1177/104544119100200303011654140 DeylZMiksikIEckhardtAPreparative procedures and purity assessment of collagen proteinsJ Chromatogr B Analyt Technol Biomed Life Sci790245275200310.1016/S1570-0232(03)00158-212767337 DaiJWangTWangWZhangSLiaoYChenJRole of MAPK7 in cell proliferation and metastasis in ovarian cancerInt J Clin Exp Pathol810444104512015266177534637568 LeeYSDuttaAThe tumor suppressor microRNA let-7 represses the HMGA2 oncogeneGenes Dev2110251030200710.1101/gad.1540407174379911855228 MiSLuJSunMLiZZhangHNeillyMBWangYQianZJinJZhangYMicroRNA expression signatures accurately discriminate acute lymphoblastic leukemia from acute myeloid leukemiaProc Natl Acad Sci USA1041997119976200710.1073/pnas.0709313104180568052148407

Experimental protocol of the present study. DEG, differentially express genes; GO, gene ontology; MERAV, Metabolic Gene Rapid Visualizer database; TGCA, The Cancer Genome Atlas.

Protein-protein interaction networks of the DEGs identified by integrated analysis of the microarray databases throughout cancer of the cervix, ovary or endometrium. Each cancer holds a number of DEGs. Driver genes were extracted from the DEGs, whose degree (the number of lines attached to each node) was ≥4. The orange dots represent cervical carcinoma, green dots represent ovarian carcinoma and blue dots represent endometrial carcinoma. Genes with a higher degree of association exhibit a larger node size. Each biological association (an edge) between two genes (nodes) was supported by at least one reference from the literature or information stored in the Human Protein Reference Database. DEGS, differentially expressed genes.

Network construction of miRNAs to driver genes. The miRNA dataset was downloaded from the Mir2 Disease database (www.mir2disease.org). The miRNAs presented in the figure are associated with cancer of the cervix, ovary or endometrium. Triangular nodes represent miRNAs. Circular nodes represent genes. Orange dots represent cervical carcinoma, green dots represent ovarian carcinoma and blue dots represent endometrial carcinoma. The degree for each dot is represented by the size of the node. miRNA/miR, microRNA.

(A) GO terms of cervical cancer driver genes. (B) GO terms of ovarian cancer driver genes. (C) GO terms of endometrial carcinoma driver genes. GO, gene ontology.

(A) KEGG pathway functional annotation of cervical cancer driver genes. (B) KEGG pathway functional annotation of ovarian cancer driver genes. (C) KEGG pathway functional annotation of endometrial carcinoma driver genes. KEGG, Kyoto Encyclopedia of Genes and Genomes.

Survival analysis of the different cancer types using the representative driver genes. Survival data representing time between initial diagnosis and mortality were downloaded directly from TCGA data portal. The red line represents the high expression group and the blue line represents the low expression group. (A) Cervical hub-gene MCM2 in cervical cancer. high and low expression of MCM2 divided the samples into two groups, with 133 and 144 samples in each group, respectively. (B) Cervical hub-gene MMP2 in cervical cancer, whose high and low expression divided the group into two, with 142 and 142 samples in each group, respectively. (C) Ovarian hub-gene COL1A1 in cervical cancer, whose high and low expression divided the group into two, with 143 and 141 samples in each group, respectively. (D) Ovarian hub-gene JUN in cervical cancer, whose high and low expression divided the group into two, with 141 and 144 samples in each group, respectively. MCM2, minichromosome maintenance complex component 2; MMP2, matrix metalloproteinase 2; COL1A1, collagen type I α1 chain; TCGA, The Cancer Genome Atlas.

Datasets from the Metabolic Gene Rapid Visualizer database (cervix).

Tissue type Datasets
Normal, n=4 GSM176135, GSM175833, GSM176130, GSM176140
Tumor
  Squamous cell carcinoma, n=5 GSM152635, GSM277702, GSM46919, GSM102527, GSM152587
  Squamous cell carcinoma non-keratinizing, n=5 GSM179907, GSM46942, GSM76614, GSM152580, GSM203742
  Squamous cell carcinoma keratinizing, n=3 GSM117576, GSM152723, GSM152751
  Adenoma, n=6 GSM179956, GSM152667, GSM152719, GSM179853, GSM325835, GSM203622

Datasets from the Metabolic Gene Rapid Visualizer database (ovary and endometrium).

Datasets
Tissue type Ovary, n=4 Endometrium, n=22
Normal tissues GSM175789 GSM175777, GSM175778,
GSM176131 GSM175779, GSM175780,
GSM176136 GSM175781, GSM175783,
GSM176318 GSM175784, GSM175785,
GSM176039, GSM176040,
GSM176041, GSM176043,
GSM176093, GSM176099,
GSM176127, GSM176137,
GSM176141, GSM176142,
GSM176144, GSM176146,
GSM176143, GSM176145,
Tissue type Ovary serous adenocarcinoma, n=11 Endometrioid carcinoma, n=12
Tumors GSM8897, GSM203626, GSM15267, GSM102425, GSM117582,
GSM102445, GSM46831, GSM152577, GSM117586, GSM117590,
GSM88973, GSM152581, GSM27769, GSM88952, GSM88966,
GSM277737, GSM301703 GSM102469, GSM102492,
GSM53058, GSM88978,
GSM46923, GSM46937

Driver genes identified by integrated analysis of the microarray datasets (cervical squamous cell carcinoma).

Gene
RB1 HTRA1 MTOR CLDN5 NARF PURA
MCM7 KPNA2 PLSCR4 CYBA NCAPD2 RBM8A
MCM2 LMNB1 PRKD1 DCUN1D1 NCF4 RECK
PLK1 MEIS1 PSMA5 DDAH2 NME4 REV3L
AR NCOA1 PSMB10 DMPK NPLOC4 RFC3
PPP1CA PBX1 PSMB9 EPS8 NR2F1 RNF126
ABL1 PIAS3 PSMD2 EXOSC5 NR2F2 RPA3
LMNA POLA2 RACGAP1 GABBR1 NRAS RRM1
PTN PPP1R14A RTN3 GAS6 NTF3 RRM2
TRIP13 AXL SNRPB GCH1 NTRK2 SAT2
CAV1 BUB1B TOR1AIP1 GCHFR NUB1 SDC2
CDC20 CCL14 TUBA4A GLRX3 NUP210 SEC24A
CDC6 CCR5 UBTF GMFB NUP50 SELENBP1
FLNA COL4A5 USP6NL GOLGA2 PAFAH1B3 SERBP1
FXR2 CSNK1D UTP3 HOXD13 PAK2 SH3BP5
ZHX1 DBF4 ACTN4 ILK PAM SMC4
CCNA2 DVL3 ADAM10 KANK1 PCGF2 SNRPD1
DGKZ EFEMP2 ANTXR2 LAPTM5 PHACTR4 SNTB2
MCM10 EIF4EBP1 ARHGAP17 LDB2 PLK2 SNX27
MCM6 EZH2 ASPM LDOC1 PNO1 SPIN1
PCNA FAM46A BID LMO4 PNP SSSCA1
RBPMS HOXD10 BMP4 LRP1 PPIA STXBP2
RPS6KA1 HSPA4 BNIP2 LRP6 PPIH SUB1
SAT1 ITGB3BP C1QA LRRC41 PRPF18 TALDO1
BUB1 KLF6 CBX4 LZTS2 PSMA6 TGFBR3
CSNK1E MAD2L2 CCNE1 MAGEH1 PSMB7 TNFRSF1A
DCN MAP2K4 CCR1 MELK PSMD4 UFD1L
FGFR1 MAPK10 CDC42BPA MPDZ PSME3 WSB2
FXYD1 MCM5 CENPE MTA1 PSMF1 XPNPEP1
GMNN MITF CHFR MYCBP PSTPIP1 YLPM1
HOXA10 MMP9 CIB1 MYL9 PTTG1 ZMIZ1

Driver genes identified by integrated analysis of the microarray datasets (cervical keratinized squamous cell carcinoma).

Gene
FYN ADAM10 ARHGAP17 HSPB2 PHF1 TMOD1
ZHX1 ADAM17 ARMCX2 ID4 PIK3C2B TMSB10
ABL1 ANXA6 BIN1 LDB2 PIP5K1C TPD52
BCL2L1 AXL CBX3 LDOC1 PNP UBTF
FXR2 BCL11A CLDN5 LMO4 PSME3 ZHX2
TBP CSNK1E CNN3 LRP1 PSMF1 ZMIZ1
AR DMPK CNNM3 LRP6 PTOV1 ZNF76
BARD1 ITGB3BP CNTNAP1 LSM5 PTPN12
BID KPNA6 CRYAB MAGI2 RAE1
DDX24 MAD2L2 CSE1L MAPK10 REV3L
NCOA1 MCL1 CSTF1 MIS12 RUNX1T1
PDGFRB NR2F1 EFEMP2 MPDZ SDC2
PRKD1 NTRK2 EXOSC5 MTA1 SFRP1
PSEN1 PPP1R14A FGFR1 MYCBP SH3BP5
RBPMS PTN FXYD1 NPDC1 TAF9
SPTAN1 RTN3 FZD6 NR2F2 TCF7L2
TCF4 SYK GAS6 NTF3 TERF1
TGFA VIM GDI1 NUDT21 TFDP1
A2M ANTXR2 GTF3C3 PBX1 TGFBR3
ACP1 AQP1 HOXA10 PDGFD TLN2

Driver genes identified by integrated analysis of the microarray datasets (cervical adenocarcinoma G3).

Gene
AR BAD PLD2 CIB1 MAPK10 SERPINA1
CAV1 BAHD1 PPA1 CLDN5 MED14 SF1
FLNA C1QBP PRKD1 CUL4B MPDZ SMO
PPP1CA CPSF6 SAT1 DMPK MYL9 SPINT2
NCK2 CSNK1D SMAD1 EFNB1 NR2F1 SSBP3
PLSCR1 DOCK1 SNAP23 F3 NTF3 SSR1
SUMO4 DVL2 TAF1D GDI1 PCGF2 STAM
LMNA FXR2 TAF9 HOXA10 PDPK1 SYNE1
LRP1 FXYD1 TCF4 HOXD10 PHACTR4 TCF7L2
PSEN1 ILK WIPI1 HOXD13 PHYHIP TGFBR3
PTN LDB1 ACVR2A HSPA1B PLSCR4 TMF1
CSNK1E LMO4 ANTXR2 HSPBAP1 PNPLA2 UBTF
DVL3 MAP2K4 ATG12 KANK1 PPP1R10 VAMP8
MMP14 NCOA1 CD82 KPNA6 PTCH2 WASF1
PPP1R14A NTRK2 CDC42BPA LDB2 RNF138 WASF2
ALDOA PBX1 CDC42EP1 LRP6 RUNX1T1 ZHX1

Driver genes identified by integrated analysis of the microarray datasets (cervical non-keratinized squamous cell carcinoma).

Gene
AR FXR2 FOXO1 TLR2 FBN2 NTF3
ABL1 ILK GMNN TXNDC9 FGR NTRK2
CAV1 LMNA HOXD10 XRCC4 FXYD1 NUBP1
CHD3 MEIS1 ICAM3 YAP1 GDI1 PALLD
HIF1A NCOA1 ITGB2 ADCY6 HCLS1 PDPK1
PTPN6 PAG1 LCP2 ADI1 HLA-DMB PGK1
SAT1 PBX1 LRP1 AGTPBP1 HLA-DRA PGLS
FLNA PIAS1 MAFG ANTXR2 HOXD13 PIK3R3
HOXA10 PSEN1 MPDZ ANXA6 HSPB2 PLTP
PLSCR1 PTN NDN ARHGDIB LCP1 PNP
RAF1 WASF2 NR2F2 CDC37 LDOC1 PRRX1
DCN ZHX1 PAICS CITED2 LILRB2 RAB11FIP2
EZR ACTR3 PLSCR4 CLDN5 LRP6 RAB18
MMP14 BIN1 PPP1R14A CNN3 MAPK10 RFXANK
PDGFRB C1QB PPP2R1A COL4A5 MED14 RUNX1T1
ABCA1 C1QC PRDX2 DOCK1 MTA1 SAT2
C1QA CSNK1D SNTB2 DVL2 MYO5B SEPHS1
CSNK1E DGKZ SSSCA1 ENO1 NARF SF1
DMPK DVL3 TCF4 FAM46A NISCH
ELN EFEMP2 TLR1 FBLN1 TICAM1
SNX2 SYNE1 TCF7L2 VTA1 TRAP1
TMEM8B TMOD1 TMSB10 TPD52 SH3BP5
WASF3 ZNF76 TEAD3 TIMP2 NR2F1

Driver genes identified by integrated analysis of the microarray datasets (cervical adenocarcinoma G2).

Gene
ABL1 HSPA5 ASAP1 PSMF1 ASS1 EHD2
AR HTRA1 AXL QKI ATRX ENAH
CAV1 LMNA BCR RAB4A AURKA ENO1
PPP1CA MEIS1 BGN RNF138 AURKB ERBB3
FLNA NTRK2 BMP4 SDC2 BIN1 FBLN1
FYN PRNP BRCA2 SMARCE1 BIRC5 GAS6
MMP2 PTPN12 CDKN2A SNAP29 CAPZB GLRX3
SMAD1 SMAD5 CSNK1E TAF7 CAV2 GOLGA2
NCK2 TAF9 DMPK TCF4 CBX4 GTF2I
RB1 TTF2 DOCK1 TGFBR3 CD81 HAT1
PTN DVL2 DR1 THBS2 CDT1 HOXD10
PTPN6 EFEMP2 FGFR1 TIFA CEP76 HSPA1B
SMAD7 FXR2 FXYD1 TIMP2 CLDN5 HSPB2
SUMO4 HOXA10 GDF5 TNFRSF1A CLU IDE
A2M HOXD13 GNA12 ZHX1 CNN3 IFI35
AP1M1 LRP1 KIDINS220 ADI1 CNTNAP1 IFNAR1
CDC5L NCOA1 LDOC1 AHNAK COL4A5 ILK
EZR NOTCH2 LRP6 ALDOA COL6A3 IQGAP1
MMP14 PBX1 MAFG ANTXR1 COX5A JAG1
PIAS1 PDGFRB MAP2K4 ANTXR2 CUL4B KANK1
CD2AP PRKD1 MAPK10 ANXA6 CXCL12 KDM2A
CDH1 SAT1 MEF2C AQP1 DCLRE1A KPNA6
DCN WASF2 POLE3 ARHGAP17 DDX24 LCAT
DRAP1 YAP1 PPP1R14A ARHGEF6 EFNB1 MAD2L1BP
ELN ACVR2A PRRX1 ASH1L EFS MAP3K3
MCM4 NR2F2 PLSCR4 RUNX1T1 SYNE1 WNK1
MED14 NTF3 PPA1 SALL2 TEAD3 YLPM1
MPDZ NUDT21 PPP1R10 SAT2 TERF1 ZMIZ1
MSN PALB2 PPP2R1A SETD7 THBS3
MYCBP2 PALLD PSMB10 SH3BP5 TMEM8B
MYO5B PBX3 PURA SH3KBP1 TSPAN4
NFE2L1 PDGFD RAB11FIP1 SKAP1 TWIST2
NMI PHACTR4 RAB11FIP2 SPARCL1 UBTF
NPHS2 PIP4K2B RBPJ STX3 VGLL4
NR2F1 PKD2 REPS2 STX7 WFDC2

Driver genes identified by integrated analysis of the microarray datasets (adenocarcinoma of the ovary Grade 2).

Gene
JUN MEF2C HSPA1A CNNM3 GNE PHF1
FXR2 NCOA2 HTRA1 COX5A GNG4 PKD2
RAF1 NIF3L1 IKZF4 CRY2 GPRASP1 PLA2G16
RBPMS PCBD1 LIFR CTF1 HMGA1 PLK1
ZBTB16 PDGFRA MAPK10 CTSD HSPA2 PTPN13
PRKACA PRTFDC1 MYO15A DCN ICAM3 RBBP8
CAV1 STAT5A NFE2L1 DST IGFBP4 RBP1
MAP3K3 APBB1 NR2F6 ELF3 IRS1 SDC2
MAP3K5 C1R PER1 ELK1 KIAA1217 SGK1
NCOA1 C1S PTPN6 ENAH MAFG SH3BP5
PDGFRB CALCOCO2 SERPING1 ENG MRAS SMC3
SIN3A CD2AP SIN3B EPS8 NBL1 SNCA
ABLIM1 DCTN1 TGFBR3 ETV6 NFATC4 SNRNP70
DDX17 DMPK TSC22D3 EYA2 NINL SPOP
FEZ1 DVL2 UBQLN1 FLAD1 NR2F2 SPTBN1
GATA4 FHL2 ACTA2 FOXO1 OLFML3 SPTBN2
GOLGA2 FLNA BEGAIN FOXO3 PAICS ST13
LRP1 FXYD1 CCT5 FTH1 PDGFD STRBP
TCF4 THRA TPM2 TXN USP13 ZC3H10
TEAD1 TOP2A TRIM21 TXNDC9 WTIP ZFPM2

Driver genes identified by integrated analysis of the microarray datasets (adenocarcinoma of the ovary Grade 3).

Gene
CDK1 HLA-DRA CD14 FCGR2B PDGFD NR2F2
AURKB ICAM3 CDC20 FOS SLPI
CAV1 KRT7 CDH1 GCA SMC4
PTPN6 MAD2L1 CDKN2A GNE SOX9
ZBTB16 MAL2 CEBPG GPRASP1 SPINT1
BCL2L1 MAP3K5 CENPA HLA-DMB ST14
HSPA1A PDGFRA CKS2 HLA-DRB1 STRBP
IRS1 PDGFRB CLDN1 LAPTM5 TACC1
ITGB2 PMAIP1 CLDN3 LCP1 TOP2A
MCM2 RACGAP1 CRIP1 LRP1 TRIP13
NDC80 RBPMS CTSS MSLN TYROBP
SYK TPD52 CXCR4 MUC1 ZWINT
TPD52L1 ALOX5 DBF4 MUC16 ECT2
BCL11A ALOX5AP DSC2 NCAPD2 CCNB1
CCNB2 BIK DSG2 NR2F1 ERBB3

Driver genes identified by the integrated analysis of the microarray datasets (endometrial carcinoma).

Gene
EP300 CDKN2A F2R AMFR EPN3 MMP11
JUN COL3A1 FZD5 AXL EPR1 MMP26
CAV1 EGR1 HLA-DMB BCL11A FOSB MYO5B
CTNNB1 ERBB4 HOXA10 BCL2A1 GALNT10 NRG2
ABL1 FBLN1 ID1 BIK GAS6 NRXN2
AR FBN1 ID4 BLNK GATA2 PCOLCE
TCF4 FLNA IDE C1R GCH1 PDGFRB
THBS1 FOXO1 INADL C1S GCHFR PKD2
TUBA4A HLA-DRA JUND C3AR1 GPI PNP
ATXN1 ID3 LMO4 CCND2 GPRASP1 PPP1R14A
COL1A1 IGFBP5 LNX1 CDH11 HLA-DQB1 PRDM1
DCN LAMB3 NCALD CDKN1A HLA-DRB1 PSTPIP2
LRP1 MITF NCF2 CDKN2C HLF PTGDS
C3 MYC NR2F2 CFB HOXA9 PTGS2
COL7A1 PLAT PDGFRA CGN ID2 R3HDM2
FBLN2 RUNX1T1 PLEKHF2 CLEC3B IGFBP4 RAB25
FOS S100A8 PTPN13 CLK1 IGFBP6 RAB3IP
GNAI2 SERPINA1 RAB8B CXADR IL33 RAPGEF6
IGF1 SYK RABAC1 CXCL10 IRS1 S100A9
LAMC2 TGFB1I1 ROR2 DNM1 KLF5 SCRIB
MUC1 CD14 SFN DPYSL2 LAPTM5 SEC24D
NID1 COL5A1 SFRP1 ECM1 LDB2 SNTB2
PRKD1 CRMP1 TFAP2A EDNRA LUC7L3 SOX9
PTPN12 DBP TJP2 EFEMP2 MAFB SPINT1
VCAN DDR2 TRPC1 EFS MAL2 SPP1
CD74 F10 WNT5A ENO2 MAPK10 ST14
SYTL1 TJP3 TLR3 TRO WASF2 WNT4
TBL1X TLR2 TPD52 USP54 WNT2 ZEB1