Molecular signatures identified by integrating gene expression and methylation in non-seminoma and seminoma of testicular germ cell tumours

ABSTRACT Testicular germ cell tumours (TGCTs) are the most common cancer in young male adults (aged 15 to 40). Unlike most other cancer types, identification of molecular signatures in TGCT has rarely reported. In this study, we developed a novel integrative analysis framework to identify co-methylated and co-expressed genes [mRNAs and microRNAs (miRNAs)] modules in two TGCT subtypes: non-seminoma (NSE) and seminoma (SE). We first integrated DNA methylation and mRNA/miRNA expression data and then used a statistical method, CoMEx (Combined score of DNA Methylation and Expression), to assess differentially expressed and methylated (DEM) genes/miRNAs. Next, we identified co-methylation and co-expression modules by applying WGCNA (Weighted Gene Correlation Network Analysis) tool to these DEM genes/miRNAs. The module with the highest average Pearson’s Correlation Coefficient (PCC) after considering all pair-wise molecules (genes/miRNAs) included 91 molecules. By integrating both transcription factor and miRNA regulations, we constructed subtype-specific regulatory networks for NSE and SE. We identified four hub miRNAs (miR-182-5p, miR-520b, miR-520c-3p, and miR-7-5p), two hub TFs (MYC and SP1), and two genes (RECK and TERT) in the NSE-specific regulatory network, and two hub miRNAs (miR-182-5p and miR-338-3p), five hub TFs (ETS1, HIF1A, HNF1A, MYC, and SP1), and three hub genes (CDH1, CXCR4, and SNAI1) in the SE-specific regulatory network. miRNA (miR-182-5p) and two TFs (MYC and SP1) were common hubs of NSE and SE. We further examined pathways enriched in these subtype-specific networks. Our study provides a comprehensive view of the molecular signatures and co-regulation in two TGCT subtypes.

[1]  Chi-Ying F. Huang,et al.  miRTarBase: a database curates experimentally validated microRNA–target interactions , 2010, Nucleic Acids Res..

[2]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[3]  Alexander E. Kel,et al.  TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes , 2005, Nucleic Acids Res..

[4]  Andreas Kowarsch,et al.  PhenomiR: microRNAs in human diseases and biological processes. , 2012, Methods in molecular biology.

[5]  Peilin Jia,et al.  VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data , 2014, PLoS Comput. Biol..

[6]  Yang Li,et al.  HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..

[7]  Doron Betel,et al.  The microRNA.org resource: targets and expression , 2007, Nucleic Acids Res..

[8]  M. S. Wagner,et al.  The role of thyroid hormone in testicular development and function. , 2008, The Journal of endocrinology.

[9]  Robert Karlsson,et al.  Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor , 2017, Nature Genetics.

[10]  Wei Zheng,et al.  dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks , 2011, Bioinform..

[11]  D. Bartel,et al.  Predicting effective microRNA target sites in mammalian mRNAs , 2015, eLife.

[12]  Brad T. Sherman,et al.  DAVID-WS: a stateful web service to facilitate gene/protein list analysis , 2012, Bioinform..

[13]  R. Huddart,et al.  The genomic landscape of testicular germ cell tumours: from susceptibility to treatment , 2016, Nature Reviews Urology.

[14]  J. Azizkhan-Clifford,et al.  Sp1 and the ‘hallmarks of cancer’ , 2015, The FEBS journal.

[15]  Lianghong Zheng,et al.  DNA methylation markers for diagnosis and prognosis of common cancers , 2017, Proceedings of the National Academy of Sciences.

[16]  Zhongming Zhao,et al.  Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data , 2019, Briefings Bioinform..

[17]  G. Botti,et al.  Exploring the molecular aspects associated with testicular germ cell tumors: a review , 2017, Oncotarget.

[18]  Anirban Mukhopadhyay,et al.  A Survey and Comparative Study of Statistical Tests for Identifying Differential Expression from Microarray Data , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[19]  Ujjwal Maulik,et al.  Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining , 2015, PloS one.

[20]  Hua Sun,et al.  Distinct telomere length and molecular signatures in seminoma and non-seminoma of testicular germ cell tumor , 2019, Briefings Bioinform..

[21]  Zhongyuan Zhang,et al.  Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data , 2017, BMC Bioinformatics.

[22]  M. Gerstein,et al.  Genomic analysis of essentiality within protein networks. , 2004, Trends in genetics : TIG.

[23]  Peilin Jia,et al.  MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors , 2020, Scientific Reports.

[24]  N. Vasdev,et al.  Classification, epidemiology and therapies for testicular germ cell tumours. , 2013, The International journal of developmental biology.

[25]  Jie Zhang,et al.  Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers , 2017, BMC Genomics.

[26]  Peter A. Jones Functions of DNA methylation: islands, start sites, gene bodies and beyond , 2012, Nature Reviews Genetics.

[27]  M. Buljubašić,et al.  Epigenetics and testicular germ cell tumors. , 2018, Gene.

[28]  Andrew E. Teschendorff,et al.  A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control , 2014, Bioinform..

[29]  Sanghamitra Bandyopadhyay,et al.  Identification of Multiview Gene Modules Using Mutual Information-Based Hypograph Mining , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[30]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[31]  R. Henrique,et al.  The epigenetics of testicular germ cell tumors: looking for novel disease biomarkers. , 2017, Epigenomics.

[32]  Joshua M. Stuart,et al.  Integrated Molecular Characterization of Testicular Germ Cell Tumors , 2018, Cell reports.

[33]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.