title: Identification of ovarian cancer subtype-specific network modules and candidate drivers through an integrative genomics approach.
abstract: Identification of cancer subtypes and associated molecular drivers is critically important for understanding tumor heterogeneity and seeking effective clinical treatment. In this study, we introduced a simple but efficient multistep procedure to define ovarian cancer types and identify core networks/pathways and driver genes for each subtype by integrating multiple data sources, including mRNA expression, microRNA expression, copy number variation, and protein-protein interaction data. Applying similarity network fusion approach to a patient cohort with 379 ovarian cancer samples, we found two distinct integrated cancer subtypes with different survival profiles. For each ovarian cancer subtype, we explored the candidate oncogenic processes and driver genes by using a network-based approach. Our analysis revealed that alterations in DLST module involved in metabolism pathway and NDRG1 module were common between the two subtypes. However, alterations in the RB signaling pathway drove distinct molecular and clinical phenotypes in different ovarian cancer subtypes. This study provides a computational framework to harness the full potential of large-scale genomic data for discovering ovarian cancer subtype-specific network modules and candidate drivers. The framework may also be used to identify new therapeutic targets in a subset of ovarian cancers, for which limited therapeutic opportunities currently exist.
authors.affiliation: Institute of Health Sciences, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Di
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Institute of Health Sciences, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
authors.affiliation: Co-Innovation Center for Information Supply and Assurance Technology, Anhui University, Hefei, Anhui 230601, China.|College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.affiliation: Institute of Health Sciences, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.|Co-Innovation Center for Information Supply and Assurance Technology, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Xia
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology; D056915:DNA Copy Number Variations; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D009154:Mutation / Q000235:genetics*; D010051:Ovarian Neoplasms / Q000145:classification* / Q000235:genetics*; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor
keywords: cancer subtype; driver gene; integrative genomics approach; network module; ovarian cancer