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

SUBMITTER: Zhang D 

PROVIDER: S-EPMC4826206 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature