Identification of cancer prognosis-associated functional modules using differential co-expression networks.

ABSTRACT: The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the signifi

SUBMITTER: Yu W 

PROVIDER: S-EPMC5762563 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature