Gene co-expression network reveals shared modules predictive of stage and grade in serous ovarian cancers.

ABSTRACT: Serous ovarian cancer (SOC) is the most lethal gynecological cancer. Clinical studies have revealed an association between tumor stage and grade and clinical prognosis. Identification of meaningful clusters of co-expressed genes or representative biomarkers related to stage or grade may help to reveal mechanisms of tumorigenesis and cancer development, and aid in predicting SOC patient prognosis. We therefore performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on three public microarray datasets (GSE26193, GSE9891, and TCGA), which included 788 samples and 10402 genes. We detected four modules related to one or more clinical features significantly shared across all modeling datasets, and identified one stage-associated module and

SUBMITTER: Sun Q 

PROVIDER: S-EPMC5522121 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature