Identification of four hub genes as promising biomarkers to evaluate the prognosis of ovarian cancer in silico

BACKGROUND: Ovarian cancer (OvCa) is one of the most fatal cancers among females in the world. With growing numbers of individuals diagnosed with OvCa ending in deaths, it is urgent to further explore the potential mechanisms of OvCa oncogenesis and development and related biomarkers. METHODS: The gene expression profiles of GSE49997 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the most potent gene modules associated with the overall survival (OS) and progression-free survival (PFS) events of OvCa patients, and the prognostic values of these genes were exhibited and validated based on data from training and validation sets. Next, protein–protein interaction (PPI) networks were built by GeneMANIA. Besides, enrichment analysis was conducted using DAVID website. RESULTS: According to the WGCNA analysis, a total of eight modules were identified and four hub genes (MM > 0.90) in the blue module were reserved for next analysis. Kaplan–Meier analysis exhibited that these four hub genes were significantly associated with worse OS and PFS in the patient cohort from GSE49997. Moreover, we validated the short-term (4-years) and long-term prognostic values based on the GSE9891 data, respectively. Last, PPI networks analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed several potential mechanisms of four hub genes and their co-operators participating in OvCa progression. CONCLUSION: Four hub genes (COL6A3, CRISPLD2, FBN1 and SERPINF1) were identified to be associated with the prognosis in OvCa, which might be used as monitoring biomarkers to evaluate survival time of OvCa patients.