Construction of a novel prognostic-predicting model correlated to ovarian cancer

Background: Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of ovarian cancer patients remain largely unclear. This study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival.

Results: We identified 164 up-regulated and 80 down-regulated differentially-expressed genes (DEGs) associated with ovarian cancer. GO term enrichment showed DEGs mainly correlated with spindle microtubes. For KEGG pathways, cell cycle was mostly enriched for the DEGs. The PPI network yielded 238 nodes and 1284 edges. Top three modules and 10 hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival.

Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified 8-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.

Keywords: Ovarian cancer; bioinformatics; biological function; hub genes; prognostic model.