Network Analysis of Differential Gene Expression to Identify Hub Genes in Ovarian Cancer

Abstract

Epithelial ovarian cancer (EOC), as a challenging disease among women with poor prognosis and unclear molecular pathogenesis, each year is responsible for 140000 deaths globally. Recent progress in the field revealed the importance of proteins as key players of different biological events. Considering the complicated protein interactions, taking a deeper look at protein-protein interactions (PPIs) could be considered as a superior strategy to unravel complex mechanisms encountered with regulatory cell signaling pathways of ovarian cancer. Hence, PPI network analysis was performed on differentially expressed genes (DEGs) of ovarian cancer to discover hub genes which have the potential to be introduced as biomarkers with clinical utility. A PPI network with 600 DEGs was constructed. Network topology analysis determined UBC, FN1, SPP1, ACTB, GAPDH, JUN, and RPL13A, with the highest Degree (K) and betweenness centrality (BC), as shortcuts of the network. KEGG pathway analysis showed that these genes are commonly enriched in ribosome and ECM-receptor interaction pathways. These pivotal hub genes, mainly UBC, FN1, RPL13A, SPP1, and JUN have been reported previously as potential prognostic biomarkers of different types of cancer. However, further experimental molecular studies and computational processes are required to confirm the function and association of the identified hub genes with epithelial ovarian cancer prognosis.