Combinatorial Bioinformatics Analysis Reveals Novel Biomarkers for Improved Ovarian Cancer Prognosis

Abstract Background: Given the known lethality of highly frequent ovarian cancer (OC) among females, it is imperative to investigate potential biomarkers of prognostic and therapeutic significance. The objective of this study was to identify significant differentially expressed genes (DEGs) with poor prognosis and to explore their underlying mechanisms. Methods: We acquired three microarray datasets (GSE14407, GSE36668 and GSE18520), available from the public database GEO. We compared a total of 72 cancerous and 26 normal samples originating from ovarian tissues. GEO2R and Venn diagram tools were used to obtain DEGs, followed by the gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analysis via Database for Annotation, Visualization and Integrated Discovery (DAVID). Subsequently, protein-protein interaction (PPI) network was constructed and visualized in Cytoscape. Results: Among three analyzed datasets, a total of 232 DEGs were common. The upregulated 108 genes were significantly enriched in the cell adhesion, cellular response to interleukin-1, positive regulation of transcription from DNA/RNA, and transcription, extracellular matrix/region, anchored membrane component, cell junction and golgi membrane, sequence-specific DNA binding, transcription factor activity, RNA polymerase II regulatory region, and DNA binding. The PPI network analysis via MCODE plug-in revealed a total of 14 upregulated genes. Kaplan-Meier plotter analysis revealed that 9 genes were associated with significantly worse survival among OC patients while 4 genes exhibited no significant effect. Gene Expression Profiling Interactive Analysis (GEPIA) showed that 13 DEGs had significantly higher expression in the ovarian cancerous tissues compared to the normal ones. Repeated KEGG analysis showed that 11 genes (CDC6, CCNE1, BUB1B, CCNB2, BUB1, SFN, TTK, CDC20, PTTG1, CDK1 and CDKN2A)) were mainly associated with cell cycle while 2 genes (SFN and RRM2) were related to p53 signaling pathway. Conclusion: Our findings identify potential upregulated DEGs of prognostic value among OC patients. This will facilitate to understand the underlying OC mechanisms and to implement targeted therapeutic measures.