Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis

Abstract

Background: Ovarian cancer (OC) is one of the worrisome gynecological cancers worldwide. Given its considerable mortality rate, it is necessary to investigate its oncogenesis. Methods: In this study, we used systems biology approaches to describe the key gene modules, hub genes, and regulatory drugs associated with serous OC as the novel biomarkers using weighted gene co-expression network analysis (WGCNA). Findings: Our findings have demonstrated that the blue module genes (r = 0.8, p-value = 1e-16) are involved in OC progression. Based on gene enrichment analysis, the genes in this module are frequently involved in biological processes such as the Cyclic adenosine monophosphate (cAMP) signaling pathway and the cellular response to transforming growth factor-beta stimulation. The co-expression network has been built using the correlated module's top hub genes, which are ADORA1, ANO9, CD24P4, CLDN3, CLDN7, ELF3, KLHL14, PRSS8, RASAL1, RIPK4, SERINC2, and WNT7A. Finally, a drug-target network has been built to show the interaction of the FDA-approved drugs with hub genes. Conclusions: Our results have discovered that ADORA1, ANO9, SERINC2, and KLHL14 are hub genes associated with serous OC. These genes can be considered as novel candidate target genes for treating OC.

Keywords

• Ovarian cancer
• Transcriptome analysis
• WGCNA
• Biomarker