Leveraging Machine Learning to Uncover Molecular Pathways and Candidate Drugs in Ovarian Cancer and Polycystic Ovary Syndrome

Ovarian cancer (OC) and polycystic ovary syndrome (PCOS) significantly impact the lives of millions of women worldwide. PCOS affects 8-13% of women of reproductive age, while OC is regarded as the deadliest form of gynecological cancer. Their impacts are enormous, yet the molecular links between these two diseases are not well defined. This study tries to explore this important knowledge gap using an integrated bioinformatics approach to unravel common genetic signatures and potential therapeutic candidates for PCOS and OC. We used differential gene expression (DEGs) analysis, protein-protein interaction (PPI) networks, pathway enrichment analysis, and regulatory network analysis on two microarray datasets. Utilizing the sophisticated machine learning approach known as minimum redundancy maximum relevance (mRMR), we detected 24 genes that exhibited differential expression across both conditions. Analysis of the PPI network highlighted ten prominent hub genes - GIGYF2, FGB, ZEB2, S100P, TNFRSF10C, CP, LMAN2, RAPH1, UBC, and KDM4D - which are presumably integral to the molecular interactions between PCOS and OC. Functional enrichment analysis identified significant biological pathways; the regulatory network analysis revealed complex TFs, miRNAs, and DEGs interactions. More importantly, we have predicted potential therapeutic compounds apigenin and sirolimus targeting the shared DEGs. These results provide a crucial clue toward the possible molecular underlying mechanisms of PCOS and OC for further therapeutic and diagnostic interventions. The present study is epitomizing the effectiveness of integrating different bioinformatic approaches to uncover complex disease mechanisms and guide further investigational efforts in the field of women's health.