Identification of Key Genes Associated with Polycystic Ovarian Syndrome and Endometrial and Ovarian Cancer through Bioinformatics

Abstract

Background: Polycystic ovary syndrome (PCOS), a common endocrine disorder, is linked to increased risks of endometrial cancer (EC) and ovarian cancer (OC). Our study utilises bioinformatics analysis to identify shared gene signatures and elucidate biological processes between EC and OC and PCOS. Aim: The objective of this research is to unveil the common molecular landscape shared by PCOS and EC and OC. Settings and Design: An observational computational bioinformatics analysis. Materials and Methods: Gene expression profiles for PCOS (GSE199225), EC (GSE215413) and OC (GSE174670) were obtained from the Gene Expression Omnibus database. Hub genes were identified through functional enrichment analysis and protein–protein interaction. Drug identification analyses were employed to find drugs targeting the hub genes. Results: Key hub genes linking PCOS and EC includes RECQL4, RAD54L, ATR, CHTF18, WDHD1, CDT1, PLK1, PKMYT1, RAD18 and RPL3; for PCOS and OC, they include HMOX1, TXNRD1, NQO1, GCLC, GSTP1, PRDX1, SOD1, GPX3, BOP1 and BYSL. Gene Ontology analysis revealed DNA metabolic process in PCOS and EC, while in PCOS and OC, it identified the removal of superoxide radicals. Kyoto Encyclopaedia of Genes and Genomes pathway analysis highlighted cell cycle in PCOS and EC and hepatocellular carcinoma in PCOS and OC. Potential drugs for PCOS and EC include quercetin, calcitriol and testosterone; for PCOS and OC, eugenol and 1-chloro-2,4-dinitrobenzene are identified. Conclusion: These findings offer insights into potential therapeutic targets and pathways linking PCOS with EC and OC, enhancing our understanding of the molecular mechanisms involved in these associations.

Keywords

• bioinformatics
• endometrial cancer
• ovarian cancer
• polycystic ovary syndrome