Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): A bioinformatics analysis

Abstract

Abstract Worldwide polycystic ovary syndrome (PCOS) is one of the most common hormonal disorders in women of reproductive age. However, there is a lack of genetic study of the internal mechanisms of PCOS. Herein, we identify core genes involved in the pathogenesis of PCOS by using bioinformatics analysis. For the study, the dataset GSE124226 was collected from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were obtained by using the R package limma. We found a total of 180 DEGs in which 73 were overexpressed and 107 were down-expressed. The functional analysis was analysed using the DAVID database and software tools for the identified up-regulated and down-regulated DEGs. We generated protein-protein interaction (PPI) networks by using Cytoscape and identified four hub genes (RARA, KPNB1, REL, and MAP1B) from the PPI network according to the degree score using cytoHubba; module analysis was also performed by using the MCODE plugin. Finally, we used the identified hub genes to reveal significant drug signatures, which may be useful as therapeutic targets for PCOS.