Identification of Potential Biomarkers of Polycystic Ovary Syndrome via Integrated Bioinformatics Analysis.

Polycystic ovary syndrome (PCOS) is a life-long reproductive, endocrine, and metabolic disorder that affects up to 17% of women of reproductive age. However, the effect of granulosa cells (GCs) transcriptome changes on oocyte capacity and follicular development in patients with PCOS has not been elucidated. This study aims to analyze transcriptome changes in GCs of PCOS from different perspectives and explore potential biomarkers for the diagnosis and treatment of PCOS. The gene expression profiles of GSE34526 and GSE107746 were obtained from the GEO database. Differentially expressed genes (DEGs) and key signaling pathways were identified. Gene Set Enrichment Analysis (GSEA) revealed that Toll-like receptors, NOD-like receptors, and NOTCH signaling pathways were obviously enriched in GCs of PCOS. We further analyzed DEGs from three aspects: transcription factors (TFs), secreted proteins, and follicular development. Compared with normal GCs, the DEGs encoding TFs and secretory proteins in GCs of PCOS remarkably changed. Besides, HAS2 and CBLN1, which are highly expressed in preovulatory follicular GCs and may trigger ovulation, were significantly decreased in GCs of PCOS. This study found candidate genes and signaling pathways in PCOS, providing new insights and foundations for the etiology of PCOS. Besides, HSA2 and CBLN1 may be potential therapeutic biomarkers for ovulation disorders in PCOS.