Identification of crucial genes for polycystic ovary syndrome and atherosclerosis through comprehensive bioinformatics analysis and machine learning.

Objective: To identify potential biomarkers in patients with polycystic ovary syndrome (PCOS) and atherosclerosis, and to explore the common pathologic mechanisms between these two diseases in response to the increased risk of cardiovascular diseases in patients with PCOS.

Methods: PCOS and atherosclerosis data sets were downloaded from the GEO database, and their differentially expressed genes were identified. Weighted gene co-expression network analysis was used to obtain intersection genes, and then protein-protein interaction and functional enrichment analysis were performed.