Gene association study between polycystic ovary syndrome and metabolic syndrome: a transcriptomic analysis and machine learning approach.

BACKGROUND: Patients with polycystic ovary syndrome (PCOS) often experience a range of metabolic comorbidities, suggesting a potential association between PCOS and metabolic syndrome (MetS). However, this potential link has not yet been fully elucidated. METHODS: This study employed transcriptomic analysis and machine learning techniques to identify key genes and signaling pathways associated with both PCOS and MetS. Differentially expressed genes (DEGs) were analyzed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were used to identify hub genes, and their diagnostic potential was assessed using Receiver Operating Characteristic (ROC) curves. RESULTS: A total of 373 DEGs were identified in PCOS, and 516 DEGs in MetS, with 14 overlapping genes considered critical for both conditions. Six hub genes including Dihydropyrimidinase-like 4(DPYSL4), FBJ osteosarcoma oncogene(FOS), Jun dimerization protein 2(JDP2), Stearoyl-CoA desaturase(SCD), Tribbles pseudokinase 1(TRIB1), Zinc finger protein 331(ZNF331) were selected through various machine learning methods. Enrichment analyses revealed that these genes significantly influence apoptosis, TNF signaling, and lipid metabolism pathways, highlighting their roles in the pathogenesis of PCOS and MetS. CONCLUSIONS: These findings suggest that these genes may serve as potential therapeutic targets for the prevention and treatment of comorbidities in patients with PCOS and MetS. The identified hub genes play significant roles in the development of PCOS and MetS, underscoring the need for further research on these genes. This study offers insights into molecular interactions and potential biomarkers for early diagnosis and therapeutic targets for these syndromes. Future studies should aim to validate these findings in larger cohorts to enhance their clinical applicability. CLINICAL TRIAL NUMBER: Not applicable.