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

ABSTRACT:

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

SUBMITTER: Xu H 

PROVIDER: S-EPMC12522301 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature