Integrated Genomic Analysis Reveals New Diagnostic Biomarkers and Immune Mechanisms for Polycystic Ovary Syndrome

Objective: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder associated with metabolic dysregulation and chronic inflammation. This study employed bioinformatics approaches to analyze the roles of neutrophil extracellular traps (NETs)-related genes (NETRGs) and mitochondria-related genes (MRGs) in PCOS pathogenesis. Methods: Through differential expression analysis, enrichment studies, and machine learning models, we identified 18 differentially expressed neutrophil extracellular trap- and mitophagy-related genes (NETMRDEGs), such as S100A9, MYH9 , and ATG5 . Functional enrichment revealed their involvement in IL-17 signaling and neutrophil chemotaxis. A LASSO regression model incorporating these genes demonstrated high diagnostic accuracy (AUC > 0.9). Immune infiltration analysis highlighted activated B cells and effector memory CD8 + T cells as key contributors. Moreover, protein-protein interaction (PPI) networks further elucidated functional synergies among NETMRDEGs. Results: The study identified distinct molecular clusters in PCOS patients based on the expression of NETMRDEGs. Cluster 2 exhibited higher immune infiltration (eg, gamma delta T cells and eosinophils) and severe metabolic dysfunction. The LASSO model achieved superior diagnostic performance (AUC: 0.93) compared to traditional biomarkers such as testosterone (AUC: 0.68). Experimental validation in a PCOS mouse model confirmed elevated expression of hub genes ( S100A9, MYH9 , and ATG5 ) and increased granulosa cell apoptosis. Conclusion: The interaction between NETRGs and MRGs forms a “dual-engine” mechanism driving PCOS pathogenesis. This study proposed a novel diagnostic model and therapeutic targets (eg, DNase I and urolithin A), advancing precision medicine for PCOS. Keywords: polycystic ovary syndrome, neutrophil extracellular traps, mitochondria-related genes, immune infiltration, diagnostic model