Integrated transcriptomic and co-expression network analysis identifies immune-metabolic biomarkers of polycystic...

Polycystic ovary syndrome (PCOS) is a prevalent endocrine-metabolic disorder characterized by hyperandrogenism, ovulatory dysfunction, and metabolic abnormalities. Despite increasing recognition of immune and metabolic dysregulation in its pathogenesis, the cell-specific molecular mechanisms, particularly within granulosa cells, remain poorly understood. This study aimed to elucidate the transcriptomic landscape and regulatory pathways of granulosa cells in PCOS using integrative bioinformatics and experimental validation.

We analyzed three granulosa cell transcriptomic datasets (GSE10946, GSE34526, and GSE80432) and identified 184 differentially expressed genes in PCOS. Through weighted gene co-expression network analysis (WGCNA), we pinpointed 29 key genes, of which CLDN11, HLA-DMA, TAB3, COLQ, and LYN were prioritized based on semantic similarity and functional enrichment. These genes demonstrated robust diagnostic potential using Least Absolute Shrinkage and Selection Operator (LASSO) and artificial neural network (ANN) models. Functional analyses revealed their involvement in immune and metabolic signaling, including IL-17, MAPK, mTOR, AMPK, and PPAR pathways. In vitro models mimicking hyperandrogenism, insulin resistance, and inflammation confirmed condition-specific expression of these genes, with synergistic upregulation observed under combined stimuli, suggesting convergent regulation by multiple pathological cues in PCOS.

Our findings highlight granulosa cells as central mediators of immune-metabolic disruption in PCOS and identify CLDN11, HLA-DMA, TAB3, COLQ, and LYN as potential biomarkers and regulatory targets. The integrative approach combining bioinformatics and in vitro validation provides new insights into the pathophysiology of PCOS and supports future development of cell-specific diagnostic and therapeutic strategies.