Aller au contenu principal
Accès ouvert déclaré 2025 article

Integrated transcriptomic and co-expression network analysis identifies immune-metabolic biomarkers of polycystic ovary syndrome in granulosa cells

4Citations signalées — pas une note de qualité
6Institutions déclarées
2Pays d’affiliation déclarés

Résumé fourni par la source

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Integrated transcriptomic and co-expression network analysis identifies immune-metabolic biomarkers of polycystic ovary syndrome in granulosa cells
Date Crossref
10/11/2025
Éditeur
Springer Science and Business Media LLC
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Ovarian function and disordersLipid metabolism and disordersReproductive System and Pregnancy

BNTIC News n’est pas le producteur de ces données. Recherche à la demande dans Crossref et Europe PMC, sans clé ; OpenAlex reste optionnel. Aucun service payant requis, aucune réponse conservée. Sources et limites.