Gene and non-coding RNA expression in human cumulus cells in polycystic ovary syndrome: A systematic review

Abstract

Objective

This systematic review aimed to synthesize and critically appraise human studies on coding and non-coding RNA expression in cumulus cells (CCs) from women with Polycystic Ovary Syndrome (PCOS), to map consistently dysregulated pathways, evaluate links to oocyte competence, and identify key methodological limitations.

Methods

A systematic search of five electronic databases (MEDLINE/PubMed, Embase, Web of Science, Scopus, Cochrane CENTRAL) was conducted from inception to October 25, 2025. Peer-reviewed human studies comparing CC RNA expression in PCOS versus controls, with explicit cumulus isolation and primary RNA data, were included. Study selection, quality assessment, and data extraction followed pre-defined analytic prisms focusing on cell-type specificity, oocyte maturation stage, stimulation protocol, and clinical phenotype. A qualitative synthesis was performed due to substantial clinical and methodological heterogeneity.

Results

Thirty-three studies were included. The CC transcriptome in PCOS is characterized by a coherent phenotype: pathway-level enrichment of EGFR-MAPK and PI3K-AKT/insulin signaling, an imbalanced TGF-β/oocyte-somatic cell axis, and blunted induction of cumulus expansion/extracellular matrix (ECM) genes (e.g., TNFAIP6, PTX3). Non-coding RNA profiles (miRNAs, lncRNAs, circRNAs) corroborate these findings, targeting the same pathways. These signatures are strongly modified by oocyte maturation stage (largest differences at Germinal Vesicle stage), stimulation protocols, and clinical phenotypes (e.g., insulin resistance, obesity).

Conclusion

The cumulus cell phenotype in PCOS reflects a rewired signaling environment that converges on defective matrix assembly, providing a molecular framework for impaired oocyte competence. Future research requires stage-aware, phenotype-stratified designs with standardized reporting to translate these findings into robust biomarkers for assisted reproduction.