ArticleFrontiers in medicine2026
Exploring the biological functions of PCOS: identifying hub androgen-related genes through bioinformatics.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
Polycystic ovary syndrome (PCOS) is a prevalent reproductive endocrine disorder in women. While the role of androgens in PCOS is well-recognized, the underlying mechanisms warrant further investigation. In this study, we identified potential hub androgen-related genes (ARGs) in PCOS and established diagnostic and classification models to find novel biomarkers for PCOS therapy. Five datasets (GSE34526, GSE80432, GSE95728, GSE124226, and GSE137684) were retrieved from GEO, followed by data normalization and batch effect removal. To identify hub genes, a PPI network was constructed based on differentially expressed ARGs. Subsequently, we employed the least absolute shrinkage and selection operator (LASSO) regression analysis and random forest (RF) algorithm to screen hub ARGs. Besides, hub ARGs associated with PCOS were determined by integrating the results of the three algorithms. Additionally, a nomogram was constructed using these hub ARGs to predict the risk of PCOS development. We also investigated the classification of ARG molecular subtypes and assessed immune characteristics and gene expression profiles in different subtypes. Lastly, RT-qPCR was utilized to validate the reliability of the hub genes. A total of 91 ARGs were retrieved from the GSEA website. This study included 26 healthy and 34 PCOS samples. Using the LASSO identified 13 key ARGs, RF identified 10 crucial ARGs, and PPI identified 19 pivotal ARGs. Integration of three methods identified four hub ARGs (
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