Numerous studies have suggested that ferroptosis plays a significant role in the development of polycystic ovary syndrome (PCOS), but the mechanism remains unclear.
In this study, we explored the role of ferroptosis-related genes in the pathogenesis of PCOS using a comprehensive bioinformatics method. First, we downloaded several Gene Expression Omnibus (GEO) datasets and combined them into a meta-GEO dataset. Differential expression analysis was performed to screen for significant ferroptosis-related genes between the normal and PCOS samples. The least absolute shrinkage selection operator regression and support vector machine–recursive feature elimination were used to select the best signs to construct a PCOS diagnostic model. Receiver operating characteristic curve analysis and decision curve analysis were applied to test the performance of the model. Finally, a ceRNA network-related ferroptosis gene was constructed.
Five genes, namely, NOX1, ACVR1B, PHF21A, FTL, and GALNT14, were identified from 10 differentially expressed ferroptosis-related genes to construct a PCOS diagnostic model. Finally, a ceRNA network including 117 lncRNAs, 67 miRNAs, and five ferroptosis-related genes was constructed.
Our study identified five ferroptosis-related genes that may be involved in the pathogenesis of PCOS, which may provide a novel perspective for the clinical diagnosis and treatment of PCOS.