Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting women of childbearing age, and we aimed to reveal its underlying molecular mechanisms. Gene expression profiles from GSE138518 and GSE155489, and single-cell RNA sequencing (scRNA-seq) data from PRJNA600740 were collected and subjected to bioinformatics analysis to identify the complex molecular mechanisms of PCOS. The expression of genes was detected by RT-qPCR. Through differential analysis, we identified 230 common differentially expressed genes (DEGs) in GSE138518 and GSE155489. GSEA results showed significant enrichment of purine metabolism and oocyte meiosis in the control group, while GSVA results indicated significant activation of ECM receptor interaction, and antigen processing and presentation in PCOS. Weighted gene co-expression network analysis revealed 7 co-expression modules, with the bisque4 module showing the highest positive correlation with PCOS. Enrichment analysis revealed that genes in the bisque4 module were mainly involved in the PI3K-Akt signaling pathway, calcium signaling pathway, and Ras signaling pathway. Pseudotime trajectory analysis of cell subpopulations revealed the potential developmental trajectory of PCOS. The gene expression consistent with the potential developmental trajectory was validated by RT-qPCR. Our study, by analyzing multiple datasets, has revealed the complex molecular network of PCOS, offering new insights into understanding its pathophysiological basis.