Integrative multi-omics analysis of druggable genes for therapeutic target identification in polycystic ovary syndrome
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrine disorders in women of reproductive age with limited targeted therapies. This study aimed to identify and prioritize potential druggable genes for PCOS through a purely computational, multi-omics approach. METHOD: We conducted an integrated in silico analysis leveraging publicly available datasets. A multi-omics Mendelian randomization (MR) framework was applied to 2,888 druggable genes. Our workflow incorporated two-sample MR for initial discovery of gene-PCOS associations with false discovery rate (FDR) correction, followed by validation using summary-data-based MR (SMR) and colocalization. Tissue-specific effects were assessed via cis-expression quantitative trait loci (cis-eQTL) MR in ovary, uterus, and blood, and epigenetic regulation via methylation QTL (mQTL) MR. The ovarian microenvironment was characterized using CIBERSORT and single-cell RNA sequencing (scRNA-seq). We also assessed whether immune cell traits mediate the causal pathway between cis-eQTLs and PCOS. Finally, we predicted potential drug targets using the Comparative Toxicogenomics Database and thereby performed molecular docking simulations. RESULTS: We identified seven FDR-significant druggable genes associated with PCOS risk: NRBP1, LGR6, GHRL, and IPP as risk-enhancing genes, and CBLN3, VIPR1, and TFRC as protective. SMR analysis confirmed that genetically predicted NRBP1 (OR = 1.5383, p = 8.67E-04), LGR6 (OR = 1.3442, p = 0.0107),…
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