Multi-omics and functional validation identify CLDN4 as a therapeutic target in ovarian cancer

Ovarian cancer (OC) is a highly lethal malignancy with limited diagnostic biomarkers and therapeutic targets. This study aimed to identify robust diagnostic signatures for OC, analyze their immune correlations, and identify causal genes. Multiple transcriptomic datasets (GEO, TCGA, GTEx) were integrated. Differentially expressed genes (DEGs) and WGCNA were applied to prioritize candidate genes. A machine learning pipeline (113 models) was constructed and validated across independent cohorts. Immune infiltration was quantified via CIBERSORT. Summary Mendelian Randomization (SMR) analysis integrating blood eQTL and FinnGen GWAS data was performed for causal inference. An eight-gene signature (KDR, CLDN4, CAV1, BMP2, CLDN7, SNCA, FGF13, AGTR1) demonstrated excellent diagnostic performance. CAV1, FGF13, and KDR were positively correlated with M2 macrophage infiltration, suggesting roles in immune evasion. SMR analysis established a positive causal relationship between CLDN4 expression and OC risk ( P < 0.05). These findings offer new insights into OC pathogenesis and highlight CLDN4 as a promising biomarker and potential therapeutic candidate. The triangulated evidence—from transcriptomic association, genetic causal inference, to functional validation—provides a robust foundation for future translational research.