Abstract
OBJECTIVES: Endometriosis (EM) is a chronic disease severely impacting reproductive health, with its exact cause still unclear. In-depth understanding of the etiology and pathogenesis of EM from the perspective of genetics and exploring individualized treatment strategies can improve the health and quality of life of patients. DESIGN: This study combined genetic data analysis with experimental validation to provide novel biomarkers and drug targets for the diagnosis and treatment of EM. PARTICIPANTS/MATERIALS, SETTING, AND METHODS: Whole blood cis-expression quantitative trait loci (eQTL) data were used as exposure data, and data from the FinnGen database EM1-2 and EM3-4 were used as outcomes. Summary-data-based Mendelian randomization (SMR) methods were used to select genes with causal relationship to the disease. These genes were validated through bioinformatics analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis of clinical samples, and potential diagnostic and drug targets were screened through colocalization and molecular docking. RESULTS: Through SMR analysis, seven genes were selected as potential diagnostic markers of EM, namely Eukaryotic Elongation Factor, Selenocysteine-TRNA-Specific (EEFSEC), INO80 complex subunit E (INO80E), RAP1 GTPase-activating protein (RAP1GAP), Lipid Droplet-Associated Hydrolase (LDAH), Ring Finger And SPRY Domain Containing 1 (RSPRY1), HLA Complex Group 22 (Non-Protein Coding) (HCG22), and Adenosine Kinase (ADK). Colocalization analysis showed that EEFSEC, HCG22, INO80E, and RSPRY1 could be used as potential drug targets. LIMITATIONS: SMR is limited by dependence on publicly available summary data, potential confounding biases in genetic variant-phenotype associations, the presence of underlying horizontal pleiotropy, and issues related to insufficient statistical power. Colocalization analysis cannot assess undiscovered genetic variants. The in vitro experiments in this study utilized clinical samples but were validated only at the expression level. The accuracy of molecular docking analysis largely depends on the quality of protein structures and ligands. CONCLUSIONS: The study identifies potential diagnostic markers and drug targets for EM from a genetic perspective, providing new directions for drug development and precision medicine for EM treatment.