Multilevel Transcriptomic Association Analysis Reveals Key Genes and Potential Mechanisms in Endometrial, Ovarian, and Cervical Cancers

Abstract Objective: This study aims to investigate the genetic associations of endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC), identify potential key genes using multiple genomic analysis approaches, and analyze their roles in cancer development. Methods: We integrated large-scale genome-wide association study (GWAS) data from Jiang L et al. and Zhou W et al., combined with blood eQTL data. We employed S-PrediXcan, SMR, and mBAT-combo to assess gene associations with EC, OC, and CC. Additionally, RNA sequencing data were used to analyze the expression levels of key genes across different tissues, followed by functional enrichment analysis to explore their potential biological functions. Results: Through S-PrediXcan, SMR, and mBAT-combo analyses, we identified 690, 620, and 624 genes associated with OC, CC, and EC, respectively. Among them, 79, 59, and 61 genes were consistently significant across all three methods, suggesting their crucial roles in cancer development. Furthermore, we identified multiple comorbidity-related genes, including SPX, which exhibited a negative association with OC, CC, and EC. Transcriptomic analysis revealed that 26 key genes displayed significant expression differences between tumor and normal tissues. Functional enrichment analysis further identified the molecular pathways potentially involved. Conclusion: This study identified a set of key genes associated with EC, OC, and CC and suggested that SPX may play a protective role in cancer development. The integration of multilevel genetic and transcriptomic analyses provides new insights into the molecular mechanisms underlying gynecological malignancies and offers potential biomarkers for future precision medicine research.