Year: 2026
Type: article
Abstract: BACKGROUND: Ovarian cancer (OC) remains the most lethal gynecologic malignancy, primarily due to late-stage diagnosis resulting from nonspecific early symptoms. This study aims to identify novel genetic targets and elucidate the underlying mechanisms driving OC progression by integrating multi-omics datasets. METHODS: We comprehensively analyzed OC datasets from the Gene Expression Omnibus (GEO) database and applied Mendelian randomization (MR) integrating genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) data to identify OC-associated genes. Cross-analysis revealed genes co-expressed with both disease-relevant and differentially expressed genes (DEGs), followed by pathway and functional enrichment investigations. RESULTS: Sixteen significant genes were identified, including XPR1, SPINT1, NFE2L3, FGFRL1, SLC24A4, CDC42EP3, PAPLN, GRAMD1B, TMEM71, MAP1A, CD36, ADRA2A, MYL9, PPBP, SIGLEC11 and CMTM5. These genes predominantly regulate tumor immune cell activity, with CIBERSORT analysis revealing distinct immune cell distribution patterns in OC. CONCLUSIONS: Our findings provide novel insights into OC molecular mechanisms and highlight promising therapeutic targets, establishing a foundation for future research and clinical applications.
Source: SLAS TECHNOLOGY
Authors: Yue Feng, Guoyan Liu
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,070
Awards: G1065635926, G1563648064, G7690098764
OpenAlex ID: W7125429204