Abstract
OBJECTIVE: Endometriosis increases ovarian cancer (OC) risk through genetic mutations, chronic inflammation, and hormonal dysregulation, yet the underlying molecular pathways remain underexplored. This study aims to identify endometriosis-associated prognostic biomarkers in OC. METHODS: Transcriptomic and clinical data from TCGA-OC and GSE53963 were integrated for comprehensive analysis. Prognostic models were constructed using LASSO and Cox regression. Tumor microenvironment (TME) characteristics, immune checkpoints, and drug sensitivity were evaluated with ESTIMATE, CIBERSORT, TIDE, and drug sensitivity profiling. Single-cell RNA sequencing (scRNA-seq, GSE184880) was employed to explore immune and gene expression patterns. MEGF8 function was validated through in vitro assays. RESULTS: Consensus clustering identified three OC molecular subtypes (A, B, and C), with subtype B showing significantly better overall survival (P = 0.001). Subtype A exhibited a "dual malignant phenotype", characterized by enhanced cell adhesion, epithelial-mesenchymal transition (EMT), TGF-β activation, and an immunosuppressive TME. An 8-gene prognostic model, including MEGF8, effectively stratified patients into high- and low-risk groups, with high-risk patients showing poorer survival, immune evasion, and elevated stromal scores. Drug sensitivity analysis indicated that the low-risk group was more responsive to PI3K/AKT/mTOR and VEGFR inhibitors. MEGF8 was identified as a key regulator in cancer stem cells, promoting tumor progression through metabolic reprogramming and extracellular matrix remodeling. Functionally, MEGF8 knockdown suppressed OC cell proliferation and migration. CONCLUSION: This study delineated the molecular-immune landscape of OC, established an 8-gene prognostic model, and identified MEGF8 as a potential therapeutic target. The model predicts responses to immunotherapy and targeted therapies, supporting personalized OC management.