Abstract
BACKGROUND: Ovarian cancer is one of the most common malignancies of the female reproductive system and is associated with poor prognosis. This study aimed to utilize single-cell RNA sequencing to investigate the heterogeneity of malignant epithelial cells in ovarian cancer, focusing on their potential functions and the implications for treatment and prognosis. METHODS: Single-cell RNA sequencing data were clustered using a single-cell transcriptome clustering method, and malignant epithelial cells were identified through copy number variation analysis. The interaction patterns between different malignant subpopulations and immune/stromal cells were analyzed using cell-to-cell communication analysis. A risk score (URS) model based on the UBE2C + epithelial subpopulation was then constructed through LASSO and multivariable Cox regression. High and low URS groups were compared in terms of tumor mutational burden (TMB), survival outcomes, and drug sensitivity. Finally, the role of GTF2F2 in ovarian cancer progression was validated through gene knockdown experiments in an ovarian cancer cell line (ES-2). RESULTS: Three major malignant epithelial cell subpopulations were identified (TMSB4X + Epi, TSC22D1 + Epi, and UBE2C + Epi). The UBE2C + Epi subpopulation exhibited higher stemness and greater invasive potential. The constructed URS model effectively stratified patients into high- and low-risk groups, with the high-risk group displaying a higher TMB level (p = 0.00011). Drug sensitivity predictions indicated that osimertinib, rapamycin, and dihydrorotenone might have stronger inhibitory effects in the high-risk group, whereas ERK inhibitors were more effective in the low-risk group. Functional assays demonstrated that GTF2F2 knockdown significantly suppressed ovarian cancer cell migration and invasion. Western blot analyses further showed elevated E-cadherin and reduced N-cadherin expression, suggesting that GTF2F2 may promote epithelial-mesenchymal transition (EMT). CONCLUSION: The risk score model established in this study offers a novel framework for patient stratification and personalized therapy. Notably, the identification of the UBE2C + Epi subpopulation and key genes such as GTF2F2 highlights potential diagnostic and therapeutic targets, shedding light on the pathogenesis of ovarian cancer and paving the way for precision medicine approaches.