Transcriptome Analysis Unravels CD4+ T-Cell and Treg-Cell Differentiation in Ovarian Cancer

Background: Ovarian cancer ranks as the fifth leading cause of cancer-related mortality among women worldwide. Owing to its insidious onset and lack of early symptoms, over 70% of patients are diagnosed at advanced stages. Methods: This study provides a comprehensive transcriptomic analysis of tumor-infiltrating CD4+ T cells in ovarian cancer, highlighting regulatory T cells (Tregs) as the dominant subset. By integrating seven multicenter ovarian cancer single-cell RNA-seq datasets, a robust metadata resource was created for detailed Treg investigation. Using the BayesPrism algorithm, Treg scores from TCGA bulk RNA-seq data enabled patient stratification into high and low Treg groups. These findings were further validated through survival analyses across five independent bulk RNA-seq cohorts. We experimentally validated the inhibitory role of Tregs in modulating CD8+ T-cell activity in ovarian cancer. Results: We conducted an in-depth investigation into the clustering patterns, differentiation trajectories, intercellular interactions, and enrichment profiles of tumor-infiltrating T cells in ovarian cancer. Among the seven functionally defined subclusters (C1–C7), we delineated two distinct “terminal states” of CD4+ T-cell differentiation: FOXP3+ regulatory T cells and STMN1+ proliferative T cells. The OCSCDs dataset comprises seven datasets totaling 137,648 single cells. Using the TCGA dataset, we quantified the proportion of tumor-infiltrating regulatory T cells (Tregs) in OCSCDs through the BayesPrism algorithm and performed survival analyses across five independent bulk RNA-seq datasets from different platforms. Conclusions: Our results establish a framework for studying Treg biology in ovarian cancer and these cells may be become an important point in the field of immunotherapy.