Regulatory T cell-associated signature: predicting prognosis and immunotherapy in ovarian cancer

Background: Immunosuppression is a key feature of cancer, promoting tumor growth and progression. Regulatory T cells (Tregs) are a specialized subset of T lymphocytes that mediate immunosuppression in cancer. However, the role of Treg-related genes in ovarian cancer (OC) remains largely unexplored. Methods: To identify marker genes related to Tregs in OC, we utilized scRNA-seq analysis. Treg scores were then constructed using single-sample gene set enrichment analysis (ssGSEA) based on these markers. To identify gene modules with the strongest correlation with Treg scores in bulk RNA-seq data, we employed the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. Multiple machine learning algorithms were then used to construct risk models with superior predictive performance, which were validated using external independent datasets. A risk score was developed for each OC sample based on the optimal model to evaluate differences in prognosis, immune infiltration, pathway activity, and immunotherapy between high and low-risk groups. Results: We identified 365 genes regulating Treg activity using the WGCNA algorithm, and found that 70 of these genes were associated with the prognosis of OC based on univariate cox analysis. Using a combination of the Random Survival Forest (RSF) and Lasso algorithms, we constructed a risk model with the highest c-index based on the assigned risk scores. The model's performance was validated using four external datasets. Our analysis revealed that the low-risk group had a better prognosis, more immune cell infiltration, higher immune checkpoint expression, and significant differences in pathway enrichment and immunotherapy efficacy between the different risk groups. Conclusion: Our findings provide new insights into the role of Treg cells in the development and progression of OC and highlight the potential for developing novel Treg-targeted therapies for the treatment of this disease.