Abstract
BACKGROUND: CD8T cells, also known as cytotoxic T lymphocytes, play a key role in the tumor immune microenvironment (TME) and immune response. The aim of this study was to explore the potential role of CD8T cell-associated biomarkers in predicting prognosis and immunotherapy efficacy in ovarian cancer. METHODS: The single-cell sequencing data from the EMTAB8107 cohort were used to identify CD8 T-cell subtypes. The TCGA-OV cohort was involved in constructing a machine learning-based CD8T cell-associated index (CCAI). Additionally, independent ovarian cancer cohorts GSE26712 and GSE26193 were used to validate the predictive validity of CCAI. Multifactorial Cox regression and ROC analysis were applied to assess CCAI. The STRING database was used to clarify the interactions of CD8 T-cell-associated molecules. Furthermore, immune landscape analysis was performed using CIBERSORT, ssGSEA, TIMER, and ESTIMATE algorithms. Tumor mutation burden (TMB) analysis and drug sensitivity analysis were used to evaluate the potential predictive value of CCAI. RESULTS: The CCAI, comprising LRP1, PLAUR, OGN, TAP1, ISG20, CXCR4, IL2RG, LCK, and CD3G, serves as a reliable prognostic marker for ovarian cancer patients, demonstrating robust predictive accuracy across various patient cohorts. Notably, individuals with low CCAI tend to exhibit immunoinflammatory tumor characteristics. CONCLUSIONS: The developed CCAI serves as a promising prognostic biomarker for ovarian cancer, accurately predicting patient outcomes. Additionally, it differentiates between patients with distinct immune landscape profiles. This insight enables personalized treatment strategies and facilitates the exploration of underlying mechanisms involving CCAI-related molecules.