Machine learning developed a CD8⁺ exhausted T cells signature for predicting prognosis, immune infiltration and drug sensitivity in ovarian cancer.

ABSTRACT: CD8⁺ exhausted T cells (CD8⁺ T_ex) played a vital role in the progression and therapeutic response of cancer. However, few studies have fully clarified the characters of CD8⁺ T_ex related genes in ovarian cancer (OC). The CD8⁺ T_ex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset. Several immunotherapy benefits indicators, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore (IPS), TMB score and tumor escape score, were used to explore performance of TRPS in predicting immunotherapy benefits of OC. The TRPS constructed by Enet (alpha = 0.3) method acte

SUBMITTER: Chen R 

PROVIDER: S-EPMC10925064 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature