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

Sci Rep. 2024-03; 
Rujun Chen, Yicai Zheng, Chen Fei, Jun Ye, He Fei
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Molecular Biology Reagents … A pcDNA3.1 plasmid encoding the human ARL6IP5 and empty vector was purchased from GenScript (Nanjing, China). Lipofectamine 3000 transfection reagent (Invitrogen, Thermo … Get A Quote

摘要

CD8 exhausted T cells (CD8 T) played a vital role in the progression and therapeutic response of cancer. However, few studies have fully clarified the characters of CD8 T related genes in ovarian cancer (OC). The CD8 T 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 acted a... More

关键词

CD8+ Tex, Immunotherapy, Machine learning, Ovarian cancer, Prognostic signature