A survival-informed CD8⁺ T cell-associated gene signature and its prognostic relevance in clear cell renal cell carcinoma.
- 1Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- 2Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Abstract
Clear cell renal cell carcinoma (ccRCC) is characterized by marked clinical and immunological heterogeneity, and conventional clinicopathological variables do not fully capture patient survival risk. CD8+ T cells play a central role in antitumor immunity, yet their functional states can encompass both effective cytotoxic responses and dysfunctional immune programs. This study aimed to derive a survival-informed CD8+ T cell-associated gene signature and evaluate its prognostic associations, cellular expression, and relationship to immune dysfunction. Tumor-infiltrating CD8+ T cells from GSE178481 were analyzed using Semi-supervised Iterative Deep Learning for Identifying Single-cell High-Risk Populations (SIDISH) with survival information from The Cancer Genome Atlas (TCGA) training cohort. Candidate genes underwent cell-level differential expression analysis, sample-level pseudobulk screening, in silico perturbation, univariable Cox screening and benchmarking of 246 machine-learning model combinations. The final risk score was assessed for survival stratification, independent prognostic value, nomogram performance, pathway enrichment, tumor microenvironment features, and survival associations in a programmed cell death protein 1 (PD-1) blockade-treated cohort. SIDISH identified a subset of high-risk CD8+ T cells and corresponding candidate marker genes for subsequent model construction. The selected elastic net (α =0.8) plus least absolute shrinkage and selection operator (LASSO) Cox regression model generated a multigene risk score that consistently stratified overall survival (OS) in the TCGA training set, TCGA validation set, E-MTAB-1980 cohort, and CPTAC cohort. Higher risk scores were associated with advanced stage and grade and remained independently prognostic after adjustment for clinicopathological covariates in TCGA and E-MTAB-1980 cohorts. Nomograms incorporating the risk score improved individualized prediction of OS and progression-free survival (PFS). The risk score showed potential associations with immune-related pathways, metabolic pathways, and the tumor immune microenvironment, and was also associated with prognosis in the CheckMate 025 cohort. This survival-informed CD8+ T cell-associated signature was associated with prognosis in ccRCC and showed potential associations with immune features. These findings suggest that integrating single-cell immune states with bulk transcriptomic survival modeling may help identify prognosis-related signatures, although further validation is needed.
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BibTeX
@article{wang2026survivalinformed,
title = {{A survival-informed CD8⁺ T cell-associated gene signature and its prognostic relevance in clear cell renal cell carcinoma.}},
author = {Hui Wang and Fan Li and Yi Lu and Lifeng Ding},
journal = {Translational cancer research},
year = {2026},
month = sep,
doi = {10.21037/tcr-2026-1965},
url = {https://europepmc.org/article/MED/42997734},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A survival-informed CD8⁺ T cell-associated gene signature and its prognostic relevance in clear cell renal cell carcinoma.
AU - Hui Wang
AU - Fan Li
AU - Yi Lu
AU - Lifeng Ding
PY - 2026
DA - 2026/09/28
JO - Translational cancer research
DO - 10.21037/tcr-2026-1965
UR - https://europepmc.org/article/MED/42997734
AB - Clear cell renal cell carcinoma (ccRCC) is characterized by marked clinical and immunological heterogeneity, and conventional clinicopathological variables do not fully capture patient survival risk. CD8+ T cells play a central role in antitumor immunity, yet their functional states can encompass both effective cytotoxic responses and dysfunctional immune programs. This study aimed to derive a survival-informed CD8+ T cell-associated gene signature and evaluate its prognostic associations, cellular expression, and relationship to immune dysfunction. Tumor-infiltrating CD8+ T cells from GSE178481 were analyzed using Semi-supervised Iterative Deep Learning for Identifying Single-cell High-Risk Populations (SIDISH) with survival information from The Cancer Genome Atlas (TCGA) training cohort. Candidate genes underwent cell-level differential expression analysis, sample-level pseudobulk screening, in silico perturbation, univariable Cox screening and benchmarking of 246 machine-learning model combinations. The final risk score was assessed for survival stratification, independent prognostic value, nomogram performance, pathway enrichment, tumor microenvironment features, and survival associations in a programmed cell death protein 1 (PD-1) blockade-treated cohort. SIDISH identified a subset of high-risk CD8+ T cells and corresponding candidate marker genes for subsequent model construction. The selected elastic net (α =0.8) plus least absolute shrinkage and selection operator (LASSO) Cox regression model generated a multigene risk score that consistently stratified overall survival (OS) in the TCGA training set, TCGA validation set, E-MTAB-1980 cohort, and CPTAC cohort. Higher risk scores were associated with advanced stage and grade and remained independently prognostic after adjustment for clinicopathological covariates in TCGA and E-MTAB-1980 cohorts. Nomograms incorporating the risk score improved individualized prediction of OS and progression-free survival (PFS). The risk score showed potential associations with immune-related pathways, metabolic pathways, and the tumor immune microenvironment, and was also associated with prognosis in the CheckMate 025 cohort. This survival-informed CD8+ T cell-associated signature was associated with prognosis in ccRCC and showed potential associations with immune features. These findings suggest that integrating single-cell immune states with bulk transcriptomic survival modeling may help identify prognosis-related signatures, although further validation is needed.
SN - 2218-676X
SN - 2219-6803
ER -