ArticleScientific reports2024
Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.Oncology letters · 2026Article
- Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation.Clinical and experimental medicine · 2026Article
- Machine learning-based programmed cell death-related index to predict prognosis and immunotherapy response in skin cutaneous melanoma.Oncology letters · 2025Article
- Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.Scientific reports · 2025Article
- A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Integrated multi-optosis model for pan-cancer candidate biomarker and therapy target discovery.Frontiers in bioinformatics · 2025Article
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Abstract
Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.
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