Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39363008", "kind": "science-record", "revision": 3, "content_hash": "sha256:65b9661664ada3ba08b82312c7f7035994cafac75a45896f9e642011dbf7ed1d", "created_at": "2026-11-01T20:29:34Z", "updated_at": "2026-11-01T22:12:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer.", "doi": "10.1038/s41598-024-73571-w", "pmid": "39363008", "authors": ["Yifan Wang", "Qi Zhang"], "journal": "Scientific reports", "year": 2024, "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.", "claims": [{"id": "claim-method", "statement": "A total of 89 algorithm combinations were performed on the prognostic signature to fit prediction models based on the leave-one-out cross-validation framework in the TCGA dataset; (2)All models were detected in four GEO cohorts (GSE13507, GSE31684, GSE32984 and GSE48276); (3)For each model, the Harrell’s concordance index (C-index) was calculated across all TCGA and GEO datasets, and the model with the highest average C-index was considered the optimal.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39363008", "locator": "sections[4] \"Development and evaluation of a prognostic CDS\" paragraph 2", "quote": "A total of 89 algorithm combinations were performed on the prognostic signature to fit prediction models based on the leave-one-out cross-validation framework in the TCGA dataset; (2)All models were detected in four GEO cohorts (GSE13507, GSE31684, GSE32984 and GSE48276); (3)For each model, the Harrell’s concordance index (C-index) was calculated across all TCGA and GEO datasets, and the model with the highest average C-index was considered the optimal."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The analyzed datasets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov) and GEO database (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39363008", "locator": "sections[21] \"notes\" paragraph 1", "quote": "The analyzed datasets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov) and GEO database (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic CDS developed by StepCox[backward] + Ridge was the optimal prognostic model, with the highest average C-index of 0.71.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39363008", "locator": "sections[10] \"Integrative machine learning algorithms developed a prognostic CDS\" paragraph 2", "quote": "As a result, the prognostic CDS developed by StepCox[backward] + Ridge was suggested as the optimal prognostic CDS with a highest average C-index of 0.71 (Fig. 2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The prognostic CDS developed by StepCox[backward] + Ridge was the optimal prognostic model, with the highest average C-index of 0.71.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39363008"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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