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

Sep 21, 2026

Uploaded by Miles

Programmed cell death (PCD) signatures are investigated as a biomarker to forecast prognosis and immunotherapy benefit in postoperative bladder cancer. A cell death signature (CDS) was built using integrative machine learning across multiple TCGA and GEO datasets, then evaluated with immunophenoscore, intratumor heterogeneity (ITH), TIDE, and immunotherapy cohort analyses. StepCox (backward) plus Ridge produced an optimal model with strong overall survival prediction and links between CDS score, immune activation, and response status.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Bladder cancer burden and current treatment challenges

Need for biomarkers for prognosis and immunotherapy response

Programmed cell death patterns and biological background

Study aim and approach

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer Yifan Wang & Qi Zhang􀀍 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 astable and powerful performance in predicting overall survival of bladder cancer patients with the AUCsat 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 hada 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 pancancer. Low CDS score indicated a better prognosis and immunotherapy benefits. Keywords Programmed cell death, Machine learning, Bladder cancer, Prognostic signature, Immunotherapy Bladder cancer (BC) is the fourth most common malignancy in men1. An estimated of 573,278 cases are initially diagnosed with bladder cancer and 212,536 patients died of this disease worldwide in 20202. Despite some principal risk factors, including tobacco smoking3, have been identified for bladder cancer, the mechanisms mediating its development and progression have not been elucidated. Currently, treatment approaches for bladder cancer include surgery, chemotherapy and immunotherapy. However, the clinical outcome of postoperative bladder cancer cases remains unsatisfactory, resulting in the 5-year overall survival rates being flat for 30 years4. High recurrence and resistance to chemotherapy are major challenges in the management of the prognosis of bladder cancer patients5. At present, the TNM staging system is the major approach for predicting the prognosis of bladder cancer patients. However, the clinical outcome of bladder cancer patients with the same stage is also very different. Moreover, there is a lack of effective biomarker for the prognosis of bladder cancer patients in clinical practice. Immunotherapy offers more life-extending possibilities for bladder cancer patients with unresectable tumors6. However, immunotherapy also has certain limitations, which are mainly reflected in the uneven efficacy, the possibility of false progression of tumor, the possibility of immune-related adverse events, and the need for individualized treatment7. Due to the heterogeneity of bladder cancer, the response to Urology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China. 􀀍 email: [Qizhang2020@outlook.com](Qizhang2020@outlook.com) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) immunoth

FAQs

What is the main objective of the study on bladder cancer?

To construct a programmed cell death (PCD) cell death signature that can predict postoperative clinical outcomes and immunotherapy benefits in bladder cancer patients.

How was the prognostic cell death signature (CDS) developed and assessed?

An integrative machine learning pipeline using multiple datasets was used to build CDS, and predictive value was evaluated using immunophenoscore, ITH, TIDE scores, and immunotherapy cohorts.

What does a low CDS score indicate?

Low CDS score is associated with better prognosis and improved immunotherapy benefits, alongside immune-activated profiles such as higher PD1/CTLA4 immunophenoscore and lower immune escape and heterogeneity.

Sep 21, 2026

Uploaded by Miles