Urology and Nephrology Center, Department of Urology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Sci Rep. 2024 Oct 3;14(1):22976. doi: 10.1038/s41598-024-73571-w.
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.
膀胱癌是男性中第四常见的恶性肿瘤，预后较差。程序性细胞死亡 (PCD) 在许多生物过程和癌症的免疫治疗反应中发挥着至关重要的作用。细胞死亡特征 (CDS) 是一种新的基因特征，全面考虑了 15 种程序性细胞死亡模式的特征，这些特征可能会影响癌症患者的预后和免疫治疗效果。我们采用包括 10 种算法的综合机器学习程序，使用 TCGA、GSE13507、GSE31684、GSE32984 和 GSE48276 数据集构建了一个预后 CDS。免疫表型评分、肿瘤内异质性 (ITH)、肿瘤免疫功能障碍和排除 (TIDE) 评分以及五个免疫治疗队列用于评估 CDS 在免疫治疗反应中的预测价值。使用 StepCox[向后] + Ridge 算法构建的预后 CDS 被认为是预测膀胱癌患者总生存率的最佳预后模型。在 TCGA 队列中，该 CDS 在预测膀胱癌患者 3 年、5 年和 7 年的总生存率的 AUC 分别为 0.740、0.763 和 0.820，具有稳定而强大的性能。此外，CDS 评分是膀胱癌患者总生存率的独立危险因素。低 CDS 评分的膀胱癌患者具有更高比例的免疫激活细胞、更高的 PD1&CTLA4 免疫表型评分、更高的 TMB 评分、更低的 TIDE 评分、更低的免疫逃逸评分、更低的 ITH 评分和更低的癌症相关特征评分。在接受免疫治疗的泛癌患者中，非应答者的 CDS 评分更高。我们构建了一种新的预后 CDS，可作为预测术后膀胱癌患者预后和泛癌免疫治疗效果的指标。低 CDS 评分提示预后较好且免疫治疗获益更高。
