Abstract: Objective This study aims to explore the construction of a risk model based on DNA Damage Repair Genes （DDRGs） that can accurately predict the survival prognosis and immunotherapy outcomes of bladder cancer patients. Methods Initially, RNA sequences and clinical information downloaded from the TCGA-BLCA dataset were analyzed through univariate Cox, Least Absolute Shrinkage and Selection Operator （LASSO）, and multivariate Cox analyses to identify DNA Damage Repair Genes （DDRGs） and construct a prognostic risk model. Subsequently, the Kaplan-Meier survival curve was utilized to assess survival differences, the Receiver Operating Characteristic （ROC） curve to validate model performance, and nomograms were constructed incorporating clinical features for further validation.Lastly, clinical features were subjected to stratified analysis, and the treatment effects of immune status and Immune Checkpoint Inhibitors （ICIs） were further evaluated through Gene Set Enrichment Analysis （GSEA）. Results The risk model constructed with 11 DNA damage repair-related genes showed that bladder cancer patients with high-risk scores had significantly lower survival rates than those with low-risk scores. Conclusion The prognostic risk model constructed based on DNA damage repair in this study can effectively predict the survival prognosis of bladder cancer patients and their responsiveness to treatment with Immune Checkpoint Inhibitors （ICIs）.

Key words: bladder cancer, DNA damage repair, prognostic model, immunotherapy

摘要： 目的 本研究旨在探索构建一个基于DNA损伤修复相关基因（DDRGs）的预测膀胱癌患者生存预后及免疫治疗效果的风险模型。方法 首先,从TCGA-BLCA数据集下载的RNA序列及临床信息,通过单变量Cox、最小绝对收缩与选择算法（LASSO）及多变量Cox分析,筛选出与DNA损伤修复相关的基因（DDRGs）,构建预后风险模型;利用Kaplan-Meier生存曲线评估生存差异,通过接收者操作特征（ROC）曲线验证模型性能,并结合临床特征构建列线图进行验证。最后,对临床特征进行了分层分析,并进一步通过基因集富集分析（GSEA）、评估免疫状态和免疫检查点抑制剂（ICIs）的治疗效果。结果 通过构建11个DNA损伤修复相关基因的风险模型,显示高风险得分的膀胱癌患者生存率明显低于低风险得分患者。免疫状态的分析揭示了高风险与低风险组之间存在显著差异,且低风险组患者对免疫检查点抑制剂的治疗效果更佳。结论 基于DNA损伤修复构建的预后风险模型能有效预测膀胱癌患者的生存预后及其对免疫检查点抑制剂治疗的响应性。

关键词: 膀胱癌, DNA损伤修复, 预后模型, 免疫治疗