Bladder cancer is one of the three major tumors in urology, and its diagnosis and treatment is a global problem. As a newly discovered mechanism of cell death, disulfidptosis is expected to help diagnose and treat bladder cancer patients. Firstly, we obtained the data required for this study in an open database through a common process. Next, we analyzed this study through bioinformatics techniques. Through differential analysis and COX regression analysis, we obtained 4 prognostically relevant differential genes. These four prognostically relevant differential genes will be used by us for Lasso regression for further screening to obtain model-related genes and output model formulas. After obtaining the prognostic model, we verified the predictive power of the model. And in the HPA database, we verified the immunohistochemistry of model-related genes. In order to personalize treatment, we analyzed the tumor microenvironment and immune cell infiltration of bladder cancer patients with different risk scores. The oncoPredict package was used to predict the sensitivity of chemotherapy drugs in patients with different risk groups, and its results have some reference value for guiding clinical use. Through the nomogram, we can calculate the survival rate of patients more accurately. In this study, we obtained a prognostic model containing 3 disulfidptosis-related genes (NDUFA11, RPN1, SLC3A2). Through functional enrichment analysis and immune-related analysis, we found that patients in the high-risk group were candidates for immunotherapy. The results of drug susceptibility analysis can provide more accurate treatment for patients with bladder cancer.

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膀胱癌二硫键相关基因的预后预测及免疫治疗综合分析

膀胱癌是泌尿外科三大肿瘤之一，其诊断和治疗是一个世界性难题。作为一种新发现的细胞死亡机制，二硫下垂有望帮助膀胱癌患者的诊断和治疗。首先，我们通过通用流程在开放数据库中获取了本研究所需的数据。接下来，我们通过生物信息学技术分析了这项研究。通过差异分析和COX回归分析，我们获得了4个与预后相关的差异基因。这四个与预后相关的差异基因将被我们用于Lasso回归进一步筛选以获得模型相关基因并输出模型公式。获得预后模型后，我们验证了模型的预测能力。并且在HPA数据库中，我们验证了模型相关基因的免疫组化。为了个性化治疗，我们分析了不同风险评分的膀胱癌患者的肿瘤微环境和免疫细胞浸润。利用oncoPredict包预测不同危险人群患者对化疗药物的敏感性，其结果对指导临床使用具有一定的参考价值。通过列线图，我们可以更准确地计算出患者的生存率。在本研究中，我们获得了包含3个二硫下垂相关基因（NDUFA11、RPN1、SLC3A2）的预后模型。通过功能富集分析和免疫相关分析，我们发现高危组患者是免疫治疗的候选者。药敏分析结果可以为膀胱癌患者提供更准确的治疗。