Abstract：
Bladder urothelial carcinoma (BLCA), a prevalent malignant neoplasm affecting the human urinary system, is frequently linked with an unfavorable prognosis in a significant proportion of individuals. More effective and sensitive markers are needed to provide a reference for prognostic judgment. We obtained RNA sequencing data and clinical information of individuals from TCGA, and 133 copper metabolism-related genes from literature. Prognostic genes were evaluated by univariate/multivariate Cox regression analysis and LASSO analysis, and a risk-scoring model was established and validated in the GEO dataset. The CIBERSORT method was utilized to explore immune cell infiltration in BLCA individuals. In addition, tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to verify whether the model can foretell the response of BLCA individuals to immunotherapy. We successfully constructed an 8-gene risk scoring model to foretell individuals\' overall survival, and the model performed well in TCGA training and GEO validation cohorts. Lastly, a nomogram containing clinical parameters and risk scores was constructed to help individualized result prediction for individuals. Calibration curves demonstrated a high degree of concordance between the observed and projected survival durations, attesting to its exceptional predictive accuracy. Analysis utilizing CIBERSORT unveiled elevated levels of immune cell infiltration in individuals classified as low risk. TIDE and IPS analyses substantiated that low-risk individuals exhibited a more favorable response to immunotherapy. In summary, the model held immense potential for stratifying the risk of survival and guiding tailored treatment approaches for individuals with BLCA, thereby offering valuable insights for personalized therapeutic interventions.
摘要：
膀胱尿路上皮癌(BLCA),一种影响人类泌尿系统的普遍恶性肿瘤，通常与相当大比例的个体的不良预后有关。需要更有效和敏感的标志物来为预后判断提供参考。我们从TCGA获得了个体的RNA测序数据和临床信息，和133个铜代谢相关基因来自文献。通过单变量/多变量Cox回归分析和LASSO分析评估预后基因,建立了风险评分模型,并在GEO数据集中进行了验证。CIBERSORT方法用于探索BLCA个体的免疫细胞浸润。此外,利用肿瘤免疫功能障碍和排斥(TIDE)和免疫表型(IPS)来验证该模型是否可以预测BLCA个体对免疫治疗的反应.我们成功构建了一个8基因风险评分模型来预测个体的总体生存率，模型在TCGA训练和GEO验证队列中表现良好。最后,我们构建了包含临床参数和风险评分的列线图,以帮助个体进行个体化结果预测.校准曲线显示了观察到的和预测的生存持续时间之间的高度一致性，证明了其卓越的预测准确性。利用CIBERSORT的分析揭示了被归类为低风险的个体中免疫细胞浸润水平的升高。TIDE和IPS分析证实，低风险个体对免疫疗法表现出更有利的反应。总之,该模型具有巨大的潜力，可以对生存风险进行分层，并指导针对BLCA患者的量身定制的治疗方法，从而为个性化治疗干预提供有价值的见解。
