Table10_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.XLSX

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.
Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.
Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort.
Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.

背景：膀胱癌是一种常见的泌尿生殖系统恶性肿瘤，具有较高的发病率与死亡率。免疫治疗已成为极具潜力的治疗选择，但不同患者的应答率存在显著差异。糖基化（glycosylation）已被证实与肿瘤发生及免疫调控密切相关，然而当前学界对糖基化在膀胱癌中的作用及其临床意义的全面认知仍较为有限。
方法：本研究基于下载获取的癌症基因组图谱膀胱癌队列（TCGA-BLCA）数据集构建训练队列，并从湘雅医院、GEO数据库及ArrayExpress数据库获取额外数据集，包括湘雅队列（Xiangya cohort）、GSE32894、GSE48075、GSE31684、GSE69795及E-MTAB-1803，将其作为验证队列。为筛选与预后相关的糖基化相关基因，本研究开展单因素Cox回归及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator，简称LASSO）回归分析，随后构建Cox比例风险回归模型以生成风险评分模型。本研究通过卡普兰-迈耶（Kaplan-Meier）生存曲线及受试者工作特征（Receiver Operating Characteristic，简称ROC）曲线在训练队列中评估该风险评分模型的性能，并在多组验证队列中进一步验证其有效性。
结果：本研究基于训练队列中糖基化相关基因的表达模式，将患者分为两个亚组：Cluster 1与Cluster 2。预后分析显示，Cluster 2组患者的生存结局更差。该组肿瘤微环境中的免疫细胞浸润水平更高，且肿瘤免疫应答循环关键步骤的激活程度更强。本研究构建了独立的预后风险评分模型（p < 0.001），并基于该评分生成了精准的预后预测列线图（nomogram）。高糖基化风险评分组患者的肿瘤免疫细胞浸润水平更高、免疫治疗相关通路富集评分更高，且更倾向于基底样分子亚型；反之，低风险评分组的免疫细胞浸润程度极低，更倾向于管腔型分子亚型。上述研究结果在本研究的真实世界湘雅队列中得到了一致验证。
结论：本研究基于上述基因构建的多组学糖基化评分模型，可有效确认膀胱癌的肿瘤异质性，预测免疫治疗应答效果及分子亚型，为个体化治疗决策提供优化依据。