Table3_TNF Family–Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma.XLSX

Background: Tumor necrosis factor (TNF) family members play vital roles in cancer development and antitumor immune responses. However, the expression patterns, prognostic values, and immunological characteristics of TNF members in bladder carcinoma (BLCA) remain unclear. Methods: The training cohort, TCGA-BLCA, was downloaded from The Cancer Genome Atlas; another two Gene Expression Omnibus datasets (GSE13507 and GSE32894) and the Xiangya cohort (RNA-sequencing cohort collected from our hospital) were used as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to screen variables. Cox regression model and random survival forest (RSF) were used to develop the risk score, respectively. Then, we systematically correlated the TNF risk score with the tumor microenvironment (TME) cell infiltration, molecular subtypes of BLCA, and the potential value for predicting the efficacy of immunotherapy. Results: We developed two TNF-based patterns, named TNF cluster 1 and TNF cluster 2. TNF cluster 1 exhibited poorer survival outcome and an inflamed TME characteristic compared with TNF cluster 2. We then filtered out 196 differentially expressed genes between the two TNF clusters and applied the LASSO algorithm and cross-validation to screen out 22 genes to build the risk score. For risk score, we found that RSF exhibited higher efficacy than the Cox regression model, and we chose the risk score developed by RSF for the following analysis. BLCA patients in the higher risk score group showed significantly poorer survival outcomes. Moreover, these results could be validated in the external validation cohorts, including the GSE13507, GSE32894, and Xiangya cohorts. Then, we systematically correlated the risk score with TME cell infiltration and found that it was positively correlated with the infiltration of a majority of immune cells. Also, a higher risk score indicated a basal subtype of BLCA. Notably, the relationship between risk score, TME cell infiltration, and molecular subtypes could be validated in the Xiangya cohort. Conclusion: We developed and validated a robust TNF-based risk score, which could predict prognostic outcomes, TME, and molecular subtypes of BLCA. However, the value of risk score predicting the efficacy of immunotherapy needs further research.背景：肿瘤坏死因子（Tumor Necrosis Factor, TNF）家族成员在癌症发生发展以及抗肿瘤免疫应答中发挥关键作用。然而，膀胱癌（Bladder Carcinoma, BLCA）中TNF家族成员的表达模式、预后价值及免疫学特征仍未明确。 方法：训练队列TCGA-BLCA从癌症基因组图谱（The Cancer Genome Atlas, TCGA）下载；另外两项基因表达综合（Gene Expression Omnibus, GEO）数据集（GSE13507与GSE32894）以及湘雅队列（本医院收集的RNA测序队列）作为外部验证队列。采用最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）算法与交叉验证进行变量筛选。分别采用Cox回归模型与随机生存森林（Random Survival Forest, RSF）构建风险评分。随后，我们系统关联了TNF风险评分与肿瘤微环境（Tumor Microenvironment, TME）细胞浸润、膀胱癌分子亚型，以及预测免疫治疗疗效的潜在价值。 结果：我们构建了两种基于TNF的分型，分别命名为TNF聚类1与TNF聚类2。与TNF聚类2相比，TNF聚类1患者的生存结局更差，且呈现炎症型肿瘤微环境特征。我们筛选出两类TNF聚类间的196个差异表达基因，并通过LASSO算法与交叉验证进一步筛选出22个基因以构建风险评分。针对风险评分的构建，我们发现随机生存森林模型的效能优于Cox回归模型，因此选用随机生存森林构建的风险评分开展后续分析。风险评分较高的膀胱癌患者生存结局显著更差。此外，上述结果可在外部验证队列（包括GSE13507、GSE32894及湘雅队列）中得到验证。随后，我们系统关联了风险评分与肿瘤微环境细胞浸润情况，发现其与多数免疫细胞的浸润呈正相关。同时，较高的风险评分提示膀胱癌为基底样亚型。值得注意的是，风险评分与肿瘤微环境细胞浸润、分子亚型之间的关联可在湘雅队列中得到验证。 结论：我们构建并验证了一款稳健的基于TNF的风险评分，该评分可预测膀胱癌的预后结局、肿瘤微环境特征及分子亚型。然而，该风险评分预测免疫治疗疗效的价值仍需进一步研究探索。