Table_1_Novel T-cell signature based on cell pair algorithm predicts survival and immunotherapy response for patients with bladder urothelial carcinoma.xlsx

BackgroundT-cell–T-cell interactions play important roles in the regulation of T-cells’ cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. There is a lack of comprehensive studies of T-cell types in bladder urothelial carcinoma (BLCA) and T-cell-related signatures for predicting prognosis and monitoring immunotherapy efficacy.MethodsMore than 3,400 BLCA patients were collected and used in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 19 T-cell types. A cell pair algorithm was applied to construct a T-cell-related prognostic index (TCRPI). Survival analysis was performed to measure the survival difference across TCRPI-risk groups. Spearman’s correlation analysis was used for relevance assessment. The Wilcox test was used to measure the expression level difference.ResultsNineteen T-cell types were collected; 171 T-cell pairs (TCPs) were established, of which 26 were picked out by the least absolute shrinkage and selection operator (LASSO) analysis. Based on these TCPs, the TCRPI was constructed and validated to play crucial roles in survival stratification and the dynamic monitoring of immunotherapy effects. We also explored several candidate drugs targeting TCRPI. A composite TCRPI and clinical prognostic index (CTCPI) was then constructed, which achieved a more accurate estimation of BLCA’s survival and was therefore a better choice for prognosis prediction in BLCA.ConclusionsAll in all, we constructed and validated TCRPI based on cell pair algorithms in this study, which might put forward some new insights to increase the survival estimation and clinical response to immune therapy for individual BLCA patients and contribute to the personalized precision immunotherapy strategy of BLCA.

背景：T细胞间的相互作用在调控T细胞细胞毒性功能中发挥着重要作用，进而影响免疫疗法的抗肿瘤效果。目前，对膀胱尿路上皮癌（BLCA）中T细胞类型以及预测预后和监测免疫疗法疗效的T细胞相关特征的综合研究尚显不足。方法：本研究收集了超过3,400例BLCA患者数据。采用ssGSEA算法计算了19种T细胞类型的浸润水平。运用细胞对算法构建了T细胞相关预后指数（TCRPI）。通过生存分析评估了TCRPI风险组间的生存差异。采用Spearman相关性分析进行相关性评估。Wilcox检验用于测量表达水平差异。结果：收集了19种T细胞类型；建立了171对T细胞（TCPs），其中26对通过最小绝对收缩和选择算子（LASSO）分析筛选。基于这些TCPs，构建并验证了TCRPI，其在生存分层和免疫疗法效果的动态监测中发挥着至关重要的作用。我们还探索了针对TCRPI的候选药物。随后构建了综合TCRPI和临床预后指数（CTCPI），该指数实现了对BLCA生存的更精确估计，因此成为BLCA预后预测的更优选择。结论：总体而言，本研究基于细胞对算法构建并验证了TCRPI，这或许为提高个体BLCA患者的生存估计和对免疫疗法的临床反应提供新的视角，并为BLCA的个性化精准免疫治疗策略贡献力量。