Table_1_Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes.xls

BackgroundThere has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA).
MethodsRNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA.
ResultsA set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6–19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit.
ConclusionThe present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.

**背景** 目前尚无基于免疫相关基因（immune-related genes, IRGs）的预后特征的相关报道。本研究旨在构建一种基于IRGs的预后特征，以实现膀胱癌（bladder cancer, BLCA）患者的风险分层。
**方法** 本研究从癌症基因组图谱（Cancer Genome Atlas, TCGA）及基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了BLCA的RNA测序数据与临床信息。基于TCGA数据集，通过威尔科克森检验筛选差异表达的IRGs；随后采用单变量Cox回归分析，从上述差异IRGs中鉴定出预后相关IRGs。研究将TCGA数据集划分为训练集（n=284）与测试集（n=119）；基于训练集，构建了纳入多个预后IRGs的最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）惩罚Cox比例风险回归模型。该模型分别在训练集、测试集以及外部数据集GSE13507（n=165）中进行验证。此外，我们从肿瘤免疫评估资源（Tumor Immune Estimation Resource, TIMER）网站获取了6种肿瘤浸润免疫细胞的数据，并分析了不同风险组间的细胞浸润差异。进一步地，我们构建并验证了列线图，以实现BLCA患者的个体化治疗方案制定。
**结果** 本研究共鉴定出47个预后相关IRGs。通过LASSO回归筛选出7种BLCA特异性预后IRGs，分别为RBP7、PDGFRA、AHNAK、OAS1、RAC3、EDNRA及SH3BP2。我们构建了基于IRGs的预后特征，可将BLCA患者分为两个生存结局具有统计学差异的亚组[风险比（hazard ratio, HR）=10，95%置信区间（confidence interval, CI）=5.6~19，P<0.001]。受试者工作特征曲线（ROC curve）分析显示该特征具有良好的区分能力，1年、3年、5年随访的曲线下面积（area under the curve, AUC）分别为0.711、0.754及0.772。该预测模型的性能在训练集、测试集及外部数据集GSE13507中均得到验证。此外，高风险组（由该特征定义）中CD4+T细胞、CD8+T细胞、巨噬细胞、中性粒细胞及树突状细胞的浸润水平显著升高，提示慢性炎症可能降低BLCA患者的生存概率。本研究构建的列线图具有良好的临床实用性与有效性，预测准确且净获益为正。
**结论** 本研究所构建的免疫相关特征可基于生存率有效将BLCA患者划分为高风险组与低风险组，有助于筛选出高风险BLCA患者以接受更强化的治疗方案。