Qiu Huaide, Hu Xiaorong, He Chuan, Yu Binbin, Li Yongqiang, Li Jianan
Center of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China.
Front Genet. 2020 Feb 5;11:12. doi: 10.3389/fgene.2020.00012. eCollection 2020.
There 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).
RNA-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 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.
A 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.
The 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.
尚无基于免疫相关基因（IRGs）的预后特征报告。本研究旨在开发一种基于IRGs的预后特征，用于对膀胱癌（BLCA）患者进行分层。
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中检索BLCA的RNA测序数据及临床信息。基于TCGA数据集，通过Wilcoxon检验鉴定差异表达的IRGs。在这些基因中，使用单变量Cox回归分析鉴定预后IRGs。随后，我们将TCGA数据集分为训练集（n = 284）和测试集（n = 119）。基于训练集，我们构建了一个包含多个预后IRGs的最小绝对收缩和选择算子（LASSO）惩罚Cox比例风险回归模型。该模型在训练集、测试集和外部数据集GSE13507（n = 165）中进行了验证。此外，我们从肿瘤免疫估计资源（TIMER）网站获取了六种肿瘤浸润免疫细胞，并分析了风险组之间的差异。此外，我们构建并验证了一个列线图，用于为BLCA患者量身定制治疗方案。
鉴定出一组47个预后IRGs。LASSO回归鉴定出7个BLCA特异性预后IRGs，即RBP7、PDGFRA、AHNAK、OAS1、RAC3、EDNRA和SH3BP2。我们开发了一种基于IRGs的预后特征，将BLCA患者分为两个亚组，其生存结果具有统计学差异[风险比（HR）= 10，95%置信区间（CI）= 5.6 - 19，P < 0.001]。ROC曲线分析显示，在1年、3年和5年随访时，AUC分别为0.711、0.754和0.772，具有可接受的区分度。预测性能在训练集、测试集和外部数据集GSE13507中得到验证。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、中性粒细胞和树突状细胞浸润增加表明慢性炎症可能降低BLCA患者的生存机会。列线图显示具有临床相关性且有效，具有准确的预测和正净效益。
目前的免疫相关特征可以根据生存率有效地将BLCA患者分为高风险和低风险组，这可能有助于选择高风险BLCA患者进行更强化的治疗。
