Xing Qianwei, Liu Shouyong, Jiang Silin, Li Tao, Wang Zengjun, Wang Yi
Department of Urology, Affiliated Hospital of Nantong University, Nantong, China.
Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Transl Androl Urol. 2020 Oct;9(5):2054-2070. doi: 10.21037/tau-20-696.
We aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance.
Differentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model.
A total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066-1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC.
Our study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches.
我们旨在建立一种基于免疫相关基因（IRG）的特征，可为临床膀胱癌（BC）预后监测提供指导。
从TCGA数据库下载的转录组数据中提取BC组织与正常组织之间差异表达的IRG和转录因子（TF）。基于差异表达的IRG进行基因本体（GO）功能和京都基因与基因组百科全书（KEGG）通路富集分析，以识别相关通路。然后，进行单变量Cox回归分析以研究具有预后价值的IRG，并利用LASSO惩罚Cox回归分析建立预后指数（PI）模型。
本研究纳入了TCGA数据库中的411例BC组织样本和19例正常膀胱组织，并鉴定出259个差异表达的IRG。还提供了TF与IRG之间的网络，以寻找差异表达IRG的上游调节因子。通过单变量Cox回归分析，分析了57个具有预后价值的IRG，最终通过LASSO惩罚Cox回归分析鉴定出10个IRG以构建PI模型。该模型可根据总生存期（P = 9.923e - 07）将BC患者显著分为高危组和低危组，其AUC达到0.711。通过单变量和多变量COX回归分析，该PI被证明是一个有价值的独立预后因素（HR = 1.119，95%CI = 1.066 - 1.175，P < 0.001）。还利用CMap数据库分析筛选出10种具有治疗BC潜力的小分子药物。
我们的研究成功提供了一种基于IRG的新型PI，具有预测BC预后的潜力，并筛选出10种具有治疗BC潜力的小分子药物。此外，还展示了TF与IRG之间的网络，以寻找其上游调节因子供未来研究。
