1. Yangzhou University Medical College, Yangzhou 225001, Jiangsu Province, China.
2. Institute of Translational Medicine, Yangzhou University, Yangzhou 225001, Jiangsu Province, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban. 2022 Feb 25;51(1):79-86. doi: 10.3724/zdxbyxb-2021-0368.
To screen for prognosis related genes in bladder cancer, and to establish prognosis model of bladder cancer.
The clinical information and bladder tissue RNA sequencing data of 406 bladder cancer patients, and the bladder tissue RNA sequencing data of 28 healthy individuals were downloaded from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression (GTEx) database through the UCSC Xena platform. The weighted gene co-expression network analysis (WGCNA), univariate Cox regression, LASSO regression analysis and multivariate Cox regression analysis were used to screen the prognosis-related genes of bladder cancer and the prognostic model was established. The prognostic model was evaluated with receiver operator characteristic curve (ROC curve).
A total of 2308 differentially expressed genes related to bladder cancer were obtained from the analysis. Six gene modules were obtained by WGCNA, and 829 genes with significant effect on bladder cancer prognosis were screened out. Univariate Cox regression and LASSO regression analysis showed that 24 genes were related to the prognosis of bladder cancer patients. Multivariate Cox regression analysis revealed 9 genes as independent predictors in training set, namely , , , , , , , , , which were used to establish the prognosis model of bladder cancer patients. The 3-year survival rates of the high-risk group and the low-risk group in the training set were 31.814% and 59.821%, respectively. The 3-year survival rates of the high-risk group and the low-risk group in the test set were 32.745% and 68.932%, respectively. The areas under the ROC curve of the model for predicting the prognosis of bladder cancer patients in both the training set and the test set were above 0.7.
The established model in this study has good predictive ability for the survival of bladder cancer patients.
筛选膀胱癌预后相关基因，建立膀胱癌预后模型。
从癌症基因组图谱（TCGA）数据库、UCSC Xena 平台通过基因型组织表达（GTEx）数据库下载 406 例膀胱癌患者的临床信息和膀胱癌组织 RNA 测序数据，以及 28 例健康个体的膀胱癌组织 RNA 测序数据。采用加权基因共表达网络分析（WGCNA）、单因素 Cox 回归、LASSO 回归分析和多因素 Cox 回归分析筛选膀胱癌预后相关基因并建立预后模型。采用受试者工作特征曲线（ROC 曲线）评估预后模型。
通过 WGCNA 分析得到 2308 个与膀胱癌相关的差异表达基因。共获得 6 个基因模块，筛选出 829 个对膀胱癌预后有显著影响的基因。单因素 Cox 回归和 LASSO 回归分析显示，24 个基因与膀胱癌患者的预后相关。多因素 Cox 回归分析显示，在训练集中有 9 个基因是独立的预后预测因子，分别为、、、、、、、、，这些基因用于建立膀胱癌患者的预后模型。在训练集中，高危组和低危组的 3 年生存率分别为 31.814%和 59.821%。在测试集中，高危组和低危组的 3 年生存率分别为 32.745%和 68.932%。该模型预测膀胱癌患者预后的 ROC 曲线下面积在训练集和测试集均大于 0.7。
本研究建立的模型对膀胱癌患者的生存具有良好的预测能力。
