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J Zhejiang Univ (Med Sci)  2022, Vol. 51 Issue (1): 79-86    DOI: 10.3724/zdxbyxb-2021-0368
    
Construction of prognosis model of bladder cancer based on transcriptome
CHEN Qiu1,CAI Liangliang1,2,3,*(),LIANG Jingyan1,2,3,*()
1. Yangzhou University Medical College, Yangzhou 225001, Jiangsu Province, China;
2. Institute of Translational Medicine, Yangzhou University, Yangzhou 225001, Jiangsu Province, China;
3. Jiangsu Provincial Key Laboratory of Geriatric Disease Prevention and Control, Yangzhou 225001, Jiangsu Province, China
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Abstract  

Objective: To screen for prognosis related genes in bladder cancer, and to establish prognosis model of bladder cancer. Methods: 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). Results: 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 ADCY9, MAFG_DT, EMP1, CAST, PCOLCE2, LTBP1, CSPG4, NXPH4, SLC1A6, 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. Conclusion: The established model in this study has good predictive ability for the survival of bladder cancer patients.



Key wordsUrinary bladder neoplasms      Prognostic      Transcriptomics      The Cancer Genome Atlas database      Genotype-Tissue Expression database      Weighted gene co-expression network analysis      Regression analysis     
Received: 29 November 2021      Published: 17 May 2022
CLC:  R737.14  
Corresponding Authors: CAI Liangliang,LIANG Jingyan     E-mail: jyliang@yzu.edu.cn
Cite this article:

CHEN Qiu,CAI Liangliang,LIANG Jingyan. Construction of prognosis model of bladder cancer based on transcriptome. J Zhejiang Univ (Med Sci), 2022, 51(1): 79-86.

URL:

https://www.zjujournals.com/med/10.3724/zdxbyxb-2021-0368     OR     https://www.zjujournals.com/med/Y2022/V51/I1/79


基于转录组学膀胱癌临床预后模型的构建

目的:筛选膀胱癌预后相关基因,建立膀胱癌预后评分模型。方法:通过UCSC Xena平台下载癌症基因组图谱(TCGA)数据库、基因型和基因表达量关联数据库(GTEx)中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名健康对照者正常膀胱组织RNA测序数据。采用加权基因共表达网络分析(WGCNA)、单因素Cox回归分析、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,结合Kaplan-Meier生存曲线、受试者操作特征曲线(ROC曲线)验证模型的准确性。结果:分析得到膀胱癌相关差异表达基因共2308个。WGCNA拟合得到6个基因模块,筛选出对膀胱癌预后有显著作用的基因829个。运用单因素Cox回归与LASSO回归分析筛选出24个与膀胱癌患者预后相关的基因,多因素Cox回归分析训练集数据得到9个作为独立预测因子的基因,分别是ADCY9MAFG_DTEMP1CASTPCOLCE2LTBP1CSPG4NXPH4SLC1A6,以此建立膀胱癌患者预后预测模型。训练集中高风险组和低风险组3年存活率分别为31.814%和59.821%,测试集中高风险组和低风险组3年存活率分别为32.745%和68.932%,模型预测训练集和测试集患者预后的ROC曲线下面积均在0.7以上。结论:本研究建立的模型对膀胱癌高风险和低风险人群的生存情况具有较好的预测能力。


关键词: 膀胱肿瘤,  预后,  转录组学,  癌症基因组图谱数据库,  基因型和基因表达量关联数据库,  加权基因共表达网络分析,  回归分析 
Figure 1 Results of weighted gene co-expression network analysis
Figure 2 WGCNA analyzes the relationship between gene modules and clinical traits每一行代表一个用颜色编码的模块特征基因,每一列代表一个临床表征,每个单元格代表对应模块特征的Pearson相关系数(值),每个单元格的颜色表示关联程度.
Figure 3 Results of LASSO regression model and multivariate regression analysis
Figure 4 Validation of prognostic models for patients with bladder cancer
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