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浙江大学学报(医学版)  2022, Vol. 51 Issue (1): 79-86    DOI: 10.3724/zdxbyxb-2021-0368
原著     
基于转录组学膀胱癌临床预后模型的构建
陈遒1,蔡良良1,2,3,*(),梁景岩1,2,3,*()
1.扬州大学医学院,江苏 扬州 225001
2.扬州大学转化医学研究院,江苏 扬州 225001
3.江苏省中西医结合老年病防治重点实验室,江苏 扬州 225001
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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摘要:

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    
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 words: Urinary bladder neoplasms    Prognostic    Transcriptomics    The Cancer Genome Atlas database    Genotype-Tissue Expression database    Weighted gene co-expression network analysis    Regression analysis
收稿日期: 2021-11-29 出版日期: 2022-05-17
CLC:  R737.14  
基金资助: 国家重点研发计划(2016YFE0126000);江苏省自然科学基金(BK20200935);江苏省高等学校大学生创新创业训练计划(202011117002Z)
通讯作者: 蔡良良,梁景岩     E-mail: jyliang@yzu.edu.cn
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引用本文:

陈遒,蔡良良,梁景岩. 基于转录组学膀胱癌临床预后模型的构建[J]. 浙江大学学报(医学版), 2022, 51(1): 79-86.

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.

链接本文:

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

图 1  加权基因共表达网络分析结果A:标本树状图和临床表征热图,二分类变量(总体生存状态、性别)以红色或白色块表示,多分类变量(年龄、组织学分级、临床分期、原发肿瘤、区域转移、远处转移、总体生存时间)以颜色编码块表示,颜色饱和度与变量值对应. B:确定最佳无标度拓扑模型拟合指数(左)和平均连通度(右)的软阈值,红色的水平线表示=0.9. C:基于拓扑重叠矩阵的基因聚类,具有相对相关性的基因位于同一或相邻的分支上.
图 2  WGCNA分析基因模块与临床表征的关系
图 3  LASSO回归模型及多因素回归分析结果A:132个与膀胱癌预后相关差异表达基因的LASSO系数分布,每条曲线代表一个系数,当调谐参数(λ)变化时,非零系数随之变化,进入LASSO回归模型;B:选择最佳λ的交叉验证,红色虚线与最佳对数λ交叉时,相当于多元Cox模型的最小值,两条虚线表示距离最小值一个标准差;C:基于训练集的多变量Cox回归分析结果.
图 4  膀胱癌患者预后预测模型验证结果A、D:训练集和测试集中患者的风险评分,垂直虚线左侧点代表低风险患者,右侧点代表高风险患者,水平虚线为低风险患者和高风险患者风险得分临界值;B、E:训练集和测试集中患者的Kaplan-Meier生存曲线图;C、F:训练集和测试集中风险得分预测患者1年和3年生存的ROC曲线.
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