[1]夏天,金志勇,于如同.基于生信分析的脑胶质瘤预后模型的构建与验证[J].中国临床神经外科杂志,2023,28(08):504-507512.[doi:10.13798/j.issn.1009-153X.2023.08.008]
　XIA Tian,JIN Zhi-yong,YU Ru-tong.Construction and verification of brain glioma prognostic model based on bioinformatics analysis[J].,2023,28(08):504-507512.[doi:10.13798/j.issn.1009-153X.2023.08.008]
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基于生信分析的脑胶质瘤预后模型的构建与验证()
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《中国临床神经外科杂志》[ISSN:1009-153X/CN:42-1603/TN]

卷:
28
期数:
2023年08期
页码:
504-507512
栏目:
论著
出版日期:
2023-08-31

文章信息/Info

Title:
Construction and verification of brain glioma prognostic model based on bioinformatics analysis
文章编号:
1009-153X（2023）08-0504-04
作者:
夏天; 金志勇; 于如同
221000江苏徐州，徐州医科大学附属医院神经外科/徐州医学大学神经系统疾病研究所（夏天、金志勇、于如同）；221300江苏邳州，徐州医科大学附属邳州医院神经外科（夏天）
Author(s):
XIA Tian1; 2;  JIN Zhi-yong1;  YU Ru-tong1
1. Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China; 2. Department of Neurosurgery, Pizhou Hospital Affiliated to Xuzhou Medical University, Pizhou 221300, China
关键词:
脑胶质瘤; 生信分析; 差异表达基因; WGCNA; 预后模型
Keywords:
Glioma;  Bioinformatics analysis;  Differentially expressed genes;  WGCNA;  Prognostic model
分类号:
A
DOI:
10.13798/j.issn.1009-153X.2023.08.008
文献标志码:
R 742.8; R 651.1+1
摘要:
目的 探讨脑胶质瘤预后模型的构建方法及预测效果。方法 从CGGA数据库获得脑胶质细胞瘤的样本数据，进行差异表达基因（DEGs）分析及加权基因共表达网络分析（WGCNA），获得关键基因，采用Lasso-Cox方法分析有效关键基因并计算风险评分，生存曲线分析风险评分与脑胶质瘤生存预后的关系，多因素Cox回归和列线图分析构建预后模型，并使用TCGA数据库验证。结果 共发现5 463个DEGs，其中4 697个显著上调，766个显著下调。WGCNA分析筛选出261个关键基因，Lasso-Cox分析得到11个有效关键基因，并计算风险评分。根据风险评分中位数将脑胶质瘤分为高、低风险组，生存曲线分析显示，高风险组脑胶质瘤病人中位生存期较低风险组明显缩短（P<0.001）。多因素Cox回归分析显示风险评分是脑胶质瘤预后危险因素。利用WHO级别、复发状态、IDH情况、年龄、化疗及风险评分构建预后模型预测1、3、5年生存预后ROC曲线下面积分别为0.76、0.82、0.84，预后模型的C-Index值为0.778，风险评分的C-Index值为0.74。结论 采用CGGA数据库中脑胶质瘤样本数据成功构建了预后模型，为临床判断脑胶质瘤预后具有一定的价值。
Abstract:
Objective To investigate the method of constructing prognostic model of brain glioma and its prediction effect. Methods The data of glioma patients were obtained from CGGA database, and the key genes were obtained by differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The effective key genes were analyzed by Lasso Cox method and the risk score was calculated. The relationship between risk score and survival prognosis of glioma patients was analyzed by survival curve. The prognostic model was constructed by multivariate Cox regression and nomogram analysis, and verified by TCGA database. Results A total of 5 463 DEGs were found, of which 4 697 were significantly up-regulated and 766 were significantly down-regulated. A total of 261 key genes was screened out by WGCNA analysis, and 11 effective key genes were screened out by Lasso-Cox analysis and the risk score was calculated. According to the median risk score, brain glioma patients were divided into high-risk and low-risk groups. Survival curve analysis showed that the median survival time of brain gliomas patients in the high-risk group was significantly shorter than that in the low-risk group (P<0.001). Multivariate Cox regression analysis showed that risk score was a prognostic risk factor for glioma patients. WHO grade, relapse status, IDH status, age, chemotherapy and risk score were used to construct a prognostic model to predict the survival prognosis and the area under ROC curve of 1, 3 and 5 years survival was 0.76, 0.82 and 0.84, respectively. The C-Index value of the prognostic model was 0.778 and the C-Index value of risk score was 0.74. Conclusions A prognostic model is successfully constructed using glioma data from CGGA database, which has certain value for clinical judgment of glioma prognosis.

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备注/Memo

备注/Memo:
（2023-03-22收稿，2023-07-26修回）
通讯作者：于如同，E-mail：yu.rutong@163.com
更新日期/Last Update: 2022-08-31