[1]乔秋江,庞 琦.基于自噬相关基因构建的预后模型对脑胶质瘤免疫微环境的影响[J].医学信息,2022,35(17):1-6.[doi:10.3969/j.issn.1006-1959.2022.17.001]
　QIAO Qiu-jiang,PANG Qi.Effect of Prognostic Model Based on Autophagy-related Genes on Immune Microenvironment of Glioma[J].Journal of Medical Information,2022,35(17):1-6.[doi:10.3969/j.issn.1006-1959.2022.17.001]
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基于自噬相关基因构建的预后模型对脑胶质瘤免疫微环境的影响()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
35卷
期数:
2022年17期
页码:
1-6
栏目:
生物信息学
出版日期:
2022-09-01

文章信息/Info

Title:
Effect of Prognostic Model Based on Autophagy-related Genes on Immune Microenvironment of Glioma
文章编号:
1006-1959（2022）17-0001-06
作者:
乔秋江; 庞 琦
（山东大学附属山东省立医院神经外科，山东 济南 250021）
Author(s):
QIAO Qiu-jiang; PANG Qi
(Department of Neurosurgery,Shandong Provincial Hospital Affiliated to Shandong University,Jinan 250021,Shandong,China)
关键词:
脑胶质瘤; 缺氧诱导因子; 自噬; 免疫微环境
Keywords:
Gliomas; Hypoxia-inducible factor; Autophagy; Immune microenvironment
分类号:
R739.41
DOI:
10.3969/j.issn.1006-1959.2022.17.001
文献标志码:
A
摘要:
目的 利用TCGA公共数据库筛选脑胶质瘤中与缺氧诱导因子（HIF-1α）表达相关的自噬相关基因（ARGs），分析其对患者生存预后的影响并构建风险评估模型及与肿瘤免疫微环境的关系。方法 从TCGA中提取脑胶质瘤患者的临床信息和基因表达数据，通过Pearson相关性分析筛选与HIF-1α表达有关的ARGs。GO和KEGG富集分析信号通路和生物学行为。单因素和多因素Cox回归分析选出对胶质瘤患者的生存预后有影响的ARGs。以中位数为截断值构建风险评估模型，ROC曲线及K-M生存曲线评估模型性能，并分析风险评估模型与免疫微环境的关系。结果 共筛选出2370个基因与HIF-1α存在相关性，其中有40个与HIF-1α表达相关的ARGs；GO和KEGG富集分析显示，其与细胞死亡的调控相关；单因素Cox回归分析显示，与生存预后相关的基因有36个；利用多因素Cox回归确定了4个ARGs（CASP3、MAPK9、NAMPT、CAMKK2）并构建了风险评估模型，其中3年时AUC最大，为0.89；K-M生存曲线显示，低风险组生存率高于高风险组；ESITMATE算法显示，高风险组免疫浸润评分高于低风险组；免疫检查点基因结果显示，高风险组PD1、PDL1、PDL2、LAG3、B7H3、TIM3、CTLA4高于低风险组；免疫细胞浸润结果显示，高风险组单核细胞、活化自然杀伤细胞和浆细胞低于低风险组。结论 本研究构建了HIF-1α相关的ARGs预后模型，揭示了缺氧环境调控自噬相关基因对于胶质瘤患者临床预后及免疫治疗的影响。
Abstract:
Objective To screen autophagy-related genes (ARGs) related to the expression of hypoxia-inducible factor (HIF-1α) in glioma by using TCGA public database, analyze their effects on the survival and prognosis of patients, and construct a risk assessment model and its relationship with tumor immune microenvironment.Methods The clinical information and gene expression data of glioma patients were extracted from TCGA, and ARGs related to HIF-1α expression were screened by Pearson correlation analysis. GO and KEGG enrichment analysis of Shadow signaling pathways and biological behavior. Univariate and multivariate Cox regression analyses selected ARGs that affect the survival prognosis of glioma patients. The ROC curve and K-M survival curve were used to evaluate the performance of the model, and the relationship between the risk assessment model and the immune microenvironment was analyzed.Results A total of 2370 genes were screened to be correlated with HIF-1α, of which 40 ARGs were related to HIF-1α expression. GO and KEGG enrichment analysis showed that it was related to the regulation of cell death. Univariate Cox regression analysis showed that there were 36 genes related to survival prognosis, 4 ARGs (CASP3, MAPK9, NAMPT, CAMKK2) were identified by multivariate Cox regression and a risk assessment model was constructed. The AUC was the largest at 3 years, which was 0.89; K-M survival curve showed that the survival rate of low risk group was higher than that of high risk group. The ESITMATE algorithm showed that the immune infiltration score of the high-risk group was higher than that of the low-risk group; the results of immune checkpoint gene showed that PD1, PDL1, PDL2, LAG3, B7H3, TIM3 and CTLA4 in high risk group were higher than those in low risk group; the results of immune cell infiltration showed that monocytes, activated natural killer cells and plasma cells in high risk group were lower than those in low risk group.Conclusion This study constructed an ARGs prognosis model correlated with the important hypoxia regulator HIF-1α, and further revealed the influence of ARGs regulated by hypoxia environment on the clinical prognosis and immunotherapy of glioma patients to provide a theoretical basis.

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更新日期/Last Update: 1900-01-01