现代医药卫生2025，Vol.41Issue(3)：588-593,6.DOI:10.3969/j.issn.1009-5519.2025.03.003
基于机器学习构建的肿瘤突变负荷相关胃癌预后模型
Conduction of tumor mutational burden related gastric cancer prognosis model based on machine learning
王海涛 1胡海红 1李卓1
作者信息
- 1. 南华大学附属第一医院药学部,湖南 衡阳 421001
- 折叠
摘要
Abstract
Objective To develop and validate a tumor mutational burden(TMB)related prognostic model and identify biomarkers for predicting gastric cancer prognosis and immunotherapy response by analy-zing genes associated with tumor mutation burden.Methods Limma package was used to screen differentially expressed genes in gastric cancer and normal tissues,and the prognostic model of gastric cancer was construc-ted by LASSO regression and multivariate Cox regression.The CIBERSORT algorithm was used to analyze the immune invasion of the tumor to assess the response to immunotherapy.Results The established tumor mutation load-related prognostic model showed good prediction accuracy,and the AUC values of 1,3 and 5 years in the TCGA fleet were 0.641,0.665 and 0.720,respectively.Overall survival was better in the low-risk group than in the high-risk group and was associated with a higher degree of infiltration of immune-activated cells.Conclusion The prognostic model constructed in this study has good prediction accuracy and can evaluate immuno-therapy reactivity,providing effective support for clinical diagnosis,treatment and immunotherapy of gastric cancer.关键词
肿瘤突变负荷/胃癌/免疫浸润/免疫治疗/预后模型/机器学习Key words
Tumor mutational burden/Gastric cancer/Immune infiltration/Immune therapy/Prognostic model/Machine learning分类
临床医学引用本文复制引用
王海涛,胡海红,李卓..基于机器学习构建的肿瘤突变负荷相关胃癌预后模型[J].现代医药卫生,2025,41(3):588-593,6.
