基于TGGA数据库构建肺腺癌自噬相关基因预后风险模型

Construction of prognostic risk model of autophagy related genes in lung adenocarcinoma based on TGGA database

目的基于TCGA数据库,结合人类自噬数据库构建预测肺腺癌患者预后的风险模型,探讨自噬相关基因预后风险模型对肺腺癌患者预后的预测性能及与免疫微环境的相关性。方法从癌症基因组图谱数据库下载肺腺癌患者临床信息和转录组数据,结合人类自噬数据库筛选出232个自噬相关基因。通过Cox回归分析筛选出4个独立与预后相关的自噬基因,并采用风险评分构建肺腺癌预后预测模型,用ROC曲线评价预测模型性能。使用ESTIMATE和CIBERSORT在线网站(https://cibersort.stanford.edu/)探索风险评分与肿瘤免疫微环境之间的关系。结果肺腺癌中有30个差异表达的自噬相关基因,其中4个自噬基因(BIRC5、ERO1A、ITGB4、NLRC4)具有预测患者预后的功能。依据风险评分进行分组,Kaplan-Meier分析表明高风险组生存率低于低风险组(P<0.0001)。ROC曲线表明风险评分模型判断肺腺癌预后的准确性(AUC=0.757)。ESTIMATE和CIBERSORT分析提示风险评分模型与肿瘤微环境中的多个免疫细胞亚群浸润相关。结论自噬相关基因预后风险模型较临床数据能更好地预测肺腺癌患者预后。在高风险组中,CD4+记忆静止细胞可改善肺腺癌患者预后。

Objective A prognostic risk model for lung adenocarcinoma patients was established based on the cancer genome atlas(TCGA)database to explore the prognostic performance of autophagy related gene risk model for lung adenocarcinoma patients and its correlation with immune microenvironment.Methods Clinical information and transcriptome data of lung adenocarcinoma patients were downloaded and extracted from TCGA database,and 232 autophagy-related genes were screened from the human autophagy database.cox regression analysis was used to screen out four autophagy genes independently associated with prognosis.The prognostic prediction model of lung adenocarcinoma was constructed by risk score,and the performance of prediction model was evaluated by ROC curve.The relationship between risk scores and tumor immune microenvironment was explored using ESTIMATE(estimation of stromal and immune cells in malignant tumour tissues using expression data)and CIBERSORT algorithms.Results Thirty differentially expressed autophagy-related genes were identified in lung adenocarcinoma,of which four autophagy genes(BIRC5,ERO1A,ITGB4,NLRC4)could predict the prognosis of the patients.Grouped by risk score,the Kaplan-Meier analysis demonstrated that the survival rate of high-risk group was significantly lower than that of low-risk group(P<0.0001).The ROC curve proved the accuracy of the model in predicting the prognosis of lung adenocarcinoma(AUC=0.757).The ESTIMATE and CIBERSORT analyses revealed that the risk scoring model was associated with multiple immune cells and immune infiltrates in the tumor microenvironment.Conclusion Compared with clinical data,the autophagy gene prognostic risk model can better predict the prognosis of patients with lung adenocarcinoma.In the high-risk group,CD4+memory quiescent cells can improve prognosis in lung adenocarcinoma patients.