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作 者:卜伟晓 穆华夏 高梦瑶 苏维强 韩梅 陶子琨 杨希 徐雅琪 石福艳 王清华 王素珍 孔雨佳 Bu Weixiao;Mu Huaxia;Gao Mengyao(School of Public Health,Shandong Second Medical University(261053),Weifang)
出 处:《中国卫生统计》2024年第3期354-359,共6页Chinese Journal of Health Statistics
基 金:国家自然科学基金(82003560);山东省自然科学基金(ZR2020MH340);山东省教育厅教改项目(M2021174,M2021327)。
摘 要:目的通过构建Lasso-Cox模型筛选肺腺癌差异表达基因,计算患者风险评分,构建肺腺癌预测模型,为肺腺癌的研究提供潜在的基因靶点,并为临床诊疗及预后提供新方向。方法下载癌症基因组图谱(TCGA)和肿瘤基因表达数据库(GEO)的肺腺癌基因表达和临床数据,用TCGA数据库训练模型,并合并两数据库用以模型验证,筛选的肺腺癌差异表达基因(DEGs)通过多因素Lasso-Cox回归构建风险评分预后模型,结合临床资料以确定肺腺癌最终的独立预后预测因素。利用GO富集分析、KEGG通路分析和CIBERSORTx免疫分析对风险模型差异表达基因进行生物学解释。结果通过单变量Cox和Lasso-Cox回归分析,获得了与肺腺癌预后相关的9个差异表达基因。结合临床数据的多因素Cox回归模型显示,恶性肿瘤病史、N分期、T分期和风险评分是预后的独立影响因素。结论本研究构建的肺腺癌预后模型可以有效预测患者的预后风险,为临床决策和个性化治疗提供理论基础。Objective To screen differentially expressed genes in lung adenocarcinoma by constructing Lasso-Cox model to provide potential gene targets for the research of lung adenocarcinoma and new directions for clinical diagnosis,treatment and prognosis by calculating patient risk score and constructing prediction model of lung adenocarcinoma.Methods The gene expression and clinical data of lung adenocarcinoma were downloaded from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus database(GEO).The TCGA database was used to train model,and the two databases were combined for model validation.The screened differentially expressed genes(DEGs)of lung adenocarcinoma were analyzed by univariate Cox and multivariate Lasso-Cox to construct a risk score prognosis model.Risk score from the final Cox prediction model and clinical data were combined to determine independent prognostic factors.GO enrichment analysis,KEGG pathway analysis and CIBERSORTx immunoassay were used to evaluate the biological interpretation of differentially expressed genes in the risk model.Results The analysis using univarate Cox and Lasso-Cox regreesion identified 9 differentially expressed genes associated with the prognosis of lung adenocarcinoma.Multivariate Cox regression analysis,incorporating clinical data,revealed that a history of malignant tumors,N stage,T stage,and the risk score were independent prognostic factors.Conclusion The prognositic model of lung adenocarcinoma can effectively predict the prognosis risk and provide a theoretical basis for clinical decision-making and personalized treatment.
关 键 词:Lasso-Cox模型 预后预测 基因表达 肺腺癌
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