摘要
目的通过生物信息学方法构建并验证基于丙烯醛相关基因的肺癌预后预测模型。方法利用GEO数据库获取肺癌数据集GSE30219和GSE68465,同时从CTD数据库筛选丙烯醛相关基因集。首先,在GSE30219数据集中筛选癌与癌旁的差异表达基因(DEGs),与丙烯醛基因集取交集,获得候选基因。随后,采用基因集变异分析(GSVA)以评估其功能变化特征。基于STRING数据库构建蛋白质互作(PPI)网络,筛选核心枢纽基因(Hub Genes)。采用SVM-RFE和LASSO-Cox回归分析构建基于丙烯醛相关基因的肺癌预后预测模型,并使用GSE68465数据集进行独立验证。通过CIBERSORT方法分析高低风险组的免疫细胞浸润特征,同时对高低风险组的DEGs进行功能富集分析,进一步揭示基于丙烯醛相关基因的肺癌预后的潜在分子机制。结果共筛选出361个丙烯醛相关的肺癌DEGs,进一步确定7个关键基因用于模型构建。Kaplan-Meier生存分析显示,高风险组患者的生存率显著低于低风险组(P<0.0001)。ROC曲线分析结果表明,该模型具有良好的预测性能。此外,免疫浸润分析显示,风险评分与多种免疫细胞亚群密切相关,揭示了丙烯醛相关基因在肺癌免疫微环境中的潜在作用。结论基于丙烯醛相关基因的肺癌预后模型在肺癌的预后中展现出显著的应用价值,为揭示丙烯醛在肺癌发生与发展的潜在机制提供新的依据。
Objective To construct and validate a prognostic model for lung cancer based on acrolein-related genes using bioinformatics methods.Methods Lung cancer datasets GSE30219 and GSE68465 were obtained from the GEO database,and acrolein-related gene sets were retrieved from the CTD database.Differentially expressed genes(DEGs)between cancer and adjacent tissues were identified in the GSE30219 dataset.The intersection of these DEGs and acrolein-related genes was then used to identify candidate genes.Gene set variation analysis(GSVA)was performed to assess functional alterations based on the intersection genes.A protein-protein interaction(PPI)network was constructed based on the STRING database to identify core hub genes.Subsequently,support vector machine recursive feature elimination(SVM-RFE)and LASSO-Cox regression analyses were employed to develop a prognostic model based on acrolein-related genes,which was independently validated using the GSE68465 dataset.The CIBERSORT algorithm was applied to evaluate the immune cell infiltration characteristics between high-and low-risk groups,and functional enrichment analysis of DEGs between the two groups was conducted to further explore the potential molecular mechanisms underlying the prognostic model.Results A total of 361 acrolein-related DEGs were identified in lung cancer,and 7 key genes were selected for model construction.Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly lower survival rates compared to those in the low-risk group(P<0.0001).Receiver operating characteristic(ROC)curve analysis demonstrated that the model possessed good predictive performance.Moreover,immune infiltration analysis indicated that the risk score was closely associated with multiple immune cell subsets,suggesting a potential role of acrolein-related genes in modulating the lung cancer immune microenvironment.Conclusion The prognostic model for lung cancer based on acrolein-related genes demonstrates significant application value in predicting the prognosis of lung cancer,providing new insights into the potential mechanisms of acrolein in the onset and progression of lung cancer.
作者
冯祎婷
任亮亮
娄丽娟
沈玉先
姜颖
Feng Yiting;Ren Liangliang;Lou Lijuan;Shen Yuxian;Jiang Ying(Dept of Biochemistry and Molecular Biology,School of Basic Medical Sciences,Anhui Medical University,Hefei 230032;State Key Laboratory of Medical Proteomics,Beijing Proteome Research Center,National Center for Protein Sciences(Beijing),Beijing Institute of Lifeomics,Beijing 102206)
出处
《安徽医科大学学报》
北大核心
2025年第11期1985-1995,共11页
Acta Universitatis Medicinalis Anhui
基金
国家重点研发计划项目(编号:2020 YFE0202200)。
关键词
丙烯醛
肺癌
环境污染物
生物信息学
机器学习
预后模型
acrolein
lung cancer
environmental pollutants
bioinformatics
machine learning
prognostic model
