〔Abstract〕 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