〔Abstract〕 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 ex- plore 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. 000 1) . Receiver operating characteristic (ROC) curve analysis demonstrated that the mod- el 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 modula- ting the lung cancer immune microenvironment. Conclusion The prognostic model for lung cancer based on acro- lein-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.