Introduction: Nicotine facilitates the progression of Lung Adenocarcinoma (LUAD) by activating signaling pathways and remodeling the Tumor Microenvironment (TME). However, the molecular classification based on nicotine response spectrum and its clinical relevance remained unclear. Materials and Methods: We retrieved 52 nicotine response-related genes from the MSigDB database and analyzed RNA-seq data obtained from TCGA-LUAD and GSE31210 cohorts. Distinct molecular subtypes were identified by consensus clustering analysis. Next, differential gene expression analysis and functional enrichment analysis were conducted. A prognostic RiskScore model was constructed using LASSO and Cox regression, and validated via Kaplan-Meier and ROC analyses. Immune microenvironment features were assessed using CIBERSORT, ESTIMATE, and TIDE algorithms, while pathway associations were explored via GSEA. Results: Two distinct molecular subtypes (C1 and C2) were identified, with C1 showing a more favorable prognosis. A RiskScore model developed based on five genes (KCNK1, CPS1, ABCC2, TCN1, PGC) can effectively stratify patients into high- and low-risk groups, with the high-risk group exhibiting a worse overall survival (OS) (p < 0.001). The two risk groups demonstrated distinct enrichment of pathways. Notably, the low-risk group exhibited increased infiltration of regulatory T cells and M2 macrophages and lower TIDE scores, suggesting better immunotherapy response. A nomogram combining RiskScore and AJCC stage demonstrated strong predictive accuracy. Discussion: This study was the first to classify nicotine response-related molecular subtypes for LUAD, offering novel insights into nicotine-driven progression of LUAD. The RiskScore and nomogram may aid in risk stratification and personalized management, though further experimental validation is still needed. Conclusion: This study established a nicotine response-related prognostic model for LUAD, revealing its utility in predicting survival and immune therapy responses. Our findings provided novel biomarkers for personalized precision medicine in LUAD.
Yue et al. (Tue,) studied this question.