Decoding chromatin regulator-LAIR1+ M2 like macrophage patterns in atherosclerosis and clinical outcomes of Lung adenocarcinoma patients: evidence from artificial intelligence-driven multi omics and in vitro validation

Atherosclerosis (AS) is a common complication of lung adenocarcinoma (LUAD), but its underlying mechanisms in LUAD remain unclear. This study aimed to decipher the role of chromatin regulators in AS pathogenesis and their association with clinical outcomes in LUAD patients. AS- and chromatin regulator (CR)-associated prognostic indicators were identified in LUAD and analyzed in the TCGA-LUAD cohort using Cox regression and Disease Ontology analysis. CR-related risk subgroups were defined by NMF in the GSE26939 cohort. GSVA and WGCNA, combined with interpretable machine learning, were used to construct a predictive model and identify key genes, which were validated in GSE68465. The molecular features of key genes and their roles in AS were further evaluated. Besides, mechanisms of key genes in M2-like macrophages were assessed at the single-cell level in LUAD patients using cutting-edge analytical frameworks. A deep learning framework and molecular docking were used to screen natural compounds. Finally, co-culture experiments were conducted for validation. CR signatures can guide the LUAD patients LUAD patients into high- and low-AS risk groups and were associated with clinical outcomes. LAIR1 can be considered as an AS-related factor enriched in M2-like macrophages and involved in LUAD progression. Quercetin was identified as potential preventive agent for AS in LUAD patients. CR-associated signatures play an important role in AS pathogenesis and LUAD progression.