Integration of multi-omics profiling reveals an epigenetic-based molecular classification of lung adenocarcinoma: implications for drug sensitivity and immunotherapy response prediction.

Background: Lung adenocarcinoma (LUAD) remains a major cause of cancer-related mortality worldwide, with high heterogeneity and poor prognosis. Epigenetic dysregulation plays a crucial role in LUAD progression, yet its potential in molecular classification and therapeutic prediction remains largely unexplored. Methods: We performed an integrated multi-omics analysis of 432 LUAD patients from TCGA and 398 patients from GEO datasets. Using consensus clustering and random survival forest (RSF) algorithms, we established an epigenetic-based molecular classification system and constructed a prognostic model. The model's performance was validated in multiple independent cohorts, and its biological implications were investigated through comprehensive functional analyses. Results: We identified two distinct molecular subtypes (CS1 and CS2) with significant differences in epigenetic modification patterns, immune microenvironment, and clinical outcomes (P = 0.005). The RSF-based prognostic model demonstrated robust performance in both training (TCGA-LUAD) and validation (GSE72094) cohorts, with time-dependent AUC values ranging from 0.625 to 0.694. Low-risk patients exhibited enhanced immune cell infiltration, particularly CD8 Conclusion: Our study establishes a novel epigenetic-based classification system and predictive model for LUAD, providing valuable insights into patient stratification and personalized treatment selection. The model's ability to predict immunotherapy response and drug sensitivity offers practical guidance for clinical decision-making, potentially improving patient outcomes through precision medicine approaches.