Integrative multiomic and immune profiling of lung adenocarcinoma: molecular landscapes, gene expression, and treatment response insights

Abstract

Background: Lung adenocarcinoma (LUAD) is a major cause of cancer death. Traditional histopathological classification overlooks molecular heterogeneity, limiting personalized treatment. This study used multiomic data to define LUAD subtypes, assess prognostic significance, and analyze immune features, aiming to improve targeted therapy and clinical outcomes. Methods: This study used Consensus Clustering and Gap Statistics to analyze LUAD multiomic data, including mRNA, lncRNA, miRNA, DNA methylation, and mutations. Clustering was validated by silhouette plots and heatmaps. Molecular characterization involved regulon activity, immune and metabolic profiling. Functional assays (qPCR, WB, CCK-8, flow cytometry) assessed neuronal differentiation factor (NDNF)’s role in LUAD. Results: Two molecular LUAD subtypes showed distinct clustering and survival outcomes. One subtype had worse prognosis and unique immune features, including checkpoint expression and microenvironment differences. Gene signatures and metabolism varied by subtype. Neuronal differentiation factor was downregulated in tumors; its overexpression suppressed LUAD cell viability and promoted apoptosis, suggesting tumor-suppressive function. Conclusions: This study identifies 2 LUAD subtypes with distinct molecular and immune features linked to prognosis and therapy response. Neuronal differentiation factor downregulation and its tumor-suppressive effects highlight its therapeutic potential. These findings support improved LUAD stratification and personalized treatment strategies.