Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.

BACKGROUND: KRAS-mutant lung adenocarcinoma (LUAD) is associated with aggressive phenotypes and therapy resistance, which highlights an urgent need to identify reliable biomarkers and therapeutic targets. This study aims to investigate the role of galactose metabolism (GM) reprogramming in shaping the tumor microenvironment (TME) and driving immunotherapy resistance in KRAS-mutant LUAD. METHODS: Bulk-tissue transcriptomics data were collected from public cohorts such as Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Single-cell RNA-sequencing (scRNA-seq) data, mutational profiles, and advanced bioinformatic approaches were integrated to analyze the correlation between mutation status and metabolic heterogeneity in LUAD. Analytical methods included dimensionality reduction, differential expression, intercellular communication, trajectory inference, gene co-expression network analysis, molecular subtyping, and machine learning-based prognostic modeling. Animal experiment and flow cytometry were performed to validate KDM5A inhibitor (CPI-455) can sensitize KRAS-mutant LUAD cells to immunotherapy. RESULTS: Single-cell analysis revealed reduced immune infiltration and upregulated GM in KRAS-mutant epithelial cells. Two GM-based molecular subtypes with distinct mutational profiles and immune patterns were identified. A prognostic model integrating GM-related genes demonstrated superior predictive performance. KDM5A was identified as a key predictor of ICB resistance. Targeting KDM5A can enhance the sensitivity to immunotherapy in KRAS-mutant LUAD. CONCLUSION: Our findings highlight the therapeutic potential of targeting galactose metabolism-related hub genes to sensitize KRAS-mutant LUAD to immunotherapy.