Translational Oncology
Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches
By Long Qian, Juan Zhao, Mengtao Fan and 2 others.
Abstract
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.