Comprehensive analysis of lung adenocarcinoma: Unveiling differential gene expression, survival-linked genes, subtype stratification, and immune landscape implications.

Abstract

This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity and treatment challenges through a multi-faceted analysis that includes gene expression, genetic subtyping, pathway analysis, immune assessment, and drug sensitivity. It identifies 165 genes with significant expression differences and 46 genes associated with survival, revealing insights into oxidative stress and autophagy. LUAD samples were divided into three subtypes using consensus clustering on these 46 genes, with distinct survival outcomes. Gene Set Enrichment Analysis (GSEA) on HALLMARK gene sets indicated pathway variations with survival implications. The immune landscape, analyzed using the CIBERSORT algorithm, showed different immune cell distributions across subtypes, with the first subtype exhibiting a better immune environment and survival prospects. Advanced machine learning techniques developed a risk model from a set of four genes, effectively categorizing patients into high and low-risk groups, validated through external datasets and analyses. This model linked lower risk scores to better clinical stages, with a higher mutation rate and potential immunotherapy benefits observed in the high-risk group. Drug sensitivity assessments highlighted varied treatment responses between risk groups, suggesting avenues for personalized therapy. This comprehensive analysis enhances the understanding of LUAD's molecular and clinical nuances, offering valuable insights for tailored treatment approaches.