Building a Risk Scoring Model for <scp>ARDS</scp> in Lung Adenocarcinoma Patients Using Machine Learning Algorithms

Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS), which increases mortality risks. Investigating the prognostic implications of ARDS-related genes in LUAD is crucial for improving clinical outcomes. Data from TCGA, GEO and GTEx were used to identify 276 ARDS-related genes in LUAD via differential expression analysis. Univariate Cox regression, consensus clustering and machine learning algorithms were used to develop a prognostic risk scoring model. Functional enrichment, immune infiltration analyses, copy number variations and mutational burdens were examined, and the results were validated at the single-cell level. ARDS-related genes significantly impact the prognosis of LUAD patients. A machine learning-based risk scoring model accurately predicted survival rates. Functional enrichment and immune infiltration analyses revealed that these genes are primarily involved in cell cycle regulation and immune cell infiltration. Single-cell expression data supported these findings, and the assessments of copy number variations and mutational burdens highlighted distinct genetic characteristics. This study establishes the prognostic relevance of ARDS-associated genes in LUAD and provides potential biomarkers for personalized therapy and prognosis. Future studies will validate these findings and explore their clinical applications.