Multiomics and Machine Learning Reveal Distinct Immune-Metabolic Signatures and Diagnostic Biomarkers for Asthma Inflammatory Endotypes.

ABSTRACT: Background: bronchial asthma is a highly heterogeneous chronic inflammatory disease, with eosinophilic (EA) and neutrophilic (NEA) asthma subtypes exhibiting distinct pathological mechanisms and treatment responses. However, the protein-metabolite interactions underlying the pathogenesis of asthma remain poorly understood, limiting the development of precision diagnostics and therapeutics. Methods: this study integrated proteomics, metabolomics, and bioinformatics analyses to perform multiomics profiling of sputum samples from 53 asthma patients (27 EA, 20 NEA) and 53 healthy controls. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and CIBERSORT immune cell infiltration analysis were systematically applied to characterize the molecular

SUBMITTER: Liu S 

PROVIDER: S-EPMC12658698 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature