Unraveling the Molecular Landscape of Neutrophil Extracellular Traps in Severe Asthma: Identification of Biomarkers and Molecular Clusters

Neutrophil extracellular traps (NETs) play a central role in chronic airway diseases. However, the precise genetic basis linking NETs to the development of severe asthma remains elusive. This study aims to unravel the molecular characterization of NET-related genes (NRGs) in severe asthma and to reliably identify relevant molecular clusters and biomarkers. We analyzed RNA-seq data from the Gene Expression Omnibus database. Interaction analysis revealed fifty differentially expressed NRGs (DE-NRGs). Subsequently, the non-negative matrix factorization algorithm categorized samples from severe asthma patients. A machine learning algorithm then identified core NRGs that were highly associated with severe asthma. DE-NRGs were correlated and subjected to protein-protein interaction analysis. Unsupervised consensus clustering of the core gene expression profiles delineated two distinct clusters (C1 and C2) characterizing severe asthma. Functional enrichment highlighted immune-related pathways in the C2 cluster. Core gene selection included the Boruta algorithm, support vector machine, and least absolute contraction and selection operator algorithms. Diagnostic performance was assessed by receiver operating characteristic curves. This study addresses the molecular characterization of NRGs in adult severe asthma, revealing distinct clusters based on DE-NRGs. Potential biomarkers (TIMP1 and NFIL3) were identified that may be important for early diagnosis and treatment of severe asthma.

Bioinformatics; Biomarkers; Machine learning; Molecular clusters; Neutrophil extracellular traps (NETs); Severe asthma.