Multi-omics integration and machine learning identify and validate neutrophil extracellular trap-associated gene signatures in chronic rhinosinusitis with nasal polyps

Abstract

This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine learning algorithms identified three core NETs-associated genes: CXCR4, CYBB, and PTAFR, which were significantly upregulated in CRSwNP patients. The diagnostic performance of these genes was evaluated using receiver operating characteristic (ROC) curves, and their clinical relevance was validated using multicenter data. Immune infiltration analysis showed strong correlations between these genes and neutrophil and immune cell infiltration. Single-cell RNA sequencing demonstrated that these genes were predominantly expressed in myeloid and immune cells and exhibited dynamic changes during disease progression. These genes may contribute to CRSwNP pathogenesis through IL-17 signaling and metabolism-related pathways. This study identifies novel biomarkers and therapeutic targets for precise diagnosis and personalized treatment of CRSwNP.