Neutrophil Extracellular Trap‐Related Gene Signatures and Molecular Clusters in Severe Influenza: Identification Through Integrative Transcriptome Analysis

BACKGROUND: Neutrophil extracellular traps (NETs) are recently discovered structures in which neutrophils trap pathogens in web-like structures composed of chromatin and proteolytic material. NETs have been linked to tissue damage in severe influenza (sFlu) pathogenesis. The present article involved a thorough analysis of NET-related gene (NRG) expression patterns and immunological characteristics in sFlu. METHODS: Microarray datasets were downloaded from the GEO database. sFlu-related NRGs (sFlu-NRGs) were screened using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Hub sFlu-NRGs were identified using LASSO regression, support vector machine (SVM), and random forest (RF) models. Hub genes were subsequently validated using an additional external dataset, clinical samples, and a nomogram model. The molecular clusters in sFlu were investigated based on the hub sFlu-NRGs using consensus clustering and related immune cell infiltration. RESULTS: A total of 13 sFlu-NRGs were identified. Using these 13 genes, five (PRTN3, MMP8, myeloperoxidase [MPO], bactericidal permeability-increasing [BPI], and LTF) hub sFlu-NRGs were identified using three machine learning algorithms. Nomogram calibration and receiver operating characteristic (ROC) analysis results suggested that accuracy was achieved in predicting sFlu. Two molecular clusters were defined in sFlu based on the five hub genes. Single-set gene expression analyses suggested that, compared with Cluster 2, Cluster 1 had a decreased adaptive immune response. CONCLUSIONS: Five hub NRGs and two distinct NET-related clusters were identified in sFlu patients, highlighting the mechanism of action of sFlu and identifying candidate anti-sFlu therapeutic targets.