Abstract：
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.
摘要：
中性粒细胞胞外诱捕网（NETs）在慢性气道疾病中起着重要作用。然而,将NETs与严重哮喘的发展联系起来的精确遗传基础仍然难以捉摸.这项研究旨在揭示严重哮喘中NET相关基因（NRGs）的分子特征，并可靠地鉴定相关分子簇和生物标志物。我们分析了来自基因表达综合数据库的RNA-seq数据。相互作用分析揭示了50个差异表达的NRGs(DE-NRGs)。随后，非负矩阵分解算法对重症哮喘患者的样本进行分类.然后，机器学习算法确定了与严重哮喘高度相关的核心NRG。将DE-NRG关联并进行蛋白质-蛋白质相互作用分析。核心基因表达谱的无监督一致聚类描绘了表征严重哮喘的两个不同的聚类(C1和C2)。功能富集突出了C2簇中的免疫相关途径。核心基因选择包括Boruta算法，支持向量机，和最小绝对收缩和选择算子算法。通过受试者工作特征曲线评估诊断性能。这项研究探讨了成人重度哮喘中NRGs的分子特征，基于DE-NRG揭示不同的聚类。鉴定了潜在的生物标志物（TIMP1和NFIL3），这些生物标志物可能对严重哮喘的早期诊断和治疗很重要。
