Childhood asthma (CA) is a prevalent chronic inflammatory disease affecting the respiratory system, with neutrophil extracellular traps (NETs) playing a key role in triggering CA. Therefore, identifying NET-related biomarkers for CA treatment is crucial. In this study, transcriptome data were utilized to identify differentially expressed genes (DEGs) associated with CA. Weighted gene co-expression network analysis was performed to identify module genes correlated with NET-related gene scores. Candidate genes were obtained by intersecting the DEGs and key module genes. Advanced machine learning techniques were then applied to these candidates to identify potential biomarkers. Subsequently, immune infiltration and gene set enrichment analyses were conducted based on these biomarkers. Finally, the expression levels of the identified diagnostic biomarkers were analyzed at the transcriptional level. A total of 34 DEGs related to CA were identified, followed by the identification of 2611 module genes associated with NET-related gene scores. Eleven candidate genes were selected for further analysis using a Venn diagram. Machine learning techniques helped identify 4 key biomarkers linked to NETs: FCGR2B, FCRL5, CCR2, and FCRL1. Furthermore, 5 immune cells were found to be differentially infiltrated into the immune microenvironment of CA. All identified biomarkers were associated with the "other glycan degradation" pathways, and notably, these biomarkers exhibited significantly higher expression in the CA group compared to the control group. In conclusion, 4 NET-related biomarkers (FCGR2B, FCRL5, CCR2, and FCRL1) linked to CA were identified, providing a theoretical basis for the development of treatments for CA.
Keywords: biomarkers; childhood asthma; immune cells; machine learning; neutrophil extracellular traps.
Copyright © 2025 the Author(s). Published by Wolters Kluwer Health, Inc.