Wu Yuexuan, Zhao Wen, Yang Yalong, Ma Jinhai
Department of Pediatrics, General Hospital of Ningxia Medical University, Yinchuan, China.
Genetics and Reproductive Medicine, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China.
Medicine (Baltimore). 2025 Aug 1;104(31):e43489. doi: 10.1097/MD.0000000000043489.
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.
儿童哮喘（CA）是一种影响呼吸系统的常见慢性炎症性疾病，中性粒细胞胞外陷阱（NETs）在引发CA中起关键作用。因此，识别与CA治疗相关的NET生物标志物至关重要。在本研究中，利用转录组数据来识别与CA相关的差异表达基因（DEGs）。进行加权基因共表达网络分析以识别与NET相关基因评分相关的模块基因。通过将DEGs与关键模块基因相交来获得候选基因。然后将先进的机器学习技术应用于这些候选基因以识别潜在的生物标志物。随后，基于这些生物标志物进行免疫浸润和基因集富集分析。最后，在转录水平分析所识别的诊断生物标志物的表达水平。共识别出34个与CA相关的DEGs，随后识别出2611个与NET相关基因评分相关的模块基因。使用维恩图选择了11个候选基因进行进一步分析。机器学习技术帮助识别出4个与NETs相关的关键生物标志物：FCGR2B、FCRL5、CCR2和FCRL1。此外，发现5种免疫细胞在CA的免疫微环境中存在差异浸润。所有识别出的生物标志物均与“其他聚糖降解”途径相关，值得注意的是，与对照组相比，这些生物标志物在CA组中表现出显著更高的表达。总之，识别出了4个与CA相关的NET相关生物标志物（FCGR2B、FCRL5、CCR2和FCRL1），为CA治疗方法的开发提供了理论基础。
