Yu Jia, Xiao Tiantian, Pan Yun, He Yangshen, Tan Jiaxiong
Department of Internal Medicine, Dongguan Hospital of Integrated Chinese and Western Medicine, Dongguan, Guangdong Province, People's Republic of China.
Department of Infectious Disease, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, People's Republic of China.
Int J Gen Med. 2025 Apr 25;18:2247-2261. doi: 10.2147/IJGM.S516139. eCollection 2025.
Neutrophil trap (NET) is an important feature of chronic inflammatory diseases. At present, there are still few studies to explore the characteristics of NET in different chronic obstructive pulmonary disease (COPD) patients. This study aimed to identify NET signature genes in different COPD patients.
We analyzed single-cell RNA sequencing data from COPD and non-COPD individuals to identify differentially expressed neutrophil genes. Machine learning algorithms were applied to construct models A and B, specific to smoking and non-smoking COPD patients, respectively.
Through single-cell cluster analysis, 165 neutrophil characteristic genes in COPD group were successfully identified. Model A, consisting of key genes CD63, RNASE2, ERAP2, and model B, consisting of GRIPAP1, NHS, EGFLAM, and GLUL, were validated internally and externally, showing significant risk scores and good diagnostic efficacy (AUC: 60.24-87.22). Alveolar lavage fluid in patients with COPD was studied and confirmed higher expression levels of RNASE2 and NHS in severe COPD patients.
The study successfully developed NET signature gene models for identifying smoking and non-smoking COPD respectively, with validated specificity and predictive power, offering a foundation for personalized treatment strategies.
中性粒细胞胞外陷阱（NET）是慢性炎症性疾病的一个重要特征。目前，探索不同慢性阻塞性肺疾病（COPD）患者中NET特征的研究仍较少。本研究旨在识别不同COPD患者的NET特征基因。
我们分析了COPD患者和非COPD个体的单细胞RNA测序数据，以识别差异表达的中性粒细胞基因。应用机器学习算法构建分别针对吸烟和非吸烟COPD患者的模型A和模型B。
通过单细胞聚类分析，成功识别出COPD组中的165个中性粒细胞特征基因。由关键基因CD63、RNASE2、ERAP2组成的模型A和由GRIPAP1、NHS、EGFLAM、GLUL组成的模型B在内部和外部均得到验证，显示出显著的风险评分和良好的诊断效能（AUC：60.24 - 87.22）。对COPD患者的肺泡灌洗液进行研究，证实重度COPD患者中RNASE2和NHS的表达水平较高。
本研究成功开发了分别用于识别吸烟和非吸烟COPD的NET特征基因模型，具有验证的特异性和预测能力，为个性化治疗策略提供了基础。
