ArticleInternational journal of general medicine2025
Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm: Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Uncovering sodium overload-associated gene signatures in chronic obstructive pulmonary disease through integrated bioinformatics and machine learning.Journal of thoracic disease · 2026Article
- The Evolving Landscape of COPD Typization.Medicina (Kaunas, Lithuania) · 2026Review
- Mitochondrial reprogramming of neutrophil extracellular traps in chronic obstructive pulmonary disease and asthma: toward a phenotype-driven therapeutic framework.Frontiers in immunology · 2026Review
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Authors and funding
5 authors.
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Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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