Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm - Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD

Aug 2, 2026

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Chronic obstructive pulmonary disease involves chronic systemic inflammation and heterogeneous patient features, yet neutrophil extracellular trap (NET) characteristics remain insufficiently defined across different COPD subgroups. This study analyzes single-cell RNA sequencing data from COPD and non-COPD individuals to identify neutrophil NET signature genes, then applies machine learning to construct models for smoking and non-smoking COPD patients. Cluster analysis identifies 165 neutrophil characteristic genes, and model validation shows significant risk scores and diagnostic performance. Higher RNASE2 and NHS expression is confirmed in severe COPD, supporting NET signature models for personalized strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background

Methods

Results

Single-cell gene identification

Model construction and validation

Alveolar lavage findings

Conclusion

International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. International Journal of General Medicine Open Access Full Text Article ORIGINAL RESEARCH Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm: Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD Jia Yu 1 , *, Tiantian Xiao 1 , *, Yun Pan 2 , *, Yangshen He 1 , Jiaxiong Tan 3 1Department of Internal Medicine, Dongguan Hospital of Integrated Chinese and Western Medicine, Dongguan, Guangdong Province, People’s Republic of China; 2Department of Infectious Disease, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, People’s Republic of China; 3Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, People’s Republic of China *These authors contributed equally to this work Correspondence: Yangshen He, Dongguan Hospital of Integrated Chinese and Western Medicine, Dongguan, Guangdong Province, 523000, People’s Republic of China, Email [13713193315@163.com](13713193315@163.com); Jiaxiong Tan, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300202, People’s Republic of China, [Email gdydtjx@163.com](Email gdydtjx@163.com) | 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.
Keywords: COPD, neutrophil extracellular traps, single-cell sequencing, transcriptomics | | --- | | Introduction
Chronic obstructive pulmonary disease (COPD) is now recognized as a complex, multicomponent disease characterized by chronic systemic inflammation and is currently the third leading cause of death worldwide.1 The mortality rate for male COPD patients is higher, with an increasing trend in mortality rates among those over 45 years of age.2 In developing countries, mortality rates are also increasing in tandem with the rise in smoking rates. In China, tobaccorelated deaths account for 12% of all deaths, and predictions suggest that this proportion could reach 33% by 2030.3 Although the hazardous role of smoking in the development of COPD is well established, nearly half of COPD patients are non-smokers, particularly women exposed to biomass smoke in poorly ventilated homes.4
The high mortality rate associated with COPD is linked to its progressive, irreversible airway obstruction and complex comorbidities.5,6 Each bacterial or respiratory viral infection experienced by this population exponentially increases the risk of adverse outcomes.6 Currently, identified endogenous factors associated with COPD include alpha-1 | Received: 7 Ja

FAQs

What is the study’s main objective for COPD patients?

To identify NET signature genes and develop predictive gene models that differentiate COPD patients by smoking status.

How were the NET signature gene models constructed?

Single-cell RNA sequencing data were analyzed to find differentially expressed neutrophil genes, then machine learning models were built for smoking and non-smoking COPD groups.

Which genes were included in the two validated models?

Model A included CD63, RNASE2, and ERAP2, while model B included GRIPAP1, NHS, EGFLAM, and GLUL.

Aug 2, 2026

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