ArticleInternational journal of molecular sciences2025
Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical and Imaging Data-based Machine Learning for Early Diagnosis of Bronchopulmonary Dysplasia: A Meta-analysis.Current medical imaging · 2025Pooled it
- Ginkgolide B Alleviates Airway Inflammation in Hyperoxia Lung Injury.Immunity, inflammation and disease · 2026Article
- Identification and validation of NETs-related biomarkers in hepatocellular carcinoma through bioinformatics analysis and machine learning algorithms.Discover oncology · 2026Article
- Exploring blood transcriptional signatures for thrombosis diagnosis in critically ill patients with COVID-19.Blood vessels, thrombosis & hemostasis · 2026Article
- The Inflammatory Nexus of Bronchopulmonary Dysplasia: From Molecular Pathways to Precision Therapeutics.Mediators of inflammation · 2026Review
- Integrative bulk and single-cell transcriptomic profiling identifies core gene networks and potential therapeutic targets in glioma.BMC cancer · 2025Article
- Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.Frontiers in immunology · 2025Article
- Neutrophil extracellular traps in ischemic stroke: mechanisms, clinical implications, and therapeutic potential.Frontiers in neurology · 2025Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neutrophil extracellular traps (NETs) play a key role in the development of bronchopulmonary dysplasia (BPD), yet their molecular mechanisms in contributing to BPD remain unexplored. Using the GSE32472 dataset, which includes 100 blood samples from postnatal day 28, we conducted comprehensive bioinformatics analyses to identify differentially expressed genes (DEGs) and construct gene modules. We identified 86 DEGs, which were enriched in immune and inflammatory pathways, including NET formation. Weighted gene co-expression network analysis (WGCNA) revealed a key gene module associated with BPD. By intersecting 69 NET-related genes (NRGs), 149 module genes, and 86 DEGs, we identified 12 differentially expressed NET-related genes (DENRGs). Immune infiltration analysis revealed an increase in neutrophils, dendritic cells, and macrophages in BPD patients. Machine learning models (LASSO, SVM-RFE, and RF) identified 5 upregulated biomarkers-MMP9, Siglec-5, DYSF, MGAM, and S100A12-showing potential as diagnostic biomarkers for BPD. Validation using nomogram, ROC curves, and qRT-PCR confirmed the diagnostic accuracy of these biomarkers. Clinical data analysis showed that Siglec-5 was most strongly correlated with BPD severity, while DYSF correlated with the grade of retinopathy of prematurity (ROP) and its laser treatment. Clustering analysis revealed two distinct BPD subtypes with different immune microenvironment profiles. Drug-gene interaction analysis identified potential inhibitors targeting MGAM and MMP9. In conclusion, the study identifies five NET-related biomarkers as reliable diagnostic tools for BPD, with their upregulation and association with disease severity and complications, such as ROP, highlighting their clinical relevance and potential for advancing BPD diagnostics and treatment.
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