Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.

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.