Exploring potential biomarkers of NETosis-Related genes in spinal cord injury through machine learning and multi-omics analysis

Spinal cord injury (SCI) is a serious condition typically caused by mechanical trauma, often resulting in significant motor, sensory, and autonomic dysfunction. It places a heavy burden on individuals, families, and society; however, effective treatment options are still limited because of the complex pathophysiology behind primary and secondary injury mechanisms. Neutrophil extracellular traps (NETs) created by neutrophils play a crucial role in exacerbating secondary injury following spinal cord injury by promoting inflammation and hindering neural repair. This study aims to clarify the molecular basis of neutrophil extracellular trap-related genes (NRGs) in SCI through an integrated bioinformatics approach. We utilized the GSE151371 dataset from the GEO database, which includes gene expression profiles from 38 SCI patients and 10 healthy controls, and we identified differentially expressed genes (DEGs) using the limma package in R. We identified 4878 DEGs, and we performed functional analysis of these genes using GO and KEGG. Immune cell infiltration analysis conducted with CIBERSORT showed significant differences in immune cell populations between the SCI group and the control group, with notable differences in the infiltration of Neutrophils, B cells memory and Macrophages M0. Weighted gene co-expression network analysis (WGCNA) identified a module highly associated with SCI, which resulted in the selection of 12 candidate genes. We built a predictive model using machine learning algorithms, identifying NLRP3, LRG1, and TLR8 as key genes with high diagnostic potential (AUC >0.9). Subsequently, through multi-omics analysis, including gene set enrichment analysis (GESA), protein interaction analysis, and correlation analysis between key genes and immune cells, we explored the relationship between key NRGs and the pathological processes in SCI patients. Finally, these findings were validated through molecular biology experiments in a rat SCI model and clinical samples, confirming the clinical relevance of our findings regarding these biomarkers. In summary, this study provides a comprehensive analysis of NRGs in SCI, highlighting their diagnostic and therapeutic potential. Future research could focus on developing interventions targeting NETs formation, providing new opportunities to enhance treatment outcomes for SCI.