# Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in spinal cord injury: insights from integrated bioinformatics analyses and experiments

## TL;DR

This study explores how neutrophil extracellular trap-related genes contribute to spinal cord injury and identifies FCGR1A as a potential biomarker linked to immune responses.

## Contribution

The study integrates bioinformatics and experiments to identify FCGR1A as a novel NET-related biomarker in spinal cord injury.

## Key findings

- Ten NET-related genes were identified as differentially expressed in spinal cord injury.
- FCGR1A was validated as a hub gene with significant differential expression in SCI.
- FCGR1A expression correlates with specific immune cell types and is upregulated in SCI models.

## Abstract

Spinal cord injury (SCI) is a debilitating neurological condition that results in severe motor, sensory, and autonomic dysfunction, imposing a considerable burden on affected individuals and healthcare systems. Neutrophil extracellular traps (NETs) have been increasingly implicated in inflammatory and immune responses; however, the roles of NETs-related genes (NRGs) in SCI remain poorly understood. This study aimed to investigate the involvement of NRGs in SCI pathophysiology and to identify NET-associated candidate genes of potential biological relevance.

The GSE151371 dataset was obtained from the Gene Expression Omnibus (GEO) to identify NRGs associated with SCI. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to screen candidate genes, followed by machine learning algorithms for hub gene prioritization. The identified hub genes were validated using an independent dataset (GSE45006). Immune cell composition in peripheral blood samples was estimated using the CIBERSORT algorithm based on a predefined leukocyte gene signature matrix. In addition, the expression of the hub gene was validated in a rat SCI model using RT-qPCR and immunofluorescence.

We identified ten intersecting genes as candidate differentially expressed NRGs in SCI. After prioritization of hub genes using multiple machine learning algorithms, FCGR1A, CLEC6A, and RETN were identified. Subsequent validation in the independent dataset GSE45006 demonstrated that only FCGR1A showed significant differential expression. In SCI samples, FCGR1A expression showed a positive correlation with activated mast cells and naïve CD4+ T cells, while exhibiting a negative correlation with naïve B cells and resting memory CD4+ T cells. Moreover, in vivo experiments confirmed the upregulation of FCGR1A at both the mRNA and protein levels in SCI models, supporting its association with SCI-related inflammatory responses.

This study provides integrative bioinformatics and experimental evidence supporting the involvement of NETs-related genes in SCI and identifies FCGR1A as a NET-associated biomarker candidate linked to immune and inflammatory responses in SCI, warranting further mechanistic investigation.