{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:41:06Z","timestamp":1772692866941,"version":"3.50.1"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 na\u00efve CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells, while exhibiting a negative correlation with na\u00efve B cells and resting memory CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells. Moreover,\n                      <jats:italic>in vivo<\/jats:italic>\n                      experiments confirmed the upregulation of FCGR1A at both the mRNA and protein levels in SCI models, supporting its association with SCI-related inflammatory responses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  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