{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:09:13Z","timestamp":1785337753926,"version":"3.55.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"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><jats:title>Background<\/jats:title><jats:p>Spinal cord injury (SCI) is a devastating disorder featuring serious motor dysfunction and proprioceptive deficits due to central nervous system (CNS) damage. During its progression, macrophage-microglia (MM) cells are rapidly activated and play pivotal roles in the inflammatory response through various mechanisms. However, limited research has investigated the differential gene expression between the two groups before and after injury, and these important genes may therefore serve as potential biological diagnostic markers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our study utilized the Gene Expression Omnibus (GEO) data for SCI-related MDEG identification. Key hub genes were screened using multiple machine learning(ML) algorithms. Their predictive potential was subsequently validated using independent datasets, and their association with immune cell infiltration was assessed. An <jats:italic>in vitro<\/jats:italic> model of SCI was established, and quantitative polymerase chain reaction (qPCR) experiments were conducted to verify our findings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Single-cell RNA sequencing identified 16 distinct cellular subpopulations, among which MM cells were split into four subsets according to functional characteristics. Enrichment analyses, including KEGG, GO, and GSEA, revealed that the MDEGs were closely linked to key biological processes in SCI. From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. Predictive models based on these genes demonstrated strong performance in both internal training and external validation cohorts. Preliminary analysis of immune infiltration and gene-immune cell correlation suggests an association with M2 macrophages. Furthermore, <jats:italic>in vitro<\/jats:italic> modeling of post-injury inflammation confirmed the elevated expression of EMP3, GNGT2, and SGPL1.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our study identifies EMP3, GNGT2, and SGPL1 as potential diagnostic biomarkers associated with MM cells in SCI. The foregoing findings lay a theoretical basis for elucidating their biological roles and formulating future treatment strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1634014","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T04:18:07Z","timestamp":1757477887000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord 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