Identification of key genes and pathways associated with spinal cord injury

Spinal cord injury (SCI) is a devastating traumatic injury, which can lead to severe motor, sensory and autonomic nervous dysfunction. This study is based on the GSE5296 expression profile dataset, including 42 control samples and 54 SCI samples. Differential analysis was used to identify differentially expressed genes between control and SCI mices, and weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression network. A protein-protein interaction network was constructed based on the intersection of differential genes and module genes. Nine hub genes (Itgb2, Tyrobp, Fcer1g, Fcgr3, Fcgr1, Ptprc, Mpeg1, Cd86 and Itgax) were selected by Cytoscape's comprehensive algorithm based on MMC, MNC, EPC, degree and proximity. In addition, the results of the hub gene were verified, and the Receiver Operating Characteristic (ROC) curve was drawn to evaluate the diagnostic efficiency. Subsequently, the expression levels of these genes were confirmed and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). These hub genes are enriched in immune-related pathways, especially leukocyte-mediated immunity, phagocytosis and membrane transport pathways. Gene Set Enrichment Analysis (GSEA) was used to explore the molecular mechanism of biomarkers. In this study, CIBERSORT was used to identify cell types to evaluate the inflammatory state of SCI. To study the relationship between key markers and infiltrating immune cells. In addition, we also use Cytoscape software to construct the Competing Endogenous RNA (ceRNA) regulatory network of biomarkers and obtain potential targeted drugs in the DGIdb database (Drug-Gene Interaction database). Finally, the reliability of Hub gene was verified by using GSE132242 dataset. These findings provide a new target for future research and a new perspective for the pathogenesis of spinal cord injury.