Identification of Ifitm1 as a Pivotal Gene in Mouse Spinal Cord Injury Using Comprehensive Machine Learning Algorithms.

Background: Spinal cord injury (SCI) functions as a medical condition leading to substantial motor and neurological system deteriorations. Researchers must understand the molecular mechanisms of this disease because it serves as a foundation for creating therapeutic solutions. Methods: This study analyzed the single-cell dataset GSE189070 and microarray datasets GSE47681, GSE92657, and GSE93561 retrieved from the GEO database. Using R packages "Seurat" and "Celldex," we identified and annotated cell clusters in single-cell data. Combined microarray datasets underwent differential expression analysis, WGCNA, and machine learning to identify key hub genes. Immune cell associations were assessed using Cibersort, while the connection map (CMap) database was employed to predict small-molecule drugs targeting the identified genes. Experimental validation confirmed findings. Results: In datasets involving single cells, granulocyte subpopulations denote unique cellular populations related to SCI. The high-dimensional weighted gene co-expression network analysis (hdWGCNA) algorithm pinpointed crucial modules linked to granulocyte subgroups, particularly from the black, green, and yellow modules. In the SCI cohort, Ifitm1 emerged as a potential hub gene. Importantly, Ifitm1 shows a significant positive correlation with M1 macrophages. Utilizing the CMap database along with molecular docking investigations, the small-molecule drug NVP-AUY922, which interacts with Ifitm1, was discovered. Experimental assessments revealed that Ifitm1 is linked to macrophage inflammation following SCI. Conclusion: This study revealed the importance of granulocyte subsets and Ifitm1 in SCI, proposed Ifitm1 as a potential therapeutic target, and provided new insights into the molecular mechanisms of SCI.