Research Paper Volume 15, Issue 4 pp 1158—1176

Comprehensive landscape of immune-based classifier related to early diagnosis and macrophage M1 in spinal cord injury

Zhao Zhang1, *, , Zhijie Zhu1, *, , Xuankang Wang1, *, , Dong Liu1, , Xincheng Liu1, , Zhenzhou Mi1, , Huiren Tao2, &, , Hongbin Fan1, ,

  • 1 Department of Orthopaedics, Xi-Jing Hospital, The Fourth Military Medical University, Xi’an 710032, China
  • 2 Department of Orthopaedics, Shenzhen University General Hospital, Shenzhen 518052, China
* Equal contribution

Received: October 31, 2022       Accepted: February 15, 2023       Published: February 23, 2023
How to Cite

Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Numerous studies have documented that immune responses are crucial in the pathophysiology of spinal cord injury (SCI). Our study aimed to uncover the function of immune-related genes (IRGs) in SCI. Here, we comprehensively evaluated the transcriptome data of SCI and healthy controls (HC) obtained from the GEO Database integrating bioinformatics and experiments. First, a total of 2067 DEGs were identified between the SCI and HC groups. Functional enrichment analysis revealed substantial immune-related pathways and functions that were abnormally activated in the SCI group. Immune analysis revealed that myeloid immune cells were predominantly upregulated in SCI patients, while a large number of lymphoid immune cells were dramatically downregulated. Subsequently, 51 major IRGs were screened as key genes involved in SCI based on the intersection of the results of WGCNA analysis, DEGs, and IRGs. Based on the expression profiles of these genes, two distinct immune modulation patterns were recognized exhibiting opposite immune characteristics. Moreover, 2 core IRGs (FCER1G and NFATC2) were determined to accurately predict the occurrence of SCI via machine learning. qPCR analysis was used to validate the expression of core IRGs in an external independent cohort. Finally, the expression of these core IRGs was validated by sequencing, WB, and IF analysis in vivo. We found that these two core IRGs were closely associated with immune cells and verified the co-localization of FCER1G with macrophage M1 via IF analysis. Our study revealed the key role of immune-related genes in SCI and contributed to a fresh perspective for early diagnosis and treatment of SCI.


SCI: spinal cord injury; IRGs: immune-related genes; GEO: gene expression omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes Genomes; GSEA: Gene Set Enrichment analysis; WGCNA: Weighted gene co-expression network analysis; LASSO: least absolute shrinkage and selection operator; qPCR: Quantitative Polymerase Chain Reaction; ROC: receiver operating characteristic curve; WB: western blot; IF: Immunofluorescence; AUC: Area Under Curve; TOM: topological overlap matrix; CDF: cumulative distribution function; PPI: Protein-Protein interaction.