Biomarkers for predicting the severity of spinal cord injury by proteomic analysis - Wei, Liangfeng; Huang, Yubei; Chen, Yehuang; Wu, Jianwu; Chen, Kaiqin; Zheng, Zhaocong; Wang, Shousen; Xue, Liang (Frontiers in Molecular Neuroscience, 2023)
Purpose: Currently, there is a shortage of the protein biomarkers for classifying spinal cord injury (SCI) severity. We attempted to explore the candidate biomarkers for predicting SCI severity.Methods: SCI rat models with mild, moderate and severe injury were constructed with electro-mechanic impactor. The behavior assessment and pathological examinations were conducted before and after SCI. Then, quantitative liquid chromatography-mass spectrometry (LC-MS/MS) was performed in spinal cord tissues with different extent of injury. The differentially expressed proteins (DEPs) in SCI relative to controls were identified, followed by Mfuzz clustering, function enrichment analysis and protein-protein interaction network construction. The differential changes of candidate proteins were validated by using parallel reaction monitoring (PRM) assay.Results: After SCI modeling, the motor function and mechanical pain sensitivity of SCI rats were impaired, dependent on the severity of injury. Total 154 DEPs were overlapped in mild, moderate and severe SCI group, among which 82 proteins were classified in cluster 1, 2, 3, 5 and 6 with similar expression pattern at different extent of injury. DEPs were closely related with inflammatory response and significantly enriched in IL-17 signaling pathway. PPI network showed that Fgg (Fibrinogen gamma chain), Fga (Fibrinogen alpha chain), Serpinc1 (Antithrombin-III) and Fgb (Fibrinogen Fibrinogen beta chain) in cluster 1 were significant nodes with largest degrees. The upregulation of the significant nodes in SCI samples was validated by PRM.Fgg, Fga, and Fgb may be the putative biomarkers for assessing the extent of SCI.