Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

BACKGROUND: Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist. METHODS: To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network. RESULTS: The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the co-expression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI. CONCLUSIONS: These results may provide a novel therapeutic strategy for the treatment of SCI.