Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

• Published in: Neural regeneration research Vol. 19; no. 12; pp. 2723 - 2734
• Main Authors: Li, Xiaolu, Yang, Ye, Xu, Senming, Gui, Yuchang, Chen, Jianmin, Xu, Jianwen
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01.12.2024; Medknow Publications & Media Pvt. Ltd; Department of Rehabilitation Medicine,The First Affiliated Hospital of Guangxi Medical University,Nanning,Guangxi Zhuang Autonomous Region,China%Department of Rehabilitation Medicine,Guilin People's Hospital,Guilin,Guangxi Zhuang Autonomous Region,China%Department of Rehabilitation Medicine,The First Affiliated Hospital of Fujian Medical University,Fuzhou,Fujian Province,China; Wolters Kluwer Medknow Publications
• Edition: 2
• Subjects: bioinformatics analysis; biomarker; cibersort; geo dataset; lasso; mirna-mrna network; rna sequencing; spinal cord injury; svm-rfe; weighted gene co-expression network analysis; Biomarkers; Genes; Machine learning; MicroRNAs; Research Article; Spinal cord injuries; T cell receptors; RNA sequencing; spinal cord injury; weighted gene co-expression network analysis; CIBERSORT; SVM-RFE; LASSO; GEO dataset; miRNA-mRNA network; biomarker; bioinformatics analysis
• ISSN: 1673-5374; 1876-7958
• DOI: 10.4103/1673-5374.391306

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).