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

作　　者：Xiaolu Li Ye Yang Senming Xu Yuchang Gui Jianmin Chen Jianwen Xu 

机构地区：[1]Department of Rehabilitation Medicine,The First Affiliated Hospital of Guangxi Medical University,Nanning,Guangxi Zhuang Autonomous Region,China [2]Department of Rehabilitation Medicine,Guilin People’s Hospital,Guilin,Guangxi Zhuang Autonomous Region,China [3]Department of Rehabilitation Medicine,The First Affiliated Hospital of Fujian Medical University,Fuzhou,Fujian Province,China

出　　处：《Neural Regeneration Research》2024年第12期2723-2734,共12页中国神经再生研究（英文版）

基　　金：supported by the Notional Natural Science Foundation of China,No.81960417 (to JX);Guangxi Key Research and Development Program,No.GuiKeA B20159027 (to JX);the Natural Science Foundation of Guangxi Zhuang Autonomous Region,No.2022GXNSFBA035545 (to YG)。

摘　　要：Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd 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 diffe rentially 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,we re abnormally activated or inhibited in spinal cord inju ry 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 biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance 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 ANO20 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 co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,rest

关 键 词：bioinformatics analysis BIOMARKER CIBERSORT GEO dataset LASSO miRNA-mRNA network RNA sequencing spinal cord injury SVM-RFE weighted gene co-expression network analysis 

分 类 号：R651.2[医药卫生—外科学]