中国神经再生研究(英文版) ›› 2026, Vol. 21 ›› Issue (7): 3249-3266.doi: 10.4103/NRR.NRR-D-25-00080

• 原著:脊髓损伤修复保护与再生 • 上一篇    下一篇

整合与单细胞转录组分析揭示脊髓损伤相关RNA修饰生物标志物

  

  • 出版日期:2026-07-15 发布日期:2026-04-01

Integrative bulk and single-cell transcriptome analyses reveal RNA modification–related biomarkers of spinal cord injury

Shixue Huang1, #, Kun Jiao1, #, Keqing Li2, #, Jiayan Yuan1, Haoming Shu1, Yinuo Zhang1, Xin Zhou1, *, Xuhui Zhou1, *   

  1. 1Department of Orthopedics, Changzheng Hospital, The Second Affiliated Hospital of Naval Medical University, Shanghai, China; 2Department of Nursing, Changzheng Hospital, The Second Affiliated Hospital of Naval Medical University, Shanghai, China
  • Online:2026-07-15 Published:2026-04-01
  • Contact: Xin Zhou, MD, zhouxin1993@126.com; Xuhui Zhou, MD, zxh_czjz@126.com.

摘要:

RNA修饰异常表达与多种疾病发病机制相关,但其在脊髓损伤中的具体分子机制尚不明确。此研究旨在探索脊髓损伤相关的RNA修饰生物标志物。从基因表达综合数据库中检索了脊髓损伤小鼠的mRNA表达谱GSE18179,通过生物信息学方法鉴定出185个差异表达基因。功能富集分析表明,脊髓损伤小鼠样本中常见代谢相关通路(包括硫代谢和类固醇生物合成)存在异常激活或抑制。采用加权基因共表达网络分析、随机森林、支持向量机及广义线性模型的整合策略,鉴定出与脊髓损伤相关的4个生物标志物:Elovl6,Idi1,Sqle和Stbd1。通过受试者工作特征曲线,在原始训练数据集和小鼠样本中验证了这4个基因的表达水平及诊断性能。定量反转录PCR结果显示,伤后不同时间点假手术组与脊髓损伤组间4个基因mRNA水平存在差异。还运用Cytoscape构建了微小RNA-mRNA及转录因子-mRNA相互作用网络,并通过CIBERSORT工具评估了小鼠脊髓中22种免疫细胞的比例。通过CIBERSORT工具分析小鼠脊髓中22种免疫细胞的比例,发现与健康脊髓相比,损伤脊髓的记忆B细胞、静息树突状细胞、M0巨噬细胞、活化肥大细胞、静息肥大细胞及CD8+ T细胞比例显著改变。单细胞测序分析识别出小胶质细胞和T细胞是关键细胞成分。这些发现为脊髓损伤相关RNA修饰治疗策略的开发提供了新方向,并提示Elovl6,Idi1,Sqle和Stbd1具有潜在生物标志物价值。


https://orcid.org/0009-0007-0935-3995 (Xin Zhou); https://orcid.org/0009-0002-6399-931X (Xuhui Zhou)

关键词: 生物标志物, 机器学习, miRNA-mRNA网络, RNA测序, RNA修饰, 单细胞测序分析, 脊髓损伤, 转录因子-mRNA网络, 加权基因共表达网络分析

Abstract: Aberrant RNA modification has been linked to the pathogenesis of various diseases; however, its specific molecular mechanisms in spinal cord injury remain poorly understood. The objective of this study was to explore RNA modification–related biomarkers of spinal cord injury. The mRNA expression profiles of mice with spinal cord injury were retrieved from the Gene Expression Omnibus (GEO) database (GSE18179). We identified 185 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis demonstrated aberrant activation or inhibition of common metabolism–related pathways, including sulfur metabolism and steroid biosynthesis, in mice with spinal cord injury. An integrated strategy comprising weighted gene co-expression network analysis, a random forest model, a support vector machine model, and a generalized linear model was employed to identify four genes whose aberrant RNA modification was linked to spinal cord injury: Elovl6, Idi1, Sqle, and Stbd1. We verified the expression levels and diagnostic performance of these four genes in the original training dataset and mouse samples via receiver operating characteristic curve analysis. Quantitative reverse transcription-polymerase chain reaction demonstrated variations in the mRNA levels of the four genes between the Sham and spinal cord injury groups at different time points following injury. We also constructed microRNA–mRNA and transcription factor–mRNA interaction networks using Cytoscape. Additionally, we evaluated the proportions of 22 types of immune cells in the spinal cords of mice using the CIBERSORT tool, revealing significant alterations in the numbers of memory B cells, resting dendritic cells, M0 macrophages, activated mast cells, resting mast cells, and CD8+ T cells in spinal cord injury mice compared with Sham controls. Microglia and T cells were identified as key cell types by single-cell sequencing analysis. These findings provide new directions for the development of RNA modification–related therapeutic strategies for spinal cord injury and suggest that Elovl6, Idi1, Sqle, and Stbd1 are potential biomarkers of spinal cord injury. 

Key words: biomarkers, machine learning, microRNA–mRNA (miRNA–mRNA) network, RNA sequencing, RNA modification, single-cell sequencing analysis, spinal cord injury, transcription factor–mRNA network, weighted gene co-expression network analysis