中国神经再生研究(英文版) ›› 2024, Vol. 19 ›› Issue (12): 2723-2734.doi: 10.4103/1673-5374.391306

• 原著:脊髓损伤修复保护与再生 • 上一篇    下一篇

基于加权基因共表达网络分析和机器学习算法筛选的脊髓损伤生物标志物

  

  • 出版日期:2024-12-15 发布日期:2024-03-30
  • 基金资助:
    国家自然科学基金项目(81960417);广西壮族自治区重点研发计划(桂科AB20159027);广西壮族自治区自然科学基金项目(2022GXNSFBA035545)

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

Xiaolu Li1, #, Ye Yang2, #, Senming Xu1, Yuchang Gui1, Jianmin Chen3, *, Jianwen Xu1, *   

  1. 1Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China; 2Department of Rehabilitation Medicine, Guilin People’s Hospital, Guilin, Guangxi Zhuang Autonomous Region, China; 3Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China
  • Online:2024-12-15 Published:2024-03-30
  • Contact: Jianwen Xu, PhD, xujianwen@gxmu.edu.cn; Jianmin Chen, MD, cjm625@163.com.
  • Supported by:
    This study was supported by the National Natural Science Foundation of China, No. 81960417 (to JX); Guangxi Key Research and Development Program, No. GuiKeAB20159027 (to JX); and the Natural Science Foundation of Guangxi Zhuang Autonomous Region, No. 2022GXNSFBA035545 (to YG).

摘要:

免疫炎症反应是脊髓损伤病理过程中的中心事件,可显著影响神经再生和功能恢复,但对外周性免疫炎症反应的了解仍较少。实验首先通过高通量测序获得了脊髓损伤患者外周血中的微小RNA表达谱,同时利用GEO数据库GSE151371获取了脊髓损伤患者的mRNA表达谱。然后利用生物信息学方法鉴定出54种差异表达的微小RNA和1656种差异表达的基因。进而通过功能富集分析发现,多种常见的免疫炎症相关信号通路可在脊髓损伤后发生了异常激活,如中性粒细胞外陷阱形成通路、T细胞受体信号通路、核因子κB信号通路。而后结合加权基因共表达网络分析和LASSO逻辑回归和SVM-RFE算法的机器学习算法筛选出3种与脊髓损伤显著相关的生物标志物ANO10,BST1和ZFP36L2。随后,通过受试者人工曲线分析在原始训练数据集和临床样本中验证了这3种基因的表达水平和诊断性能。定量聚合酶链式反应结果显示,脊髓损伤患者外周血中ANO10和BST1表达升高,而ZFP36L2表达下降。同时,利用Cytoscape构建了微小RNA-mRNA相互作用网络。此外,CIBERSORT工具用于评估SCI患者中外周血中22种免疫细胞的比例,可见与健康受试者相比,脊髓损伤患者的幼稚B细胞、浆细胞、单核细胞和中性粒细胞比例增加,而记忆B细胞、CD8+T细胞、静息自然杀伤细胞、静息树突状细胞和嗜酸性粒细胞比例明显降低,且ANO10, BST1和ZFP26L2表达水平与免疫细胞比例密切相关。该研究结果为脊髓损伤免疫炎症相关治疗策略的制定提供新的方向,并提出ANO10,BST1和ZFP36L2是脊髓损伤的潜在生物标志物。

https://orcid.org/0000-0002-8095-591X (Jianwen Xu); https://orcid.org/0000-0002-0528-5354 (Jianmin Chen)

关键词: RNA测序, GEO数据集, 生物信息学分析, 权基因共表达网络分析, LASSO逻辑回归, SVM-RFE算法, miRNA-mRNA网络, CIBERSORT, 生物标志物, 脊髓损伤

Abstract: 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).

Key words: bioinformatics analysis, biomarker, CIBERSORT, GEO dataset, LASSO, miRNA-mRNA network, RNA sequencing, spinal cord injury, SVM-RFE, weighted gene co-expression network analysis