中国神经再生研究(英文版) ›› 2025, Vol. 20 ›› Issue (6): 1764-1775.doi: 10.4103/NRR.NRR-D-23-01069

• 原著:脊髓损伤修复保护与再生 • 上一篇    下一篇

外周血RNA表达谱可预测退行性脊髓型颈椎病及严重程度

  

  • 出版日期:2025-06-15 发布日期:2024-11-12

Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathy

Zhenzhong Zheng, Jialin Chen, Jinghong Xu, Bin Jiang, Lei Li, Yawei Li, Yuliang Dai* , Bing Wang*   

  1. Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
  • Online:2025-06-15 Published:2024-11-12
  • Contact: Bing Wang, MD, PhD, wbxyeyy@csu.edu.cn; Yuliang Dai, MD, squaer_d@hotmail.com.
  • Supported by:
    This work was supported by Hunan Provincial Key Research and Development Program, No. 2021SK2002 (to BW) and the Natural Science Foundation of Hunan Province of China (General Program), No. 2021JJ30938 (to YL).

摘要:

退行性脊髓型颈椎病是导致脊髓功能障碍最常见的原因之一,其症状持续时间较长,且损伤严重程度较高,预后较差。尽管大量研究探索了急性脊髓损伤的血清生物标志物,但对诊断退行性脊髓型颈椎病的生物标志物的探索仍然却很少。此次试验收集了30例退行性脊髓型颈椎病患者(51.3±7.3岁,12女18男),7名健康对照(25.7±1.7岁,1女6男),以及9例神经根型颈椎病患者(51.9±8.6岁,3女6男)的外周血样本,发现3组患者的血液样本在转录组学特征上呈现明显差异。进一步对差异基因表达行富集分析,确定了128种与神经功能障碍相关的退行性脊髓型颈椎病特异性差异表达基因。然后利用LASSO分析从中筛选出5种基因TBCD、TPM2、PNKD、EIF4G2和AP5Z1,构建了一个诊断模型,其在退行性脊髓型颈椎病诊断上的准确率达到93.5%。此外,以TCAP和SDHA构建的模型可区分轻度和重度退行性脊髓型颈椎病,准确率分别为83.3%和76.7%。而记忆性B细胞和记忆活化CD4+ T细胞这2种免疫细胞类型也能够有效预测退行性脊髓型颈椎病病变的数量,准确率达到80%。上述结果提示血液中的mRNA表达谱可用于预测退行性脊髓型颈椎病及严重程度。

https://orcid.org/0000-0002-9647-5275 (Bing Wang)

关键词:

生物标志物, 候选基因, 退行性脊髓型颈椎病, 基因表达分析, 免疫细胞类型, 神经功能障碍, 外周血, RNA图谱, 脊髓损伤

Abstract: Degenerative cervical myelopathy is a common cause of spinal cord injury, with longer symptom duration and higher myelopathy severity indicating a worse prognosis. While numerous studies have investigated serological biomarkers for acute spinal cord injury, few studies have explored such biomarkers for diagnosing degenerative cervical myelopathy. This study involved 30 patients with degenerative cervical myelopathy (51.3 ± 7.3 years old, 12 women and 18 men), seven healthy controls (25.7 ± 1.7 years old, one woman and six men), and nine patients with cervical spondylotic radiculopathy (51.9 ± 8.6 years old, three women and six men). Analysis of blood samples from the three groups showed clear differences in transcriptomic characteristics. Enrichment analysis identified 128 differentially expressed genes that were enriched in patients with neurological disabilities. Using least absolute shrinkage and selection operator analysis, we constructed a five-gene model (TBCD, TPM2, PNKD, EIF4G2, and AP5Z1) to diagnose degenerative cervical myelopathy with an accuracy of 93.5%. One-gene models (TCAP and SDHA) identified mild and severe degenerative cervical myelopathy with accuracies of 83.3% and 76.7%, respectively. Signatures of two immune cell types (memory B cells and memory-activated CD4+ T cells) predicted levels of lesions in degenerative cervical myelopathy with 80% accuracy. Our results suggest that peripheral blood RNA biomarkers could be used to predict lesion severity in degenerative cervical myelopathy.

Key words: biomarkers, candidate genes, degenerative cervical myelopathy, gene expression analysis, immune cell types, neurological ,
disabilities,
peripheral blood, RNA profiles, spinal cord injury