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

Abstract

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 lesion severity in degenerative cervical myelopathy with 80% accuracy. Our results suggest that peripheral blood RNA biomarkers could be used to predict levels of lesions in degenerative cervical myelopathy.