Novel Blood-Based RNA Profiles can Predict Human Degenerative Cervical Myelopathy

Abstract

Abstract Background Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord injury and factors leading to worse prognosis are a longer symptoms duration and a higher myelopathy severity. However, no studies have identified serological biomarkers for the early diagnosis of patients with DCM.Methods A total of 46 participants were enrolled in the study, and peripheral blood was collected for subsequent analysis. Further, differentially expressed genes (DEGs) in DCM, healthy controls (HCs) and patients with cervical spondylotic radiculopathy (CSR, as DCM mimics) were identified. Gene Ontology (GO) enrichment analysis was performed on DEGs. DEGs enriched in neurological disabilities by DisGeNET ontology category were used to diagnosis DCM and predict severity. Proportions of immune cell types were selected to predict injury levels in DCM.Results The results showed that DEGs enriched terms were mainly related to neurological disabilities with 128 genes included, such as muscle weakness, dystonia, myopathy, skeletal muscle atrophy, and peripheral nervous system diseases. LASSO analysis was used for candidate genes selection to construct a multinomial logistic regression model based on the 128 DEGs. A five-gene model was constructed to diagnose DCM from CSR and HC with an accuracy of 93.5%. The model had good specificity and sensitivity with the area under the ROC curve (AUC) value of 0.939. As for DCM severity, one gene model was constructed to distinguish mild DCM and severe DCM with 83.3% accuracy (AUC: 0.769) and 76.7% accuracy (AUC: 0.770), respectively. Using the same method of model building, signatures of two immune cell types distinguished single-level and multi-level injury with 80% accuracy (AUC: 0.895). Our results suggest that mRNAs extracted from peripheral blood could serve as biomarkers for the diagnosis of DCM and can predict severity and injury levels in DCM.Conclusion Blood RNA biomarkers could diagnose DCM and predict the severity of DCM as well as the level of injury in DCM. Our results may provide new insights into the pathogenesis of DCM and aid in designing treatment.