Differential Proteomic Profiling of Cerebrospinal Fluid Shows Potential for the Diagnosis of Degenerative Cervical Myelopathy
Jianhua Zheng, Jinchao Wang, Xiaopeng Zhang, Liguo Liu, Qinpeng Song, Penglei Cui, Xiao HanABSTRACT
Degenerative cervical myelopathy (DCM) is the main neurodegenerative cause of spinal cord dysfunction and impairment. Due to its atypical clinical symptoms, the lack of reliable biomarkers to sensitively evaluate the degree of neurodegeneration has hindered the diagnosis and treatment. Label‐free quantitative proteomics was applied to analyze the cerebrospinal fluid (CSF) samples from DCM and non‐DCM control (Control) subjects, and another cohort was collected for parallel reaction monitoring (PRM) validation analysis. Overall, 1502 proteins were identified, and 120 proteins were differentially expressed. Furthermore, eight promising CSF biomarkers linked to a wide range of pathophysiologies in DCM were successfully validated by using PRM in an independent cohort, and the diagnostic values of those were assessed by receiver operating characteristic (ROC) analyses. Among them, six proteins demonstrated the ability to discriminate DCM patients from non‐DCM control (AUC>0.85). Moreover, the combination of CADM1 and MANBA achieved the highest diagnostic accuracy with an AUC of 0.959. This study presents the first quantitative proteomic analysis of human CSF from DCM. The results may help understand the pathophysiology of DCM and provide insight into identifying objective criteria for the diagnosis of DCM in clinical applications.
Summary
This study presents the first quantitative proteomic analysis of human cerebrospinal fluid (CSF) from degenerative cervical myelopathy (DCM). Label‐free quantitative proteomics was applied to analyze the CSF samples from DCM and Control subjects, and the identified and differentially expressed proteins underwent extensive bioinformatics analyses. Eight promising CSF biomarkers linked to a wide range of pathophysiologies in DCM were successfully validated by using PRM in an independent cohort, and the diagnostic values of those were assessed by ROC analyses; the combination of CADM1 and MANBA achieved the highest diagnostic accuracy with an AUC of 0.959. The results may help understand the pathophysiology of DCM and provide an insight into identifying objective criteria for the diagnosis of DCM in clinical applications.