Background: Multiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF).
Objective: Serum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers.
Methods: Cytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS.
Results: Twenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five of these biomarkers were used. Interestingly, the highest accuracy (99%) of MS diagnosis was demonstrated when CCL27, IFN-γ, and IL-4 were part of the five selected cytokines, suggesting their important role in MS pathogenesis. Also, these binary classifier models had the accuracy in the range of 70-78% (serum) and 60-69% (CSF) to discriminate between the progressive (primary and secondary progressive) and relapsing-remitting forms of MS.
Conclusion: We identified the set of cytokines from the serum and CSF that could be used for the MS diagnosis and classification.
Copyright © 2020 Ekaterina Martynova et al.