Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis
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 lev...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Mediators of Inflammation |
| Online Access: | http://dx.doi.org/10.1155/2020/2727042 |
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| author | Ekaterina Martynova Mehendi Goyal Shikhar Johri Vinay Kumar Timur Khaibullin Albert A. Rizvanov Subhash Verma Svetlana F. Khaiboullina Manoj Baranwal |
| author_facet | Ekaterina Martynova Mehendi Goyal Shikhar Johri Vinay Kumar Timur Khaibullin Albert A. Rizvanov Subhash Verma Svetlana F. Khaiboullina Manoj Baranwal |
| author_sort | Ekaterina Martynova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cbdc3b2057b04adeb8c3102749cc9d56 |
| institution | Kabale University |
| issn | 0962-9351 1466-1861 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Mediators of Inflammation |
| spelling | doaj-art-cbdc3b2057b04adeb8c3102749cc9d562025-02-03T06:05:43ZengWileyMediators of Inflammation0962-93511466-18612020-01-01202010.1155/2020/27270422727042Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple SclerosisEkaterina Martynova0Mehendi Goyal1Shikhar Johri2Vinay Kumar3Timur Khaibullin4Albert A. Rizvanov5Subhash Verma6Svetlana F. Khaiboullina7Manoj Baranwal8Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, RussiaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, RussiaAmity School of Engineering and Technology, Amity University, Jaipur, Rajasthan, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, IndiaRepublican Clinical Neurological Canter, Republic of Tatarstan, RussiaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, RussiaDepartment of Microbiology and Immunology, University of Nevada, Reno, NV 89557, USADepartment of Microbiology and Immunology, University of Nevada, Reno, NV 89557, USADepartment of Biotechnology, Thapar Institute of Engineering and Technology, Patiala 147004, IndiaBackground. 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.http://dx.doi.org/10.1155/2020/2727042 |
| spellingShingle | Ekaterina Martynova Mehendi Goyal Shikhar Johri Vinay Kumar Timur Khaibullin Albert A. Rizvanov Subhash Verma Svetlana F. Khaiboullina Manoj Baranwal Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis Mediators of Inflammation |
| title | Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis |
| title_full | Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis |
| title_fullStr | Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis |
| title_full_unstemmed | Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis |
| title_short | Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis |
| title_sort | serum and cerebrospinal fluid cytokine biomarkers for diagnosis of multiple sclerosis |
| url | http://dx.doi.org/10.1155/2020/2727042 |
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