MS-Related Disorders ID'd by Proteomic Pattern Analysis

CSF proteomic pattern analysis discriminates among multiple sclerosis-related disorders

MONDAY, Sept. 19 (HealthDay News) -- Proteomic pattern analysis of cerebrospinal fluid (CSF) analysis using matrix-assisted laser desorption/ionization time-of-flight (MALDI) mass spectrometry distinguishes between similar multiple sclerosis (MS)-related disorders, according to a study published online Sept. 12 in the Annals of Neurology.

Mika Komori, M.D., from the Kyoto University in Japan, and colleagues analyzed CSF proteomic patterns from 107 patients with MS-related disorders, including relapsing remitting-MS (RRMS), primary progressive-MS (PPMS), anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder (SP-NMOSD), seronegative-NMOSD with long cord lesions of spinal magnetic resonance imaging (SN-NMOSD), amyotrophic lateral sclerosis, and non-MS inflammatory neurological control diseases, using a new, unbiased biomarker discovery strategy. Magnetic bead-based enrichment of CSF peptides and proteins was followed by MALDI mass spectrometry. Multivariate statistics and pattern-matching algorithms were used to analyze the obtained spectra. These analyses were duplicated in an independent sample of 84 patients with MS-related disorders or with other neurological diseases.

The investigators found that MS-related disorders differed considerably with respect to CSF protein profiles. The support vector machine classifier distinguished SP-NMOSD and SN-NMOSD from RRMS with high cross-validation accuracy, especially in relapse phases. Some peaks derived from SP-NMOSD were able to discriminate RRMS with high area under the curve scores, and these results were also replicated in the second cohort. Pattern matching analysis revealed some similarity between proteomic patterns in selected neurological diseases. RRMS and PPMS had larger spectral differences than PPMS and amyotrophic lateral sclerosis.

"Our applied proteomic pattern analysis facilitated the effective distinction of similar MS-related disorders, and revealed a possibility that these patterns, themselves, can be used as biomarkers for each disorder," the authors write.

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