Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis

Journal of Clinical Bioinformatics

Thomas M Aune⁸  Nancy J Olsen⁵  Cor L Verweij³  Saskia Vosslamber³  Davit Mrelashvili¹  Subramaniam Sriram⁶  Melodie A Henderson⁷  Philip S Crooke²  John T Tossberg⁴ 

[1] Department of Neurology, University of South Carolina, Columbia, SC, USA;Department of Mathematics, Vanderbilt University, Nashville, TN, USA;Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands;Research Department, ArthroChip, LLC, Franklin, TN, USA;Department of Medicine, Penn State Hershey Medical Center, Hershey, PA, USA;Department of Neurology, Vanderbilt University School of Medicine, Nashville, TN, USA;Department of Medicine, Vanderbilt University School of Medicine, MCN T3219, 1161 21st Avenue South, Nashville, TN, 37232-2681, USA;Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA

关键词: Diagnosis;    Disease prediction;    Multiple sclerosis;    Genomics;   

Others  :  801535
DOI  :  10.1186/2043-9113-3-18

 received in 2013-08-21, accepted in 2013-09-30,  发布年份 2013

【 摘 要 】

Background

Detection of brain lesions disseminated in space and time by magnetic resonance imaging remains a cornerstone for the diagnosis of clinically definite multiple sclerosis. We have sought to determine if gene expression biomarkers could contribute to the clinical diagnosis of multiple sclerosis.

Methods

We employed expression levels of 30 genes in blood from 199 subjects with multiple sclerosis, 203 subjects with other neurologic disorders, and 114 healthy control subjects to train ratioscore and support vector machine algorithms. Blood samples were obtained from 46 subjects coincident with clinically isolated syndrome who progressed to clinically definite multiple sclerosis determined by conventional methods. Gene expression levels from these subjects were inputted into ratioscore and support vector machine algorithms to determine if these methods also predicted that these subjects would develop multiple sclerosis. Standard calculations of sensitivity and specificity were employed to determine accuracy of these predictions.

Results

Our results demonstrate that ratioscore and support vector machine methods employing input gene transcript levels in blood can accurately identify subjects with clinically isolated syndrome that will progress to multiple sclerosis.

Conclusions

We conclude these approaches may be useful to predict progression from clinically isolated syndrome to multiple sclerosis.

【 授权许可】

   
2013 Tossberg et al.; licensee BioMed Central Ltd.

【 图 表 】

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