Conference Coverage

Can Gene Expression–Based Technologies Help Diagnose MS?

Long, noncoding RNA expression levels may be an early biomarker for MS.


ORLANDO—RNA expression patterns are highly dynamic, even at the earliest stages of multiple sclerosis (MS) pathogenesis, according to research presented at the ACTRIMS 2017 Forum. Machine learning methods trained and validated with these gene targets are a highly accurate tool that could provide actionable data for health care providers who suspect MS.

Charles F. Spurlock III, PhD

“Using RNA sequencing, we have identified messenger RNAs (mRNAs), which are classic protein-coding genes, and long, noncoding RNAs (lncRNAs), which are a newly discovered class of RNA molecule that plays important roles in regulating gene expression,” said Charles F. Spurlock III, PhD, and colleagues. Dr. Spurlock is an Instructor in Medicine in the Rheumatology Division of Vanderbilt University and Chief Executive Officer at IQuity, both in Nashville. He and his research colleagues identified RNA patterns that differ between patients with MS, patients with other neurologic diseases, and healthy controls. The goal of their research was to explore whether lncRNA expression levels could serve as biomarkers for MS and provide clinically useful information to health care providers. Peripheral whole blood was collected into PAXgene tubes from 161 healthy control subjects, 185 unaffected first-degree relatives of patients with MS, 84 subjects with a clinically isolated syndrome who later progressed to clinically definite MS, 90 subjects diagnosed with MS before initiation of treatment, 212 patients diagnosed with MS after initiation of treatment, 132 patients with other inflammatory neurologic disorders, and 115 patients with noninflammatory neurologic disorders.

RNA sequencing was performed to identify differentially expressed protein-coding and noncoding RNA genes at distinct stages of MS, compared with controls. Expression levels of these genes were validated by real-time polymerase chain reaction (PCR) across all 979 subjects recruited. Ratios of gene expression data were used as inputs to train machine learning classifiers capable of multicategory comparisons. An independent validation testing set consisting of individuals across each control and disease class was used to evaluate the performance of these classifiers. In contrast to protein-coding gene targets where gene expression differences were often twofold or less, lncRNAs were highly differentially expressed across each of the experimental groups compared. Training and validation of machine learning methods revealed that lncRNA gene expression inputs resulted in higher overall accuracy and confidence of machine learning predictions than did protein-coding genes, resulting in greater than 90% accuracy in the validation testing set.

Glenn S. Williams

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