Long noncoding RNAs associated with phenotypic severity in multiple sclerosis.

Introduction: Multiple sclerosis (MS) is a disease that causes progressive neurological disability. Treatments are available that are protective against MS relapses and it is thought that reduction of early neuroinflammation may improve long term prognosis. At present there is no biomarker that can predict which patients may have a more severe disease course, and potentially benefit from more aggressive therapy. Long noncoding RNAs (lncRNAs) are emerging as potential disease biomarkers that could be of interest in prognostication of MS.

Methods: We identified a discovery cohort of 20 patients, ten of which had a mild MS phenotype and ten with severe MS phenotype according to the Age-Related MS Severity Scale (ARMSS). RNAseq was performed on RNA extracted from whole blood and bioinformatic analysis restricted to lncRNAs. Our goal was to select the most significant lncRNAs and quantify these using custom digital droplet RT-qPCR assays in a validation cohort of 44 participants (with mild or severe MS).

Results: Eight lncRNA candidates were identified from the discovery cohort. Of these, four lncRNAs remained significantly differentially expressed in the validation cohort (ENSG00000260302, ENSG00000270972, ENSG00000272512 and ENSG00000223387). Little is known about the precise roles of these lncRNAs but based on expression data they appear to be important to immune function and are of potential biological significance to MS pathogenesis.

Conclusions: This study is the first to investigate possible lncRNA biomarkers to differentiate phenotypic severity in MS. Although the findings are preliminary based on our small sample size, they are sufficient to identify hypotheses for future investigation, and give guidance regarding the design of future studies.