Long noncoding RNAs associated with phenotypic severity in multiple sclerosis

Abstract

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.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

“… Gronsbell et al [ 62 ] EMR SSL; Accuracy in [92.9%, 93.9%]. Gupta et al [ 63 ] Microbiomics RF; Specificity = 86.4%; Sensitivity = 45.4%. Lim et al [ 64 ] Kyneurenine DT; DA; CART; SVM; Accuracy in [83.0%, 91.0%].…”

“…ML applications to recognize MS sub-types or clinical-courses—such as RRMS, Primary-Progressive MS (PPMS), and SPMS, each of which might be mild, moderate, or severe—emphasized the role of medical records and omics data in the biomarker search (Table 2 and Additional file 3 ). MS sub-typing was addressed in seven studies (10.6%) by analyzing clinical [ 60 – 62 ] and biological [ 44 , 63 – 65 ] data. However, the accuracy of over 90.0% was reported only when using data from medical records [ 62 ] or omics 7 [ 64 ].…”

Expression of risk genes linked to vitamin D receptor super-enhancer regions and their association with phenotype severity in multiple sclerosis

“…Selection criteria and experimental methodology have been described previously ( 28 ). Briefly, study participants ( n = 64) representing a range of phenotypic severity were selected from a large prospective cohort consisting of 2,831 participants recruited for an ongoing prospective cohort study at the University of Calgary MS Clinic in Calgary, Alberta.…”

Long Noncoding RNAs and Circular RNAs in Autoimmune Diseases

“…Together, these deregulated lncRNAs may provide valuable tools for understanding the relationships between lncRNAs and MS progression as well as new pathological pathways [ 50 ]. Little is known about the precise roles of these lncRNAs but based on the expression profiles, lncRNAs appear to be important in the regulation of immune functions and are of potential biological significance for MS pathogenesis [ 122 ]. Although these findings are still preliminary, they are sufficient to identify an hypothesis for future investigation and give guidance regarding the design of future studies to analyze the role of lncRNA in MS onset, progression, and therapy.…”