Long noncoding RNAs associated with phenotypic severity in multiple sclerosis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".