Circulating miRNAs as Biomarkers for Mitochondrial Neuro-Gastrointestinal Encephalomyopathy
Bibliographic record
Abstract
Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is an ultra-rare disease for which there are currently no validated outcome measures for assessing therapeutic intervention efficacy. The aim of this study was to identify a plasma and/or serum microRNA (miRNA) biomarker panel for MNGIE. Sixty-five patients and 65 age and sex matched healthy controls were recruited and assigned to one of four study phases: (i) discovery for sample size determination; (ii) candidate screening; (iii) candidate validation; and (iv) verifying the performance of the validated miRNA panel in four patients treated with erythrocyte-encapsulated thymidine phosphorylase (EE-TP), an enzyme replacement under development for MNGIE. Quantitative PCR (qPCR) was used to profile miRNAs in serum and/or plasma samples collected for the discovery, validation and performance phases, and next generation sequencing (NGS) analysis was applied to serum samples assigned to the candidate screening phase. Forty-one differentially expressed candidate miRNAs were identified in the sera of patients (p < 0.05, log2 fold change > 1). The validation cohort revealed that of those, 27 miRNAs were upregulated in plasma and three miRNAs were upregulated in sera (p < 0.05). Through binary logistic regression analyses, five plasma miRNAs (miR-192-5p, miR-193a-5p, miR-194-5p, miR-215-5p and miR-34a-5p) and three serum miRNAs (miR-192-5p, miR-194-5p and miR-34a-5p) were shown to robustly distinguish MNGIE from healthy controls. Reduced longitudinal miRNA expression of miR-34a-5p was observed in all four patients treated with EE-TP and coincided with biochemical and clinical improvements. We recommend the inclusion of the plasma exploratory miRNA biomarker panel in future clinical trials of investigational therapies for MNGIE; it may have prognostic value for assessing clinical status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".