Transcriptome analysis from muscle biopsy tissues in late-onset myopathies identifies potential biomarkers correlating to muscle pathology
Bibliographic record
Abstract
The diagnosis of adult-onset genetic muscle diseases is challenging because of the diversity of clinical phenotypes, findings on muscle biopsy that may be nonspecific, and the large number of genetic causes. Even with thorough investigation, the diagnostic yield for genetic testing in these populations is very low, and the distinction from acquired conditions such as sporadic inclusion body myositis [sIBM] can also prove difficult. In this study, we analysed whole transcriptome data generated from RNA isolated from muscle biopsy tissues, from a cohort of 16 participants with sIBM and other histologic diagnoses. Our objective was to identify candidate RNA biomarkers that could be an adjunctive tool in differentiating these conditions. Principal component analysis was able to delineate the groups based on their histologic diagnoses. Gene ontology and pathway analyses demonstrated dysregulation of immune pathways in sIBM. In mitochondrial myopathy we observed upregulation of FGF21, GDF15, ASNS and TRIB3, which are known candidate biomarkers for mitochondrial myopathy. Novel findings included the identification of transcripts of unknown function that were dysregulated in myofibrillar myopathy [JPX], dystrophic changes [MEG3], and mitochondrial myopathy [GAS5]. We suggest future investigations with larger cohorts of participants to confirm the findings of this study, with further directed experiments to determine the role of novel transcripts in disease pathogenesis.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".