Identification of <scp>miR</scp>‐190a‐5p and <scp>miR</scp>‐26b‐5p as Potential <scp>microRNA</scp> Biomarkers for Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) is a chronic immune-mediated inflammatory arthritis that develops in 30% of patients with psoriasis, leading to increased morbidity and mortality and reduced quality of life. MicroRNAs (miRNAs) modulate gene expression and have been associated with the pathogenesis of immune-mediated disorders. We aimed to identify miRNAs that can be used as biomarkers for the development of PsA in patients with psoriasis. METHODS: miRNA expression levels were assessed in serum samples from 28 patients with PsA, 35 patients with cutaneous psoriasis without arthritis (PsC), and 28 healthy controls through next-generation sequencing. Differential expression was assessed by linear modeling with empirical Bayes moderation corrected for sequencing batch, age, sex, and duration of psoriasis. For validation, we measured the expression of >191 genes predicted to be targeted by the dysregulated miRNAs using a custom NanoString probe panel in an independent cohort of 144 patients with PsA and 88 patients with PsC. The enrichment of specific pathways corresponding to the differentially expressed gene targets was examined using pathDIP. RESULTS: In the discovery cohort, the miRNA miR-190a-5p was significantly down-regulated in patients with PsA compared to those with PsC (P< 0.05), and both miR-190a-5p and miR-26b-5p were down-regulated in patients with PsA versus healthy controls (P < 0.05). In the validation cohort, 26 gene targets of both of these miRNAs were differentially expressed. These genes were enriched in signaling pathways associated with bone formation and regeneration: Wnt and transforming growth factor β. CONCLUSION: Serum expression levels of miR-190a-5p and miR-26b-5p can potentially serve as biomarkers for PsA development.
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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 | 0.001 |
| 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".