Identifying Differentially Expressed MicroRNAs for Treatment Response to TNF Inhibitors and Il-17 Inhibitors in Psoriatic Arthritis
Bibliographic record
Abstract
Objectives Micro-RNAs (miRNAs) are stable, specific and can make good candidates for biomarker research. We aimed to (i) Identify differentially expressed miRNAs in serum samples of Psoriatic Arthritis (PsA) patients that can predict response to Tumor necrosis factor inhibitor (TNFi) or Interleukin-17 inhibitor (IL-17i) (ii) Identify biologic pathways enriched by the identified miRNAs. Methods From our prospective PsA database, patients satisfying CASPAR criteria and initiating biologic disease-modifying anti-rheumatic drugs (bDMARDs); TNFi or IL-17i, were identified. Biobanked serum samples before initiation of treatment and after 6 months were retrieved. miRNA expression in serum samples was measured with next-generation sequencing. Articular response was defined as achieving a low disease activity or remission according to DAPSA (<14) and a cutaneous response as achieving at least a 50% reduction in PASI. An unpaired Student’s t test was used to compare the distributions of quantitative variables between responders and non-responders in the IL-17i and TNFi groups. Furthermore, enrichment of specific biologic pathways corresponding to the identified miRNAs was examined using pathDIP v.5. Analysis was restricted to literature curated pathways and experimentally detected protein-protein interactions with a prediction confidence of 0.99. Results 74 patients have been included so far (Table 1). Articular and cutaneous response to IL-17i was observed in 55.6% and 22.2% of patients, respectively. Likewise, articular and cutaneous response to TNFi was observed in 65.8% and 26.3% of patients, respectively. No miRNAs showed significant differences in expression between responders and non-responders at baseline (p< 0.05). However, miRNA miR-1246 showed the most difference in expression (|logFC|>1) between responders and non-responders (for both articular and cutaneous criteria) in patients treated with IL-17i. In patients treated with TNFi, miR-11400 and miR-1277-3p showed the most difference (|logFC|>1) between cutaneous responders and non-responders, while miR-11400 also showed the most difference (|logFC|>1) between articular responders and non-responders. When patients were stratified by changes in swollen joint count, miR-1246 at baseline was significantly lower (log FC = −9.89, p = 0.02) in IL-17i treated patients showing any reduction in swollen joints. The most commonly targeted pathways by miR-1246 were related to bone formation and regeneration, cell proliferation and apoptosis, including the non-canonical Wnt, PI3K-AKT-mTOR and Rho GTPases signaling pathways. Table 1. Patient demographics at baseline Conclusion Deregulation of miRNAs was observed between responders and non-responders of biologic treated patients. Further analysis is required to better understand the role of these miRNAs in PsA inflammatory mechanisms which can help with selection of effective treatments and provide better disease outcomes for patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".