Identifying Differentially Expressed MicroRNAs for Treatment Response to TNF Inhibitors and Il-17 Inhibitors in Psoriatic Arthritis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».