Identification of Psoriatic Arthritis-Related Pathways Using Multi-Omics Data Integration
Notice bibliographique
Résumé
Objectives The objectives of this study include (i) identifying and curating publicly available omics studies on psoriatic disease (PsD) to build a multiomics data integration portal (PsDIP) and (ii) integrating studies from PsDIP comparing the serum omics profiles of psoriatic arthritis (PsA) and cutaneous psoriasis (PsC) patients to identify PsA-related pathways. Methods A scoping review was conducted to curate all publicly available omics studies in the field of PsD from 3 databases: Ovid MEDLINE, Embase and Cochrane Central. Inclusion criteria comprise all English-language studies related to psoriasis, PsA and PsC investigating markers/molecular signatures in human subjects using non-targeted high throughput experiments. Lists of differentially expressed markers, study and clinical information were extracted from all papers that passed the eligibility criteria, to develop a multi-omics data integration portal for PsD (PsDIP). To demonstrate the usability of this portal in identifying novel PsA-related pathways, we conducted a preliminary integrative analysis. Lists of differentially expressed proteins, microRNAs (miRNAs) and metabolites from 3 independent single omics studies comparing serum samples of PsA and PsC patients were collected from PsDIP. All differentially expressed markers were integrated using the following bioinformatics tools: mirDIP (miRNA Data Integration Portal) v5.2, IID (Integrated Interactions Database) ver. 2021-05, STITCH (Search Tool for Interacting Chemicals) v5, pathDIP (Pathway Data Integration Portal) v5, and NAViGaTOR (Network Analysis, Visualization, & Graphing TORonto) v3. Single omics markers (proteins, metabolites and miRNAs) were connected via a network of biological interactions and overlapping pathways. Results 5 miRNAs, 34 proteins and 19 metabolites differentially expressed between PsA and PsC were derived from the 3 independent studies selected. 71 target genes of the 5 miRNAs were found to be connected with 10 proteins and 19 gene interactors of 3 metabolites via protein-protein interactions and 71 statistically significant pathways (q<0.05) were found to be common among them. 39 are found to be relevant to PsA from literature. 25 of the 39 pathways are potentially important pathways for PsA not identified by the PsA single omics studies in PsDIP such as RANKL, oncostatin M, HIF-1, PDGFR-beta, M-CSF, IL-7, and IL-18 signaling pathways (Figure 1). Figure 1. Preliminary integrative analysis of 3 independent serum studies comparing PsA and PsC identified candidate pathways for PsA. This analysis identified a biological subnetwork of differentially expressed markers from the 3 studies (5miRNAs, 10 proteins, 3 metabolites) connected by protein-protein interactions and overlapping pathways. Pathways (light blue nodes) outlined in dark blue are found to be relevant to PsA from literature and are shown in the table. Pathways in bold text are potentially important pathways for PsA not identified by PsA single omics in PsDIP. Pathways are ranked by the total number of genes in each pathway with the number provided in brackets. This biological subnetwork was visualized using NAViGaTOR3. Conclusion Multi-omics integration of independent single omics serum datasets identified key pathways related to osteoclastogenesis, angiogenesis and inflammation which are important to PsA pathophysiology. Genes and proteins associated with these pathways are candidate differentially expressed molecules between PsA and PsC. Further analyses and validation are ongoing. Best Abstract on Basic Science Research by a Trainee Award
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,015 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,040 | 0,029 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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 ».