Identification of Psoriatic Arthritis-Related Pathways Using Multi-Omics Data Integration
Bibliographic record
Abstract
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
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.040 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".