Subtype‐specific peripheral blood gene expression profiles in recent‐onset juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify differences in peripheral blood gene expression between patients with different subclasses of juvenile idiopathic arthritis (JIA) and healthy controls in a multicenter study of patients with recent-onset JIA prior to treatment with disease-modifying antirheumatic drugs (DMARDs) or biologic agents. METHODS: Peripheral blood mononuclear cells (PBMCs) from 59 healthy children and 136 patients with JIA (28 with enthesitis-related arthritis [ERA], 42 with persistent oligoarthritis, 45 with rheumatoid factor [RF]-negative polyarthritis, and 21 with systemic disease) were isolated from whole blood. Poly(A) RNA was labeled using a commercial RNA amplification and labeling system (NuGEN Ovation), and gene expression profiles were obtained using commercial expression microarrays (Affymetrix HG-U133 Plus 2.0). RESULTS: A total of 9,501 differentially expressed probe sets were identified among the JIA subtypes and controls (by analysis of variance; false discovery rate 5%). Specifically, 193, 1,036, 873, and 7,595 probe sets were different in PBMCs from the controls compared with those from the ERA, persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA patients, respectively. In patients with persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA subtypes, up-regulation of genes associated with interleukin-10 (IL-10) signaling was prominent. A hemoglobin cluster was identified that was underexpressed in ERA patients but overexpressed in systemic JIA patients. The influence of JAK/STAT, ERK/MAPK, IL-2, and B cell receptor signaling pathways was evident in patients with persistent oligoarthritis. In systemic JIA, up-regulation of innate immune pathways, including IL-6, Toll-like receptor/IL-1 receptor, and peroxisome proliferator-activated receptor signaling, were noted, along with down-regulation of gene networks related to natural killer cells and T cells. Complement and coagulation pathways were up-regulated in systemic JIA, with a subset of these genes being differentially expressed in other subtypes as well. CONCLUSION: Expression analysis identified differentially expressed genes in PBMCs obtained early in the disease from patients with different subtypes of JIA and in healthy controls, providing evidence of immunobiologic differences between these forms of childhood arthritis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".