Development and Validation of a MicroRNA Panel to Differentiate Between Patients with Rheumatoid Arthritis or Systemic Lupus Erythematosus and Controls
Bibliographic record
Abstract
OBJECTIVE: MicroRNA (miRNA) are short noncoding RNA that regulate genes and are both biomarkers and mediators of disease. We used small RNA (sRNA) sequencing and machine learning methodology to develop an miRNA panel to reliably differentiate between rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE) and control subjects. METHODS: Plasma samples from 167 RA and 91 control subjects who frequency-matched for age, race, and sex were used for sRNA sequencing. TIGER was used to analyze miRNA. DESeq2 and random forest analyses were used to identify a prioritized list of miRNA differentially expressed in patients with RA. Prioritized miRNA were validated by quantitative PCR, and lasso and logistic regression were used to select the final panel of 6 miRNA that best differentiated RA from controls. The panel was validated in a separate cohort of 12 SLE, 32 RA, and 32 control subjects. Panel efficacy was assessed by area under the receiver operative characteristic curve (AUC) analyses. RESULTS: The final panel included miR-22-3p, miR-24-3p, miR-96-5p, miR-134-5p, miR-140-3p, and miR-627-5p. The panel differentiated RA from control subjects in discovery (AUC = 0.81) and validation cohorts (AUC = 0.71), seronegative RA (AUC = 0.84), RA remission (AUC = 0.85), and patients with SLE (AUC = 0.80) versus controls. Pathway analysis showed upstream regulators and targets of panel miRNA are associated with pathways implicated in RA pathogenesis. CONCLUSION: An miRNA panel identified by a bioinformatic approach differentiated between RA or SLE patients and control subjects. The panel may represent an autoimmunity signature, perhaps related to inflammatory arthritis, which is not dependent on active disease or seropositivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".