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Validating Differentially Expressed Micro-RNAs That Distinguish Psoriatic Arthritis from Osteoarthritis

2025· article· en· W4411846638 on OpenAlexaffvenue
Samantha Bestavros, Darshini Ganatra, Anas Samman, David Nasri, Omar Correa, Rajiv Gandhi, Mohit Kapoor, Vinod Chandran

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsKrembil Foundation
Fundersnot available
KeywordsMedicinePsoriatic arthritisOsteoarthritisPsoriasisArthritisDermatologyComputational biologyPathologyImmunologyBiology

Abstract

fetched live from OpenAlex

Objectives Biomarkers may help differentiate psoriatic arthritis (PsA) from osteoarthritis (OA). MicroRNAs have potential to be robust biomarkers, we aimed to 1) Use miRNA sequencing to analyze miRNA expression profiles in the synovial fluid of patients with PsA compared to those with OA, 2) Validate differential expression of select miRNAs in an independent cohort of PsA and OA patients through quantitative real-time PCR (qRT-PCR), and 3) Investigate the potential for differentially expressed miRNAs between PsA and OA to serve as biomarkers for PsA to enhance PsA diagnostic accuracy. Methods We performed miRNA sequencing on synovial fluid (SF) aspirated from knees of 12 PsA and 12 OA patients using Illumina NextSeq 550. We used linear modeling with empirical Bayes moderation (using the Limma R package) for assessing differential expressions of miRNAs. For validation, we extracted miRNA from SF samples from an independent set of 35 PsA and 37 OA patients. We investigated the differential expression of miRNAs between the disease states of 12 selected miRNAs using qRT-PCR (99a-5p, 100-5p, 10b-5p, 7c-5p, 125b-5p, 125a-5p, 27b-3p, 26b-5p, 142-3p, 142-5p, 223-3p, and 150-5p). We calculated the differential expression of miRNAs between PsA and OA using fold change, and calculated area under the receiver operating characteristic curve (AUC) values for the select miRNAs. Results After miRNA sequencing, 17 miRNAs were found to be significantly differentially expressed between PsA and OA (FDR adjusted p-value less than 0.05) (Figure 1). From the 12 selected miRNAs analyzed further by qRT-PCR in an independent set of patients, statistically significant differences were identified in the expression of 11 out of 12 miRNAs between PsA and OA. PsA patients had lower expression of 99a-5p (FC=0.162, p<0.0001), 100-5p (FC=0.091, p<0.0001), 125b-5p (FC=0.087, p<0.0001), 27b-3p (FC=0.191, p<0.0001), 7c-5p (FC=0.330, p<0.001), 10b-5p (FC=0.355, p<0.01), and 125a-5p (FC=0.500, p<0.05) compared to OA patients. PsA patients had higher expressions of 150-5p (FC=9.183, p<0.0001), 223-3p (FC=7.112, p<0.0001), 142-3p (FC=2.432, p<0.01), and 142-5p (FC=2.627, p<0.01) compared to OA patients. ROC analyses revealed that 4 miRNAs (miR-223-3p, miR-99a-5p, miR-125b-5p, miR-150-5p) had an area under the curve value greater than 0.8. Fig. 1. Volcano plot of log2 fold change (FC) against −log10 FDR adjusted p-value of miRNAs identified through miRNA sequencing. Red dots indicate differentially expressed miRNAs (FDR adjusted p value <0.05 and a log2 FC>±l). miRNAs with FC≥±1 but FDR corrected p-value of ≥0.05 are shown in green. Black dots represent non-significant miRNAs with a log2 FC<±1. Conclusion The validated miRNAs, shown to be differentially expressed in PsA and OA patient synovial fluid across 2 independent cohorts, may serve as potential biomarkers for PsA, and improve our understanding of PsA’s inflammatory mechanisms.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.014
GPT teacher head0.268
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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