Abstract
ObjectivesEarly diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.MethodsLow-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).ResultsAccording to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.ConclusionsGene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.
Highlights
Rheumatoid arthritis (RA) is an inflammatory joint disorder that results in progressive joint damage when insufficiently treated
According to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation
Gene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions
Summary
Rheumatoid arthritis (RA) is an inflammatory joint disorder that results in progressive joint damage when insufficiently treated. In order to better identify early RA patients, an American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) collaboration recently developed new RA classification criteria [10]. These criteria are more sensitive, the risk of misdiagnosis is an important issue to consider, especially in very early disease [11, 12]. In this context, the present study explores the feasibility of a molecular diagnosis of arthritis, based on the identification of disease-specific transcriptomic profiles in synovial biopsies from UA patients, using low-density cDNA arrays
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