Dataset Information

Heterogeneity of synovial molecular patterns in patients with arthritis.

ABSTRACT:

Objectives

Early 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.

Methods

Low-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).

Results

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. 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.

Conclusions

Gene 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.

SUBMITTER: Lauwerys BR

PROVIDER: S-EPMC4415786 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Heterogeneity of synovial molecular patterns in patients with arthritis.

Lauwerys Bernard R BR Hernández-Lobato Daniel D Gramme Pierre P Ducreux Julie J Dessy Adrien A Focant Isabelle I Ambroise Jérôme J Bearzatto Bertrand B Nzeusseu Toukap Adrien A Van den Eynde Benoît J BJ Elewaut Dirk D Gala Jean-Luc JL Durez Patrick P Houssiau Frédéric A FA Helleputte Thibault T Dupont Pierre P

PloS one 20150430 4

<h4>Objectives</h4>Early 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 plat ...[more]

PMID: 25927832

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Project description:Rheumatoid arthritis (RA) is an inflammatory joint disorder that results in progressive joint damage when insufficiently treated. In order to prevent joint destruction and functional disability in RA, early diagnosis and initiation of appropriate treatment with Disease-Modifying Antirheumatic Drugs (DMARDs) is needed. However, in daily clinical practice, patients may initially display symptoms of arthritis that do not fulfil the classification criteria for a definite diagnosis of RA, or any other joint disease, a situation called “Undifferentiated Arthritis” (UA). Out of the patients with UA, 30 to 50% usually develop RA, and early identification of these remains a challenge. At the present time, although several risk factors associated with the development of RA have been identified (6-9), a model that reliably predicts the probability of evolution of UA into RA in individual patients is lacking. In order to better identify early RA patients, an American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) collaboration recently developed new classification criteria. Although these criteria are more sensitive, the risk of over-diagnosis is an important issue to consider, especially in very early disease. 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 patients with arthritis according to the underlying condition. In a previous study, we performed global analyses of gene expression in synovial biopsies from patients with RA, Systemic Lupus Erythematosus (SLE) and Osteoarthritis (OA), using high-density oligonucleotide spotted microarrays. We found that the gene expression profiles are strikingly different according to the underlying condition. Thus, the majority of the genes up-regulated in SLE are type I Interferon-inducible genes, as compared with the up-regulation genes involved in T cell and B cell activation in RA, and in extracellular matrix homeostasis in OA. Based on these results, similar analyses were performed in synovial biopsies from patients with seronegative arthritis (SA) and microcrystalline arthritis (MIC), in order to identify disease-specific molecular signatures.

Dataset Information

Heterogeneity of synovial molecular patterns in patients with arthritis.

Objectives

Methods

Results

Conclusions

Publications

Heterogeneity of synovial molecular patterns in patients with arthritis.

Similar Datasets

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