Gene Expression Profiling in Rheumatology

Kraan, Tineke C. T. M. van der Pouw; Baarsen, Lisa G. M. van; Rustenburg, François; Baltus, Belinda; Fero, Mike; Verweij, Cornelis L.

doi:10.1007/978-1-59745-402-5_22

Cited by 17 publications

(16 citation statements)

References 15 publications

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“…Sample preparation and microarray hybridization were performed as described previously. 29,30 Microarray data analysis Data storage and filtering was performed using the Stanford Microarray Database 31 (http://genome-www5. stanford.edu//) as described previously.…”

Section: Pharmacogenomics Of Infliximab Treatmentmentioning

confidence: 99%

Pharmacogenomics of infliximab treatment using peripheral blood cells of patients with rheumatoid arthritis

et al. 2010

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Section: Pharmacogenomics Of Infliximab Treatmentmentioning

confidence: 99%

Pharmacogenomics of infliximab treatment using peripheral blood cells of patients with rheumatoid arthritis

et al. 2010

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“…45 Total RNA was linear amplified using the Message Amp. aRNA kit (Ambion, Huntingdon, UK), which is based on the Eberwine method.…”

Section: Patientsmentioning

confidence: 99%

A subtype of multiple sclerosis defined by an activated immune defense program

et al. 2006

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Given the heterogeneous nature of multiple sclerosis (MS), we applied DNA microarray technology to determine whether variability is reflected in peripheral blood (PB) cells. In this study, we studied whole-blood gene expression profiles of 29 patients with relapsing-remitting MS (RRMS) and 25 age-and sex-matched healthy controls. We used microarrays with a complexity of 43K cDNAs. The data were analyzed using sophisticated pathway-level analysis in order to provide insight into the deregulated peripheral immune response programs in MS. We found a remarkable elevated expression of a spectrum of genes known to be involved in immune defense in the PB of MS patients compared to healthy individuals. Cluster analysis revealed that the increased expression of these genes was characteristic for approximately half of the patients. In addition, the gene signature in this group of patients was comparable with a virus response program. We conclude that the transcriptional signature of the PB cells reflects the heterogeneity of MS and defines a sub-population of RRMS patients, who exhibit an activated immune defense program that resembles a virus response program, which is supportive for a link between viruses and MS.

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“…These attempts have partially focused on the expression of selected candidate molecules with a known influence on the respective diseases; for example, type I interferon family members [22,23], tumor necrosis factor superfamily and bone morphogenetic protein family members [24], citrullinated synovial proteins [25], and proteases such as metalloproteinases or cathepsins [26]. Although these studies have indicated the existence of individual or combined biomarkers for RA, the validity of this approach has not been universal.…”

Section: Introductionmentioning

confidence: 99%

“…This has been applied to discriminate early versus late RA [29] and to discriminate RA versus OA [30,31]. In addition, differentially expressed genes have been successfully used to predict the response of RA patients to therapeutic approaches, for example, the capability of certain (type I interferon-responsive) genes to predict rituximab nonresponders [32] and anti-tumor necrosis factor nonresponders [33] or to define homogeneous subgroups within a heterogeneous disease such as RA [22]. However, most studies were not designed to identify gene expression patterns as a potential diagnostic tool, but rather to elucidate the underlying transcriptional networks [34].…”

Section: Introductionmentioning

confidence: 99%

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

et al. 2014

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IntroductionDiscrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers.MethodsThree multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.ResultsThe optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways.ConclusionFirst-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

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Gene Expression Profiling in Rheumatology

Cited by 17 publications

References 15 publications

Pharmacogenomics of infliximab treatment using peripheral blood cells of patients with rheumatoid arthritis

Pharmacogenomics of infliximab treatment using peripheral blood cells of patients with rheumatoid arthritis

A subtype of multiple sclerosis defined by an activated immune defense program

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

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