Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Whole blood transcript profiling of rheumatoid arthritis patients

ABSTRACT: The whole blood was collected pre-treatment from rheumatoid arthritis patients starting the anti_TNF therapy. All patients were naïve to anti_TNFs. The disease activity was measured using the DAS28 score at the pre-treatment visit1 (DAS28_v1) and 14 weeks after treatment visit3 (DAS28_v3). The response to the therapy was evaluated using the EULAR [European League Against Rheumatism] definition of the response. The objective of the data analysis was to identify gene expression coorelating with response as well as to identify genes that differentiate responders versus non-responders pre-treatment. The results of this investigation identified 8 trainscripts that predict responders vs. non-responders with 89% accuracy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE15258 | GEO | 2009/09/14

SECONDARY ACCESSION(S): PRJNA116555

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcription profiling of human whole blood from rheumatoid arthritis patients

Project description:The whole blood was collected pre-treatment from rheumatoid arthritis patients starting the anti_TNF therapy. All patients were naïve to anti_TNFs. The disease activity was measured using the DAS28 score at the pre-treatment visit1 (DAS28_v1) and 14 weeks after treatment visit3 (DAS28_v3). The response to the therapy was evaluated using the EULAR [European League Against Rheumatism] definition of the response. The objective of the data analysis was to identify gene expression coorelating with response as well as to identify genes that differentiate responders versus non-responders pre-treatment. The results of this investigation identified 8 trainscripts that predict responders vs. non-responders with 89% accuracy. Experiment Overall Design: Patients' response to anti-TNF was assessed using EULAR score and patients were classified as responders, moderate responders and non-responders. Genes correlating with the response status have been identified.

2009-09-18 | E-GEOD-15258 | biostudies-arrayexpress

Microarray analysis of the synovial membrane of rheumatoid arthritis patients undergoing anti-TNF therapy

Project description:Synovial biopsies of Rheumatoid Arthritis patients were obtained at week 20 of anti-TNF therapy. The clinical response to therapy was determined comparing the DAS28 at this time point with the baseline DAS28, using the EULAR response criteria. Gene expression profiles of patients responding to anti-TNF therapy were compared to non-responders and different genes, pathways and deconvoluted cell types were found to be differential between both groups of rheumatoid arthritis patients.

2020-06-30 | GSE140036 | GEO

RF003-V1: The expression of ERAP2 and LST1 distinguishes between Glucocorticoid-Responders and Non-Responders in Rheumatoid Arthritis

Project description:Objective: Glucocorticoids (GC) have been a cornerstone of Rheumatoid arthritis (RA)-therapy for the last decades. However, about a third of RA-patients do not respond adequately. As monocytes and T-cells play an important role in RA-pathogenesis, the differential gene expression of these cells before pulse therapy with methylprednisolone was evaluated in order to find predictors for clinical response to GC. Methods: CD14-positive and CD4-positive cells were isolated by MACS sorting from five GC-Responders meeting the European League against Rheumatism (EULAR) response criteria and five Non-Responders. The clinical response was measured by disease activity score (DAS28) at the end of pulse therapy (3x 1000mg Methylprednisolone) at day 5. Cellular RNA was hybridized to Agilent 4x44K microarray chips and differentially expressed genes were calculated by MAANOVA. False discovery rate (FDR) was used as multiple testing correction. Selected genes were verified by quantitative real-time PCR (qPCR). Results: Eight genes were differentially regulated in CD14-cells and 4 genes in CD4-T-cells of GC-Responders compared to Non-Responders. Up-regulation of ERAP2 in monocytes and CD4-cells and LST1 and FAM26F in CD4-Tcells of GC-Responders was verified by qPCR and correlated with DAS28 at day 5 and (for LST1 and FAM26F) with smoking. Conclusion: As ERAP2 and LST1 are implicated in autoimmune disease, their increased expression in GC-Responders before GC-therapy may constitute not only a new tool for prediction of the clinical response to GC in RA, but also warrants further investigation to elucidate their role in the inflammatory process of RA.

2017-11-11 | GSE48823 | GEO

Gene expression profiling in Abatacept treated Rheumatoid Arthritis patients according to response

Project description:The objective of this study was to identify specific gene expression profiles able to predict the response of rheumatoid arthritis patients treated with methotrexate /abatacept (Aba). Thirty six RA patients were received Abatacept. The drug efficacy was evaluated with the DAS28 score after 6 months of treatment according to the EULAR response criteria. Among 36 Aba RA patients, 17 were responders and 19 were classified as no-responders. A blood sample was carried out in patients just before the first injection of treatment in PaxGene tubes. Two color experiments : patient(Cy5)/Control pool (Cy3).

2017-05-17 | E-GEOD-68215 | biostudies-arrayexpress

Gene expression profiling in Abatacept treated Rheumatoid Arthritis patients according to response

2017-05-17 | GSE68215 | GEO

Gene expression fluctuation in Abatacept treated Rheumatoid Arthritis patients between baseline and 6 months

Project description:The objective of this study was to identify response-specific gene expression fluctuations in methotrexate /abatacept (Aba) treated rheumatoid arthritis patients : responders (R) VS no-responders (NR). Nineteen RA patients were received Abatacept. The drug efficacy was evaluated with the DAS28 score after 6 months of treatment according to the EULAR response criteria. Among 19 Aba RA patients, 14 were R and 5 were classified as NR. A blood sample was carried out in patients both at baseline (just before the first injection of ABA) and at 6 months in PaxGene tubes.

2020-07-24 | GSE91079 | GEO

Identification of immune-based biomarkers associated with response to abatacept (CTLA4-Ig) in rheumatoid arthritis patients.

Project description:RA patients (according to ACR/EULAR 2010 criteria) who started abatacept due to high disease activity (DAS28>5.1), were recruited to perform immunological studies at baseline, 3 and 6 months of therapy. Peripheral blood mononuclear cells (PBMCs) were isolated and immune cell populations were characterized using flow cytometry. Proteomic analysis was performed on baseline sera.

2024-01-26 | PXD046112 | Pride

Genome-wide transcriptional analysis of synovial tissue gene expression associated with the response to anti-TNF therapy in Rheumatoid Arthritis

Project description:Synovial biopsies of Rheumatoid Arthritis patients with active disease were obtained prior to anti-TNF therapy. Clinical response to anti-TNF treatment was measured 20 weeks later using the EULAR response criteria. Gene expression profiles of patients responding to anti-TNF therapy were compared to non-responders and several genes were found to be differentially expressed between both groups of Rheumatoid Arthritis patients.

2019-07-26 | GSE47726 | GEO

Peripheral blood DNA methylome in adalimumab-treated patients with rheumatoid arthritis

Project description:Background and aims Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade such as anti-TNFÃŽÂ± therapy. Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood Methods DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of adalimumab (ADA) was determined using Illumina HumanMethylationEPIC BeadChip Array. After 6 months, treatment response was assessed according to the Alliance of Associations for Rheumatology (EULAR) criteria for disease response. Patients were classified as responders (DAS28<3.2 or decrease of 1.2 points) or as non-responders (DAS28>5.1 or decrease of less than 0.6 points). Machine learning models were built with gradient boosting and stability selection models to predict response prior to ADA treatment with predictor DNA methylation markers. Results We demonstrated a 27-feature panel of DNA methylation markers or CpG classifiers that predict response in RA patients treated with ADA. Forty-nine patients were assigned as responder and 43 patients assigned as non-responders. We differentiate responders from non-responders with a high sensitivity (AUC 0.76). The predictor CpGs annotated to genes involved in immunological- and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology and angiogenesis. Conclusion Our findings indicate that DNA methylation signatures discriminate responders and non-responders to ADA treatment and can serve as a tool for therapy prediction.

| EGAS00001007578 | EGA

Whole-blood transcriptomic profiling highlights rituximab response in rheumatoid arthritis

Project description:Objective: We performed whole-blood transcriptomic profiling for patients with rheumatoid arthritis (RA) who received rituximab (RTX). We aimed to identify a molecular signature that could predict the clinical response to RTX and transcriptomic changes after RTX therapy. Methods: We performed a microarray assay of the whole human genome with RNA from a peripheral blood sample taken before the first RTX cycle from 68 patients included in the SMART study (24 EULAR non-responders and 44 responders at week 24). The transcriptomic profile was also assessed 24 weeks after the first RTX administration

2015-01-01 | E-GEOD-54629 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data