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Can baseline serum microRNAs predict response to TNF-alpha inhibitors in rheumatoid arthritis?

Overview of attention for article published in Arthritis Research & Therapy, August 2016
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Title
Can baseline serum microRNAs predict response to TNF-alpha inhibitors in rheumatoid arthritis?
Published in
Arthritis Research & Therapy, August 2016
DOI 10.1186/s13075-016-1085-z
Pubmed ID
Authors

Bart V. J. Cuppen, Marzia Rossato, Ruth D. E. Fritsch-Stork, Arno N. Concepcion, Yolande Schenk, Johannes W. J. Bijlsma, Timothy R. D. J. Radstake, Floris P. J. G. Lafeber, on behalf of all SRU investigators

Abstract

In rheumatoid arthritis, prediction of response to TNF-alpha inhibitor (TNFi) treatment would be of clinical value. This study aims to discover miRNAs that predict response and aims to replicate results of two previous studies addressing this topic. From the observational BiOCURA cohort, 40 adalimumab- (ADA) and 40 etanercept- (ETN) treated patients were selected to enter the discovery cohort and baseline serum profiling on 758 miRNAs was performed. The added value of univariately selected miRNAs (p < 0.05) over clinical parameters in prediction of response was determined by means of the area under the receiver operating characteristic curve (AUC-ROC). Validation was performed by TaqMan single qPCR assays in 40 new patients. Expression of miR-99a and miR-143 predicted response to ADA, and miR-23a and miR-197 predicted response to ETN. The addition of miRNAs increased the AUC-ROC of a model containing only clinical parameters for ADA (0.75 to 0.97) and ETN (0.68 to 0.78). In validation, none of the selected miRNAs significantly predicted response. miR-23a was the only overlapping miRNA compared to the two previous studies, however inversely related with response in one of these studies. The reasons for the inability to replicate previously proposed miRNAs predicting response to TNFi and replicate those from the discovery cohort were investigated and discussed. To date, no miRNA consistently predicting response to TNFi therapy in RA has been identified. Future studies on this topic should meet a minimum of standards in design that are addressed in this study, in order to increase the reproducibility.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 17%
Student > Master 8 15%
Other 5 10%
Researcher 5 10%
Student > Postgraduate 5 10%
Other 9 17%
Unknown 11 21%
Readers by discipline Count As %
Medicine and Dentistry 19 37%
Agricultural and Biological Sciences 4 8%
Immunology and Microbiology 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 3 6%
Computer Science 2 4%
Other 3 6%
Unknown 17 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2016.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Arthritis Research & Therapy
#3,132
of 3,381 outputs
Outputs of similar age
#312,504
of 352,645 outputs
Outputs of similar age from Arthritis Research & Therapy
#48
of 54 outputs
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