Unknown

Dataset Information

0

Gene Expression Deconvolution for Uncovering Molecular Signatures in Response to Therapy in Juvenile Idiopathic Arthritis.


ABSTRACT: Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including ?-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.

SUBMITTER: Cui A 

PROVIDER: S-EPMC4887077 | BioStudies | 2016-01-01

REPOSITORIES: biostudies

Similar Datasets

2019-01-01 | S-EPMC6952798 | BioStudies
2019-01-01 | S-EPMC6649876 | BioStudies
1000-01-01 | S-EPMC5850489 | BioStudies
2018-01-01 | S-EPMC6191408 | BioStudies
2020-01-01 | S-EPMC7411147 | BioStudies
1000-01-01 | S-EPMC3729726 | BioStudies
1000-01-01 | S-EPMC5759884 | BioStudies
2010-01-01 | S-EPMC2918379 | BioStudies
2018-01-01 | S-EPMC6105455 | BioStudies
2016-01-01 | S-EPMC5080347 | BioStudies