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Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling.


ABSTRACT: In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 "predictor genes" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.

SUBMITTER: Liu Z 

PROVIDER: S-EPMC2950309 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling.

Liu Zheng Z   Sokka Tuulikki T   Maas Kevin K   Olsen Nancy J NJ   Aune Thomas M TM  

Human genomics and proteomics : HGP 20090427


In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated w  ...[more]

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