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Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling
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...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
SAGE-Hindawi Access to Research
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2950309/ https://www.ncbi.nlm.nih.gov/pubmed/20948566 http://dx.doi.org/10.4061/2009/484351 |
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author | Liu, Zheng Sokka, Tuulikki Maas, Kevin Olsen, Nancy J. Aune, Thomas M. |
author_facet | Liu, Zheng Sokka, Tuulikki Maas, Kevin Olsen, Nancy J. Aune, Thomas M. |
author_sort | Liu, Zheng |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2950309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | SAGE-Hindawi Access to Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-29503092010-10-14 Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling Liu, Zheng Sokka, Tuulikki Maas, Kevin Olsen, Nancy J. Aune, Thomas M. Hum Genomics Proteomics Research Article 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. SAGE-Hindawi Access to Research 2009-04-27 /pmc/articles/PMC2950309/ /pubmed/20948566 http://dx.doi.org/10.4061/2009/484351 Text en Copyright © 2009 Zheng Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Zheng Sokka, Tuulikki Maas, Kevin Olsen, Nancy J. Aune, Thomas M. Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title | Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title_full | Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title_fullStr | Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title_full_unstemmed | Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title_short | Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling |
title_sort | prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2950309/ https://www.ncbi.nlm.nih.gov/pubmed/20948566 http://dx.doi.org/10.4061/2009/484351 |
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