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Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis
There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy cont...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223752/ https://www.ncbi.nlm.nih.gov/pubmed/34177888 http://dx.doi.org/10.3389/fimmu.2021.638066 |
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author | Rychkov, Dmitry Neely, Jessica Oskotsky, Tomiko Yu, Steven Perlmutter, Noah Nititham, Joanne Carvidi, Alexander Krueger, Melissa Gross, Andrew Criswell, Lindsey A. Ashouri, Judith F. Sirota, Marina |
author_facet | Rychkov, Dmitry Neely, Jessica Oskotsky, Tomiko Yu, Steven Perlmutter, Noah Nititham, Joanne Carvidi, Alexander Krueger, Melissa Gross, Andrew Criswell, Lindsey A. Ashouri, Judith F. Sirota, Marina |
author_sort | Rychkov, Dmitry |
collection | PubMed |
description | There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90. |
format | Online Article Text |
id | pubmed-8223752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82237522021-06-25 Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis Rychkov, Dmitry Neely, Jessica Oskotsky, Tomiko Yu, Steven Perlmutter, Noah Nititham, Joanne Carvidi, Alexander Krueger, Melissa Gross, Andrew Criswell, Lindsey A. Ashouri, Judith F. Sirota, Marina Front Immunol Immunology There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90. Frontiers Media S.A. 2021-06-08 /pmc/articles/PMC8223752/ /pubmed/34177888 http://dx.doi.org/10.3389/fimmu.2021.638066 Text en Copyright © 2021 Rychkov, Neely, Oskotsky, Yu, Perlmutter, Nititham, Carvidi, Krueger, Gross, Criswell, Ashouri and Sirota https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Rychkov, Dmitry Neely, Jessica Oskotsky, Tomiko Yu, Steven Perlmutter, Noah Nititham, Joanne Carvidi, Alexander Krueger, Melissa Gross, Andrew Criswell, Lindsey A. Ashouri, Judith F. Sirota, Marina Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title_full | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title_fullStr | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title_full_unstemmed | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title_short | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
title_sort | cross-tissue transcriptomic analysis leveraging machine learning approaches identifies new biomarkers for rheumatoid arthritis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223752/ https://www.ncbi.nlm.nih.gov/pubmed/34177888 http://dx.doi.org/10.3389/fimmu.2021.638066 |
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