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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
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.
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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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