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Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis
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| Zeitschriftentitel: | Frontiers in Immunology |
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| Personen und Körperschaften: | , , , , , , , , , , , |
| In: | Frontiers in Immunology, 12, 2021 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Frontiers Media SA
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| Schlagwörter: |
| 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 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 |
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 |
| spellingShingle |
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 Frontiers in Immunology Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis Immunology Immunology and Allergy |
| author_sort |
rychkov, dmitry |
| spelling |
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 1664-3224 Frontiers Media SA Immunology Immunology and Allergy http://dx.doi.org/10.3389/fimmu.2021.638066 <jats:p>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:<jats:italic>TNFAIP6</jats:italic>,<jats:italic>S100A8</jats:italic>,<jats:italic>TNFSF10</jats:italic>,<jats:italic>DRAM1</jats:italic>,<jats:italic>LY96</jats:italic>,<jats:italic>QPCT</jats:italic>,<jats:italic>KYNU</jats:italic>,<jats:italic>ENTPD1</jats:italic>,<jats:italic>CLIC1</jats:italic>,<jats:italic>ATP6V0E1</jats:italic>,<jats:italic>HSP90AB1</jats:italic>,<jats:italic>NCL</jats:italic>and<jats:italic>CIRBP</jats:italic>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,<jats:italic>TNFAIP6</jats:italic>/TSG6 and<jats:italic>HSP90AB1</jats:italic>/HSP90.</jats:p> Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis Frontiers in Immunology |
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| title |
Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
| title_unstemmed |
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 Immunology and Allergy |
| url |
http://dx.doi.org/10.3389/fimmu.2021.638066 |
| publishDate |
2021 |
| physical |
|
| description |
<jats:p>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:<jats:italic>TNFAIP6</jats:italic>,<jats:italic>S100A8</jats:italic>,<jats:italic>TNFSF10</jats:italic>,<jats:italic>DRAM1</jats:italic>,<jats:italic>LY96</jats:italic>,<jats:italic>QPCT</jats:italic>,<jats:italic>KYNU</jats:italic>,<jats:italic>ENTPD1</jats:italic>,<jats:italic>CLIC1</jats:italic>,<jats:italic>ATP6V0E1</jats:italic>,<jats:italic>HSP90AB1</jats:italic>,<jats:italic>NCL</jats:italic>and<jats:italic>CIRBP</jats:italic>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,<jats:italic>TNFAIP6</jats:italic>/TSG6 and<jats:italic>HSP90AB1</jats:italic>/HSP90.</jats:p> |
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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, 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 |
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| description | <jats:p>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:<jats:italic>TNFAIP6</jats:italic>,<jats:italic>S100A8</jats:italic>,<jats:italic>TNFSF10</jats:italic>,<jats:italic>DRAM1</jats:italic>,<jats:italic>LY96</jats:italic>,<jats:italic>QPCT</jats:italic>,<jats:italic>KYNU</jats:italic>,<jats:italic>ENTPD1</jats:italic>,<jats:italic>CLIC1</jats:italic>,<jats:italic>ATP6V0E1</jats:italic>,<jats:italic>HSP90AB1</jats:italic>,<jats:italic>NCL</jats:italic>and<jats:italic>CIRBP</jats:italic>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,<jats:italic>TNFAIP6</jats:italic>/TSG6 and<jats:italic>HSP90AB1</jats:italic>/HSP90.</jats:p> |
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| spelling | 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 1664-3224 Frontiers Media SA Immunology Immunology and Allergy http://dx.doi.org/10.3389/fimmu.2021.638066 <jats:p>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:<jats:italic>TNFAIP6</jats:italic>,<jats:italic>S100A8</jats:italic>,<jats:italic>TNFSF10</jats:italic>,<jats:italic>DRAM1</jats:italic>,<jats:italic>LY96</jats:italic>,<jats:italic>QPCT</jats:italic>,<jats:italic>KYNU</jats:italic>,<jats:italic>ENTPD1</jats:italic>,<jats:italic>CLIC1</jats:italic>,<jats:italic>ATP6V0E1</jats:italic>,<jats:italic>HSP90AB1</jats:italic>,<jats:italic>NCL</jats:italic>and<jats:italic>CIRBP</jats:italic>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,<jats:italic>TNFAIP6</jats:italic>/TSG6 and<jats:italic>HSP90AB1</jats:italic>/HSP90.</jats:p> Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis Frontiers in Immunology |
| spellingShingle | 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, Frontiers in Immunology, Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis, Immunology, Immunology and Allergy |
| 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 |
| title_unstemmed | Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis |
| topic | Immunology, Immunology and Allergy |
| url | http://dx.doi.org/10.3389/fimmu.2021.638066 |