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PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-1...

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Autores principales: Fujisawa, Kota, Shimo, Mamoru, Taguchi, Y.-H., Ikematsu, Shinya, Miyata, Ryota
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403676/
https://www.ncbi.nlm.nih.gov/pubmed/34456333
http://dx.doi.org/10.1038/s41598-021-95698-w
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author Fujisawa, Kota
Shimo, Mamoru
Taguchi, Y.-H.
Ikematsu, Shinya
Miyata, Ryota
author_facet Fujisawa, Kota
Shimo, Mamoru
Taguchi, Y.-H.
Ikematsu, Shinya
Miyata, Ryota
author_sort Fujisawa, Kota
collection PubMed
description Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.
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spelling pubmed-84036762021-09-01 PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients Fujisawa, Kota Shimo, Mamoru Taguchi, Y.-H. Ikematsu, Shinya Miyata, Ryota Sci Rep Article Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood. Nature Publishing Group UK 2021-08-30 /pmc/articles/PMC8403676/ /pubmed/34456333 http://dx.doi.org/10.1038/s41598-021-95698-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Fujisawa, Kota
Shimo, Mamoru
Taguchi, Y.-H.
Ikematsu, Shinya
Miyata, Ryota
PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title_full PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title_fullStr PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title_full_unstemmed PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title_short PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
title_sort pca-based unsupervised feature extraction for gene expression analysis of covid-19 patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403676/
https://www.ncbi.nlm.nih.gov/pubmed/34456333
http://dx.doi.org/10.1038/s41598-021-95698-w
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