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

Abstract 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 o...

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Autores principales:	Kota Fujisawa, Mamoru Shimo, Y.-H. Taguchi, Shinya Ikematsu, Ryota Miyata
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b1c1ba980c0a438490674f11c0b77ecc
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spelling	oai:doaj.org-article:b1c1ba980c0a438490674f11c0b77ecc2021-12-02T16:38:25ZPCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients10.1038/s41598-021-95698-w2045-2322https://doaj.org/article/b1c1ba980c0a438490674f11c0b77ecc2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95698-whttps://doaj.org/toc/2045-2322Abstract 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.Kota FujisawaMamoru ShimoY.-H. TaguchiShinya IkematsuRyota MiyataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kota Fujisawa Mamoru Shimo Y.-H. Taguchi Shinya Ikematsu Ryota Miyata PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients
description	Abstract 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.
format	article
author	Kota Fujisawa Mamoru Shimo Y.-H. Taguchi Shinya Ikematsu Ryota Miyata
author_facet	Kota Fujisawa Mamoru Shimo Y.-H. Taguchi Shinya Ikematsu Ryota Miyata
author_sort	Kota Fujisawa
title	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_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_sort	pca-based unsupervised feature extraction for gene expression analysis of covid-19 patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b1c1ba980c0a438490674f11c0b77ecc
work_keys_str_mv	AT kotafujisawa pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT mamorushimo pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT yhtaguchi pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT shinyaikematsu pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT ryotamiyata pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients
_version_	1718383594095771648

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

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