Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783746036557152256 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8403676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fujisawakota pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT shimomamoru pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT taguchiyh pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT ikematsushinya pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients AT miyataryota pcabasedunsupervisedfeatureextractionforgeneexpressionanalysisofcovid19patients |