HOME    About this site    mypage    Japanese    library    university    Feedback

University of the Ryukyus Repository >
Faculty of Engineering >
Peer-reviewed Journal Articles (Faculty of Engineering) >

 
Title :PCA‑based unsupervised feature extraction for gene expression analysis of COVID‑19 patients
Authors :Fujisawa, Kota
Shimo, Mamoru
Taguchi, Y.‑H.
Ikematsu, Shinya
Miyata, Ryota
Issue Date :30-Aug-2021
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.
URL :https://doi.org/10.1038/s41598-021-95698-w
Type Local :雑誌掲載論文
ISSN :2045-2322
Publisher :Nature Research
URI :http://hdl.handle.net/20.500.12000/49789
Citation :Scientific Reports Vol.11
Appears in Collections:Peer-reviewed Journal Articles (Faculty of Engineering)

Files in This Item:

File Description SizeFormat
s41598-021-95698-w.pdf1400KbAdobe PDFView/Open