Cargando…
Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease
Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screen...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256337/ https://www.ncbi.nlm.nih.gov/pubmed/37335656 http://dx.doi.org/10.1097/MD.0000000000033912 |
_version_ | 1785057082203963392 |
---|---|
author | Dong, Wenbo Jin, Yan Shi, Hongshuo Zhang, Xuecheng Chen, Jinshu Jia, Hongling Zhang, Yongchen |
author_facet | Dong, Wenbo Jin, Yan Shi, Hongshuo Zhang, Xuecheng Chen, Jinshu Jia, Hongling Zhang, Yongchen |
author_sort | Dong, Wenbo |
collection | PubMed |
description | Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screened using the Gene Expression Omnibus. Common differentially expressed genes were then identified using a Venn diagram. Gene ontology analysis and KEGG pathway enrichment were performed on the differentially expressed genes. A protein–protein interaction network was also constructed using the STRING platform, and key genes were identified using the Cytoscape plugin. GES63067 was selected for validation of the results. Analysis of ferroptosis gene expression during the development of the 2 diseases and prediction of their upstream miRNAs and lncRNAs. In addition, transcription factors (TFs) and miRNAs related to key genes were identified. Effective drugs that act on target genes were found in the DSigDB. The GSE147507 and GSE126848 datasets were crossed to obtain 28 co-regulated genes, 22 gene ontology terms, 3 KEGG pathways, and 10 key genes. NAFLD may affect COVID-19 progression through immune function and inflammatory signaling pathways. CYBB was predicted to be a differential ferroptosis gene associated with 2 diseases, and the CYBB-hsa-miR-196a/b-5p-TUG1 regulatory axis was identified. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Eckol, sulfinpyrazone, and phenylbutazone) were considered as target drugs for Patients with COVID-19 and NAFLD. This study identified key gene and defined molecular mechanisms associated with the progression of COVID-19 and NAFLD. COVID-19 and NAFLD progression may regulate ferroptosis through the CYBB-hsa-miR-196a/b-5p-TUG1 axis. This study provides additional drug options for the treatment of COVID-19 combined with NAFLD disease. |
format | Online Article Text |
id | pubmed-10256337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102563372023-06-10 Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease Dong, Wenbo Jin, Yan Shi, Hongshuo Zhang, Xuecheng Chen, Jinshu Jia, Hongling Zhang, Yongchen Medicine (Baltimore) 4400 Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screened using the Gene Expression Omnibus. Common differentially expressed genes were then identified using a Venn diagram. Gene ontology analysis and KEGG pathway enrichment were performed on the differentially expressed genes. A protein–protein interaction network was also constructed using the STRING platform, and key genes were identified using the Cytoscape plugin. GES63067 was selected for validation of the results. Analysis of ferroptosis gene expression during the development of the 2 diseases and prediction of their upstream miRNAs and lncRNAs. In addition, transcription factors (TFs) and miRNAs related to key genes were identified. Effective drugs that act on target genes were found in the DSigDB. The GSE147507 and GSE126848 datasets were crossed to obtain 28 co-regulated genes, 22 gene ontology terms, 3 KEGG pathways, and 10 key genes. NAFLD may affect COVID-19 progression through immune function and inflammatory signaling pathways. CYBB was predicted to be a differential ferroptosis gene associated with 2 diseases, and the CYBB-hsa-miR-196a/b-5p-TUG1 regulatory axis was identified. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Eckol, sulfinpyrazone, and phenylbutazone) were considered as target drugs for Patients with COVID-19 and NAFLD. This study identified key gene and defined molecular mechanisms associated with the progression of COVID-19 and NAFLD. COVID-19 and NAFLD progression may regulate ferroptosis through the CYBB-hsa-miR-196a/b-5p-TUG1 axis. This study provides additional drug options for the treatment of COVID-19 combined with NAFLD disease. Lippincott Williams & Wilkins 2023-06-09 /pmc/articles/PMC10256337/ /pubmed/37335656 http://dx.doi.org/10.1097/MD.0000000000033912 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | 4400 Dong, Wenbo Jin, Yan Shi, Hongshuo Zhang, Xuecheng Chen, Jinshu Jia, Hongling Zhang, Yongchen Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title | Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title_full | Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title_fullStr | Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title_full_unstemmed | Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title_short | Using bioinformatics and systems biology methods to identify the mechanism of interaction between COVID-19 and nonalcoholic fatty liver disease |
title_sort | using bioinformatics and systems biology methods to identify the mechanism of interaction between covid-19 and nonalcoholic fatty liver disease |
topic | 4400 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256337/ https://www.ncbi.nlm.nih.gov/pubmed/37335656 http://dx.doi.org/10.1097/MD.0000000000033912 |
work_keys_str_mv | AT dongwenbo usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT jinyan usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT shihongshuo usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT zhangxuecheng usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT chenjinshu usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT jiahongling usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease AT zhangyongchen usingbioinformaticsandsystemsbiologymethodstoidentifythemechanismofinteractionbetweencovid19andnonalcoholicfattyliverdisease |