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Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease affecting people’s health worldwide. Exploring the potential biomarkers and dynamic networks during NAFLD progression is urgently important. MATERIAL AND METHODS: Differentially expressed genes (DEGs) in ob...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380347/ https://www.ncbi.nlm.nih.gov/pubmed/34419146 http://dx.doi.org/10.1186/s41065-021-00196-8 |
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author | Zheng, Jing Wu, Huizhong Zhang, Zhiying Yao, Songqiang |
author_facet | Zheng, Jing Wu, Huizhong Zhang, Zhiying Yao, Songqiang |
author_sort | Zheng, Jing |
collection | PubMed |
description | BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease affecting people’s health worldwide. Exploring the potential biomarkers and dynamic networks during NAFLD progression is urgently important. MATERIAL AND METHODS: Differentially expressed genes (DEGs) in obesity, NAFL and NASH were screened from GSE126848 and GSE130970, respectively. Gene set enrichment analysis of DEGs was conducted to reveal the Gene Ontology (GO) biological process in each period. Dynamic molecular networks were constructed by DyNet to illustrate the common and distinct progression of health- or obesity-derived NAFLD. The dynamic co-expression modular analysis was carried out by CEMiTool to elucidate the key modulators, networks, and enriched pathways during NAFLD. RESULTS: A total of 453 DEGs were filtered from obesity, NAFL and NASH periods. Function annotation showed that health-NAFLD sequence was mainly associated with dysfunction of metabolic syndrome pathways, while obesity-NAFLD sequence exhibited dysregulation of Cell cycle and Cellular senescence pathways. Nine nodes including COL3A1, CXCL9, CYCS, CXCL10, THY1, COL1A2, SAA1, CDKN1A, and JUN in the dynamic networks were commonly identified in health- and obesity-derived NAFLD. Moreover, CYCS, whose role is unknown in NAFLD, possessed the highest correlation with NAFLD activity score, lobular inflammation grade, and the cytological ballooning grade. Dynamic co-expression modular analysis showed that module 4 was activated in NAFL and NASH, while module 3 was inhibited at NAFLD stages. Module 3 was negatively correlated with CXCL10, and module 4 was positively correlated with COL1A2 and THY1. CONCLUSION: Dynamic network analysis and dynamic gene co-expression modular analysis identified a nine-gene signature as the potential key regulator in NAFLD progression, which provided comprehensive regulatory mechanisms underlying NAFLD progression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-021-00196-8. |
format | Online Article Text |
id | pubmed-8380347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83803472021-08-23 Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease Zheng, Jing Wu, Huizhong Zhang, Zhiying Yao, Songqiang Hereditas Research BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease affecting people’s health worldwide. Exploring the potential biomarkers and dynamic networks during NAFLD progression is urgently important. MATERIAL AND METHODS: Differentially expressed genes (DEGs) in obesity, NAFL and NASH were screened from GSE126848 and GSE130970, respectively. Gene set enrichment analysis of DEGs was conducted to reveal the Gene Ontology (GO) biological process in each period. Dynamic molecular networks were constructed by DyNet to illustrate the common and distinct progression of health- or obesity-derived NAFLD. The dynamic co-expression modular analysis was carried out by CEMiTool to elucidate the key modulators, networks, and enriched pathways during NAFLD. RESULTS: A total of 453 DEGs were filtered from obesity, NAFL and NASH periods. Function annotation showed that health-NAFLD sequence was mainly associated with dysfunction of metabolic syndrome pathways, while obesity-NAFLD sequence exhibited dysregulation of Cell cycle and Cellular senescence pathways. Nine nodes including COL3A1, CXCL9, CYCS, CXCL10, THY1, COL1A2, SAA1, CDKN1A, and JUN in the dynamic networks were commonly identified in health- and obesity-derived NAFLD. Moreover, CYCS, whose role is unknown in NAFLD, possessed the highest correlation with NAFLD activity score, lobular inflammation grade, and the cytological ballooning grade. Dynamic co-expression modular analysis showed that module 4 was activated in NAFL and NASH, while module 3 was inhibited at NAFLD stages. Module 3 was negatively correlated with CXCL10, and module 4 was positively correlated with COL1A2 and THY1. CONCLUSION: Dynamic network analysis and dynamic gene co-expression modular analysis identified a nine-gene signature as the potential key regulator in NAFLD progression, which provided comprehensive regulatory mechanisms underlying NAFLD progression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-021-00196-8. BioMed Central 2021-08-21 /pmc/articles/PMC8380347/ /pubmed/34419146 http://dx.doi.org/10.1186/s41065-021-00196-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zheng, Jing Wu, Huizhong Zhang, Zhiying Yao, Songqiang Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title | Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title_full | Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title_fullStr | Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title_full_unstemmed | Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title_short | Dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
title_sort | dynamic co-expression modular network analysis in nonalcoholic fatty liver disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380347/ https://www.ncbi.nlm.nih.gov/pubmed/34419146 http://dx.doi.org/10.1186/s41065-021-00196-8 |
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