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A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach
AIM OF THE STUDY: Liver regeneration is one of the essential fields of regenerative medicine as a branch of tissue engineering and molecular biology that draws global researchers’ attention. This study aims to conduct a systematic review and meta-analysis on the high-throughput gene expression micro...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284170/ https://www.ncbi.nlm.nih.gov/pubmed/34295986 http://dx.doi.org/10.5114/ceh.2021.107564 |
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author | Asnaashari, Solmaz Amjad, Elham Sokouti, Babak |
author_facet | Asnaashari, Solmaz Amjad, Elham Sokouti, Babak |
author_sort | Asnaashari, Solmaz |
collection | PubMed |
description | AIM OF THE STUDY: Liver regeneration is one of the essential fields of regenerative medicine as a branch of tissue engineering and molecular biology that draws global researchers’ attention. This study aims to conduct a systematic review and meta-analysis on the high-throughput gene expression microarray dataset of liver regeneration on the NCBI-GEO database to identify the significant genes and signaling pathways and confirm the genes from literature studies on associated diseases. MATERIAL AND METHODS: We thoroughly searched the NCBI-GEO database to retrieve and screen the GEO microarray datasets’ contents. Due to the inclusion of different species in eligible GEO datasets in the meta-analysis, the list of significant genes for the random-effects model were identified. Moreover, we carried out detailed gene analyses for three main gene ontology components and the KEGG signaling pathway. Furthermore, we investigated the possibility of genes’ association with liver cancer through the Kaplan-Meier plot. RESULTS: The random-effects model from six eligible GEO datasets identified 71 genes with eight down-regulated and 63 up-regulated genes. The target genes are involved in various cellular functions such as cell proliferation, cell death, and cell cycle control. Finally, we noted that 58 out of 71 genes are associated with different types of diseases related explicitly to other liver and inflammation diseases. CONCLUSIONS: The current study assessed various GEO datasets at the early stages of liver regeneration with promising results. The present systematic review and meta-analysis results are beneficial for future novel drug design and discovery specifically for patients in the liver transplantation process. |
format | Online Article Text |
id | pubmed-8284170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
spelling | pubmed-82841702021-07-21 A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach Asnaashari, Solmaz Amjad, Elham Sokouti, Babak Clin Exp Hepatol Original Paper AIM OF THE STUDY: Liver regeneration is one of the essential fields of regenerative medicine as a branch of tissue engineering and molecular biology that draws global researchers’ attention. This study aims to conduct a systematic review and meta-analysis on the high-throughput gene expression microarray dataset of liver regeneration on the NCBI-GEO database to identify the significant genes and signaling pathways and confirm the genes from literature studies on associated diseases. MATERIAL AND METHODS: We thoroughly searched the NCBI-GEO database to retrieve and screen the GEO microarray datasets’ contents. Due to the inclusion of different species in eligible GEO datasets in the meta-analysis, the list of significant genes for the random-effects model were identified. Moreover, we carried out detailed gene analyses for three main gene ontology components and the KEGG signaling pathway. Furthermore, we investigated the possibility of genes’ association with liver cancer through the Kaplan-Meier plot. RESULTS: The random-effects model from six eligible GEO datasets identified 71 genes with eight down-regulated and 63 up-regulated genes. The target genes are involved in various cellular functions such as cell proliferation, cell death, and cell cycle control. Finally, we noted that 58 out of 71 genes are associated with different types of diseases related explicitly to other liver and inflammation diseases. CONCLUSIONS: The current study assessed various GEO datasets at the early stages of liver regeneration with promising results. The present systematic review and meta-analysis results are beneficial for future novel drug design and discovery specifically for patients in the liver transplantation process. Termedia Publishing House 2021-06-30 2021-06 /pmc/articles/PMC8284170/ /pubmed/34295986 http://dx.doi.org/10.5114/ceh.2021.107564 Text en Copyright © 2021 Clinical and Experimental Hepatology https://creativecommons.org/licenses/by-nc-sa/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/ (https://creativecommons.org/licenses/by-nc-sa/4.0/) ) |
spellingShingle | Original Paper Asnaashari, Solmaz Amjad, Elham Sokouti, Babak A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title | A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title_full | A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title_fullStr | A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title_full_unstemmed | A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title_short | A comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
title_sort | comprehensive investigation on liver regeneration: a meta-analysis and systems biology approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284170/ https://www.ncbi.nlm.nih.gov/pubmed/34295986 http://dx.doi.org/10.5114/ceh.2021.107564 |
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