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Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis

BACKGROUND: Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. METHODS: miRNA and mRNA expression arrays of...

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Autores principales: Tai, Yang, Zhao, Chong, Gao, Jinhang, Lan, Tian, Tong, Huan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351572/
https://www.ncbi.nlm.nih.gov/pubmed/34434654
http://dx.doi.org/10.7717/peerj.11910
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author Tai, Yang
Zhao, Chong
Gao, Jinhang
Lan, Tian
Tong, Huan
author_facet Tai, Yang
Zhao, Chong
Gao, Jinhang
Lan, Tian
Tong, Huan
author_sort Tai, Yang
collection PubMed
description BACKGROUND: Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. METHODS: miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. RESULTS: By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. CONCLUSIONS: We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.
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spelling pubmed-83515722021-08-24 Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis Tai, Yang Zhao, Chong Gao, Jinhang Lan, Tian Tong, Huan PeerJ Bioinformatics BACKGROUND: Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. METHODS: miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. RESULTS: By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. CONCLUSIONS: We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment. PeerJ Inc. 2021-08-06 /pmc/articles/PMC8351572/ /pubmed/34434654 http://dx.doi.org/10.7717/peerj.11910 Text en ©2021 Tai et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Tai, Yang
Zhao, Chong
Gao, Jinhang
Lan, Tian
Tong, Huan
Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title_full Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title_fullStr Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title_full_unstemmed Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title_short Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
title_sort identification of mirna-target gene regulatory networks in liver fibrosis based on bioinformatics analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351572/
https://www.ncbi.nlm.nih.gov/pubmed/34434654
http://dx.doi.org/10.7717/peerj.11910
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