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Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis

BACKGROUND: Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA–mRNA–TF regulatory networks to investigate the underlying molecular mechan...

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Autores principales: Gao, Xiaoli, Zhao, Dong, Han, Jing, Zhang, Zheng, Wang, Zuomin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994180/
https://www.ncbi.nlm.nih.gov/pubmed/35397550
http://dx.doi.org/10.1186/s12903-022-02150-0
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author Gao, Xiaoli
Zhao, Dong
Han, Jing
Zhang, Zheng
Wang, Zuomin
author_facet Gao, Xiaoli
Zhao, Dong
Han, Jing
Zhang, Zheng
Wang, Zuomin
author_sort Gao, Xiaoli
collection PubMed
description BACKGROUND: Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA–mRNA–TF regulatory networks to investigate the underlying molecular mechanism in periodontitis. METHODS: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using the “limma” package in R. The intersection of the target genes of candidate DEMis and DEMs were considered significant DEMs in the regulatory network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Subsequently, DEMs were uploaded to the STRING database, a protein–protein interaction (PPI) network was established, and the cytoHubba and MCODE plugins were used to screen out key hub mRNAs and significant modules. Ultimately, to investigate the regulatory network underlying periodontitis, a global triple network including miRNAs, mRNAs, and TFs was constructed using Cytoscape software. RESULTS: 8 DEMis and 121 DEMs were found between the periodontal and control groups. GO analysis showed that mRNAs were most significantly enriched in positive regulation of the cell cycle, and KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. A PPI network was constructed including 81 nodes and 414 edges. Furthermore, 12 hub genes ranked by the top 10% genes with high degree connectivity and five TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of periodontitis. Additionally, a miRNA–mRNA–TF coregulatory network was established. CONCLUSION: In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a TF–miRNA–mRNA network was established to provide a comprehensive perspective of the regulatory mechanism networks of periodontitis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-022-02150-0.
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spelling pubmed-89941802022-04-10 Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis Gao, Xiaoli Zhao, Dong Han, Jing Zhang, Zheng Wang, Zuomin BMC Oral Health Research BACKGROUND: Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA–mRNA–TF regulatory networks to investigate the underlying molecular mechanism in periodontitis. METHODS: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using the “limma” package in R. The intersection of the target genes of candidate DEMis and DEMs were considered significant DEMs in the regulatory network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Subsequently, DEMs were uploaded to the STRING database, a protein–protein interaction (PPI) network was established, and the cytoHubba and MCODE plugins were used to screen out key hub mRNAs and significant modules. Ultimately, to investigate the regulatory network underlying periodontitis, a global triple network including miRNAs, mRNAs, and TFs was constructed using Cytoscape software. RESULTS: 8 DEMis and 121 DEMs were found between the periodontal and control groups. GO analysis showed that mRNAs were most significantly enriched in positive regulation of the cell cycle, and KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. A PPI network was constructed including 81 nodes and 414 edges. Furthermore, 12 hub genes ranked by the top 10% genes with high degree connectivity and five TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of periodontitis. Additionally, a miRNA–mRNA–TF coregulatory network was established. CONCLUSION: In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a TF–miRNA–mRNA network was established to provide a comprehensive perspective of the regulatory mechanism networks of periodontitis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-022-02150-0. BioMed Central 2022-04-09 /pmc/articles/PMC8994180/ /pubmed/35397550 http://dx.doi.org/10.1186/s12903-022-02150-0 Text en © The Author(s) 2022 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
Gao, Xiaoli
Zhao, Dong
Han, Jing
Zhang, Zheng
Wang, Zuomin
Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title_full Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title_fullStr Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title_full_unstemmed Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title_short Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis
title_sort identification of microrna–mrna–tf regulatory networks in periodontitis by bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994180/
https://www.ncbi.nlm.nih.gov/pubmed/35397550
http://dx.doi.org/10.1186/s12903-022-02150-0
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