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Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets

BACKGROUND: 2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world....

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Autores principales: Zhang, Churen, Sun, Yuzhe, Xu, Min, Shu, Chang, Yue, Zhaoguo, Hou, Jianxia, Ou, Dongchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682728/
https://www.ncbi.nlm.nih.gov/pubmed/36414950
http://dx.doi.org/10.1186/s12903-022-02435-4
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author Zhang, Churen
Sun, Yuzhe
Xu, Min
Shu, Chang
Yue, Zhaoguo
Hou, Jianxia
Ou, Dongchen
author_facet Zhang, Churen
Sun, Yuzhe
Xu, Min
Shu, Chang
Yue, Zhaoguo
Hou, Jianxia
Ou, Dongchen
author_sort Zhang, Churen
collection PubMed
description BACKGROUND: 2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world. Because SARS-CoV-2 infection can be spread by contact with the oral cavity, the link between oral illness and COVID-19 is gaining traction. Through bioinformatics approaches, we explored the possible molecular mechanisms linking the COVID-19 and periodontitis to provide the basis and direction for future research. METHODS: Transcriptomic data from blood samples of patients with COVID-19 and periodontitis was downloaded from the Gene Expression Omnibus database. The shared differentially expressed genes were identified. The analysis of Gene Ontology, Kyoto Encyclopedia of Genesand Genomes pathway, and protein–protein interaction network was conducted for the shared differentially expressed genes. Top 5 hub genes were selected through Maximal Clique Centrality algorithm. Then mRNA-miRNA network of the hub genes was established based on miRDB database, miRTarbase database and Targetscan database. The Least absolute shrinkage and selection operator regression analysis was used to discover possible biomarkers, which were then investigated in relation to immune-related genes. RESULTS: Fifty-six shared genes were identified through differential expression analysis in COVID-19 and periodontitis. The function of these genes was enriched in regulation of hormone secretion, regulation of secretion by cell. Myozenin 2 was identified through Least absolute shrinkage and selection operator regression Analysis, which was down-regulated in both COVID-19 and periodontitis. There was a positive correlation between Myozenin 2 and the biomarker of activated B cell, memory B cell, effector memory CD4 T cell, Type 17 helper cell, T follicular helper cell and Type 2 helper cell. CONCLUSION: By bioinformatics analysis, Myozenin 2 is predicted to correlate to the pathogenesis and immune infiltrating of COVID-19 and periodontitis. However, more clinical and experimental researches are needed to validate the function of Myozenin 2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-022-02435-4.
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spelling pubmed-96827282022-11-24 Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets Zhang, Churen Sun, Yuzhe Xu, Min Shu, Chang Yue, Zhaoguo Hou, Jianxia Ou, Dongchen BMC Oral Health Research BACKGROUND: 2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world. Because SARS-CoV-2 infection can be spread by contact with the oral cavity, the link between oral illness and COVID-19 is gaining traction. Through bioinformatics approaches, we explored the possible molecular mechanisms linking the COVID-19 and periodontitis to provide the basis and direction for future research. METHODS: Transcriptomic data from blood samples of patients with COVID-19 and periodontitis was downloaded from the Gene Expression Omnibus database. The shared differentially expressed genes were identified. The analysis of Gene Ontology, Kyoto Encyclopedia of Genesand Genomes pathway, and protein–protein interaction network was conducted for the shared differentially expressed genes. Top 5 hub genes were selected through Maximal Clique Centrality algorithm. Then mRNA-miRNA network of the hub genes was established based on miRDB database, miRTarbase database and Targetscan database. The Least absolute shrinkage and selection operator regression analysis was used to discover possible biomarkers, which were then investigated in relation to immune-related genes. RESULTS: Fifty-six shared genes were identified through differential expression analysis in COVID-19 and periodontitis. The function of these genes was enriched in regulation of hormone secretion, regulation of secretion by cell. Myozenin 2 was identified through Least absolute shrinkage and selection operator regression Analysis, which was down-regulated in both COVID-19 and periodontitis. There was a positive correlation between Myozenin 2 and the biomarker of activated B cell, memory B cell, effector memory CD4 T cell, Type 17 helper cell, T follicular helper cell and Type 2 helper cell. CONCLUSION: By bioinformatics analysis, Myozenin 2 is predicted to correlate to the pathogenesis and immune infiltrating of COVID-19 and periodontitis. However, more clinical and experimental researches are needed to validate the function of Myozenin 2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-022-02435-4. BioMed Central 2022-11-21 /pmc/articles/PMC9682728/ /pubmed/36414950 http://dx.doi.org/10.1186/s12903-022-02435-4 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
Zhang, Churen
Sun, Yuzhe
Xu, Min
Shu, Chang
Yue, Zhaoguo
Hou, Jianxia
Ou, Dongchen
Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title_full Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title_fullStr Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title_full_unstemmed Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title_short Potential links between COVID-19 and periodontitis: a bioinformatic analysis based on GEO datasets
title_sort potential links between covid-19 and periodontitis: a bioinformatic analysis based on geo datasets
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682728/
https://www.ncbi.nlm.nih.gov/pubmed/36414950
http://dx.doi.org/10.1186/s12903-022-02435-4
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