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Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods

BACKGROUND: Despite of numerous studies on periodontitis, the mechanism underlying the progression of periodontitis still remains largely unknown. This study aimed to have an expression profiling comparison between periodontitis and normal control and to identify more candidate genes involved in per...

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Autores principales: Song, Liang, Yao, Jueqi, He, Zhijing, Xu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4559289/
https://www.ncbi.nlm.nih.gov/pubmed/26334995
http://dx.doi.org/10.1186/s12903-015-0086-7
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author Song, Liang
Yao, Jueqi
He, Zhijing
Xu, Bin
author_facet Song, Liang
Yao, Jueqi
He, Zhijing
Xu, Bin
author_sort Song, Liang
collection PubMed
description BACKGROUND: Despite of numerous studies on periodontitis, the mechanism underlying the progression of periodontitis still remains largely unknown. This study aimed to have an expression profiling comparison between periodontitis and normal control and to identify more candidate genes involved in periodontitis and to gain more insights into the molecular mechanisms of periodontitis progression. METHODS: The gene expression profile of GSE16134, comprising 241 gingival tissue specimens and 69 healthy samples as control which were obtained from 120 systemically healthy patients with periodontitis (65 with chronic and 55 with aggressive periodontitis), was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in periodontitis samples were screened using the limma package in R compared with control samples. Gene Ontology (GO) and pathway enrichment analysis upon the DEGs were carried out using Hypergeometric Distribution test. Protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape, followed by module selection from the PPI network using MCODE plugin. Moreover, transcription factors (TFs) of these DEGs were identified based on TRANSFAC database and then a regulatory network was constructed. RESULTS: Totally, 762 DEGs (507 up- and 255 down-regulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as immune system process, cell activation biological processes, cytokine-cytokine receptor interaction, and metabolic pathways. Cathepsin S (CTSS) and pleckstrin (PLEK) were the hub proteins in the PPI network and 3 significant modules were selected. Moreover, 19 TFs were identified including interferon regulatory factor 8 (IRF8), and FBJ murine osteosarcoma viral oncogene homolog B (FOSB). CONCLUSION: This study identified genes (CTSS, PLEK, IRF-8, PTGS2, and FOSB) that may be involved in the development and progression of periodontitis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12903-015-0086-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-45592892015-09-04 Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods Song, Liang Yao, Jueqi He, Zhijing Xu, Bin BMC Oral Health Research Article BACKGROUND: Despite of numerous studies on periodontitis, the mechanism underlying the progression of periodontitis still remains largely unknown. This study aimed to have an expression profiling comparison between periodontitis and normal control and to identify more candidate genes involved in periodontitis and to gain more insights into the molecular mechanisms of periodontitis progression. METHODS: The gene expression profile of GSE16134, comprising 241 gingival tissue specimens and 69 healthy samples as control which were obtained from 120 systemically healthy patients with periodontitis (65 with chronic and 55 with aggressive periodontitis), was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in periodontitis samples were screened using the limma package in R compared with control samples. Gene Ontology (GO) and pathway enrichment analysis upon the DEGs were carried out using Hypergeometric Distribution test. Protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape, followed by module selection from the PPI network using MCODE plugin. Moreover, transcription factors (TFs) of these DEGs were identified based on TRANSFAC database and then a regulatory network was constructed. RESULTS: Totally, 762 DEGs (507 up- and 255 down-regulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as immune system process, cell activation biological processes, cytokine-cytokine receptor interaction, and metabolic pathways. Cathepsin S (CTSS) and pleckstrin (PLEK) were the hub proteins in the PPI network and 3 significant modules were selected. Moreover, 19 TFs were identified including interferon regulatory factor 8 (IRF8), and FBJ murine osteosarcoma viral oncogene homolog B (FOSB). CONCLUSION: This study identified genes (CTSS, PLEK, IRF-8, PTGS2, and FOSB) that may be involved in the development and progression of periodontitis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12903-015-0086-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-04 /pmc/articles/PMC4559289/ /pubmed/26334995 http://dx.doi.org/10.1186/s12903-015-0086-7 Text en © Song et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Song, Liang
Yao, Jueqi
He, Zhijing
Xu, Bin
Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title_full Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title_fullStr Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title_full_unstemmed Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title_short Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
title_sort genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4559289/
https://www.ncbi.nlm.nih.gov/pubmed/26334995
http://dx.doi.org/10.1186/s12903-015-0086-7
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