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Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis

BACKGROUND: The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis. METHODS: The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection s...

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Autores principales: Qin, Xue-Bing, Zhang, Wei-Jue, Zou, Lin, Huang, Pei-Jia, Sun, Bao-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031349/
https://www.ncbi.nlm.nih.gov/pubmed/27655333
http://dx.doi.org/10.1186/s12879-016-1822-6
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author Qin, Xue-Bing
Zhang, Wei-Jue
Zou, Lin
Huang, Pei-Jia
Sun, Bao-Jun
author_facet Qin, Xue-Bing
Zhang, Wei-Jue
Zou, Lin
Huang, Pei-Jia
Sun, Bao-Jun
author_sort Qin, Xue-Bing
collection PubMed
description BACKGROUND: The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis. METHODS: The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection samples were used to identify the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed by Cytoscape software. Then network-based neighborhood scoring analysis was performed to identify the important genes. Furthermore, the functional enrichment analysis, correlation analysis and logistic regression analysis for the identified important genes were performed. RESULTS: A total of 1084 DEGs were identified, including 565 down- and 519 up-regulated genes. The PPI network was constructed with 446 nodes and 768 edges. Down-regulated genes RIC8 guanine nucleotide exchange factor A (RIC8A), basic leucine zipper transcription factor, ATF-like (BATF) and microtubule associated monooxygenase, calponin LIM domain containing 1 (MICAL1) and up-regulated genes ATPase, Na+/K+ transporting, alpha 4 polypeptide (ATP1A4), histone cluster 1, H3c (HIST1H3C), histone cluster 2, H3d (HIST2H3D), histone cluster 1, H3e (HIST1H3E) and tyrosine kinase 2 (TYK2) were selected as important genes in network-based neighborhood scoring analysis. The functional enrichment analysis results showed that these important DEGs were mainly enriched in regulation of osteoblast differentiation and nucleoside triphosphate biosynthetic process. The gene pairs RIC8A-ATP1A4, HIST1H3C-HIST2H3D, HIST1H3E-BATF and MICAL1-TYK2 were identified with high positive correlations. Besides, these genes were selected as significant feature genes in logistic regression analysis. CONCLUSIONS: The genes such as RIC8A, ATP1A4, HIST1H3C, HIST2H3D, HIST1H3E, BATF, MICAL1 and TYK2 may be potential biomarkers in pulmonary TB or TB latent infection.
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spelling pubmed-50313492016-09-29 Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis Qin, Xue-Bing Zhang, Wei-Jue Zou, Lin Huang, Pei-Jia Sun, Bao-Jun BMC Infect Dis Research Article BACKGROUND: The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis. METHODS: The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection samples were used to identify the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed by Cytoscape software. Then network-based neighborhood scoring analysis was performed to identify the important genes. Furthermore, the functional enrichment analysis, correlation analysis and logistic regression analysis for the identified important genes were performed. RESULTS: A total of 1084 DEGs were identified, including 565 down- and 519 up-regulated genes. The PPI network was constructed with 446 nodes and 768 edges. Down-regulated genes RIC8 guanine nucleotide exchange factor A (RIC8A), basic leucine zipper transcription factor, ATF-like (BATF) and microtubule associated monooxygenase, calponin LIM domain containing 1 (MICAL1) and up-regulated genes ATPase, Na+/K+ transporting, alpha 4 polypeptide (ATP1A4), histone cluster 1, H3c (HIST1H3C), histone cluster 2, H3d (HIST2H3D), histone cluster 1, H3e (HIST1H3E) and tyrosine kinase 2 (TYK2) were selected as important genes in network-based neighborhood scoring analysis. The functional enrichment analysis results showed that these important DEGs were mainly enriched in regulation of osteoblast differentiation and nucleoside triphosphate biosynthetic process. The gene pairs RIC8A-ATP1A4, HIST1H3C-HIST2H3D, HIST1H3E-BATF and MICAL1-TYK2 were identified with high positive correlations. Besides, these genes were selected as significant feature genes in logistic regression analysis. CONCLUSIONS: The genes such as RIC8A, ATP1A4, HIST1H3C, HIST2H3D, HIST1H3E, BATF, MICAL1 and TYK2 may be potential biomarkers in pulmonary TB or TB latent infection. BioMed Central 2016-09-21 /pmc/articles/PMC5031349/ /pubmed/27655333 http://dx.doi.org/10.1186/s12879-016-1822-6 Text en © The Author(s). 2016 Open AccessThis 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
Qin, Xue-Bing
Zhang, Wei-Jue
Zou, Lin
Huang, Pei-Jia
Sun, Bao-Jun
Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title_full Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title_fullStr Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title_full_unstemmed Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title_short Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
title_sort identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031349/
https://www.ncbi.nlm.nih.gov/pubmed/27655333
http://dx.doi.org/10.1186/s12879-016-1822-6
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