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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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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. |
format | Online Article Text |
id | pubmed-5031349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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