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Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis

BACKGROUND: Sarcoidosis (SARC) is a multisystem inflammatory disease of unknown etiology and pulmonary tuberculosis (PTB) is caused by Mycobacterium tuberculosis. Both of these diseases affect lungs and lymph nodes and share similar clinical manifestations. However, the underlying mechanisms for the...

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Autores principales: Zhao, Min, Di, Xin, Jin, Xin, Tian, Chang, Cong, Shan, Liu, Jiangyin, Wang, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397754/
https://www.ncbi.nlm.nih.gov/pubmed/32701935
http://dx.doi.org/10.12659/MSM.925438
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author Zhao, Min
Di, Xin
Jin, Xin
Tian, Chang
Cong, Shan
Liu, Jiangyin
Wang, Ke
author_facet Zhao, Min
Di, Xin
Jin, Xin
Tian, Chang
Cong, Shan
Liu, Jiangyin
Wang, Ke
author_sort Zhao, Min
collection PubMed
description BACKGROUND: Sarcoidosis (SARC) is a multisystem inflammatory disease of unknown etiology and pulmonary tuberculosis (PTB) is caused by Mycobacterium tuberculosis. Both of these diseases affect lungs and lymph nodes and share similar clinical manifestations. However, the underlying mechanisms for the similarities and differences in genetic characteristics of SARC and PTB remain unclear. MATERIAL/METHODS: Three datasets (GSE16538, GSE20050, and GSE19314) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SARC and PTB were identified using GEO2R online analyzer and Venn diagram software. Functional enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) and R packages. Two protein–protein interaction (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes database, and module analysis was performed using Cytoscape. Hub genes were identified using area under the receiver operating characteristic curve analysis. RESULTS: We identified 228 DEGs, including 56 common SARC-PTB DEGs (enriched in interferon-gamma-mediated signaling, response to gamma radiation, and immune response) and 172 SARC-only DEGs (enriched in immune response, cellular calcium ion homeostasis, and dendritic cell chemotaxis). Potential biomarkers for SARC included CBX5, BCL11B, and GPR18. CONCLUSIONS: We identified potential biomarkers that can be used as candidates for diagnosis and/or treatment of patients with SARC.
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spelling pubmed-73977542020-08-13 Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis Zhao, Min Di, Xin Jin, Xin Tian, Chang Cong, Shan Liu, Jiangyin Wang, Ke Med Sci Monit Database Analysis BACKGROUND: Sarcoidosis (SARC) is a multisystem inflammatory disease of unknown etiology and pulmonary tuberculosis (PTB) is caused by Mycobacterium tuberculosis. Both of these diseases affect lungs and lymph nodes and share similar clinical manifestations. However, the underlying mechanisms for the similarities and differences in genetic characteristics of SARC and PTB remain unclear. MATERIAL/METHODS: Three datasets (GSE16538, GSE20050, and GSE19314) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SARC and PTB were identified using GEO2R online analyzer and Venn diagram software. Functional enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) and R packages. Two protein–protein interaction (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes database, and module analysis was performed using Cytoscape. Hub genes were identified using area under the receiver operating characteristic curve analysis. RESULTS: We identified 228 DEGs, including 56 common SARC-PTB DEGs (enriched in interferon-gamma-mediated signaling, response to gamma radiation, and immune response) and 172 SARC-only DEGs (enriched in immune response, cellular calcium ion homeostasis, and dendritic cell chemotaxis). Potential biomarkers for SARC included CBX5, BCL11B, and GPR18. CONCLUSIONS: We identified potential biomarkers that can be used as candidates for diagnosis and/or treatment of patients with SARC. International Scientific Literature, Inc. 2020-07-23 /pmc/articles/PMC7397754/ /pubmed/32701935 http://dx.doi.org/10.12659/MSM.925438 Text en © Med Sci Monit, 2020 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Database Analysis
Zhao, Min
Di, Xin
Jin, Xin
Tian, Chang
Cong, Shan
Liu, Jiangyin
Wang, Ke
Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title_full Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title_fullStr Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title_full_unstemmed Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title_short Identification of Biomarkers for Sarcoidosis and Tuberculosis of the Lung Using Systematic and Integrated Analysis
title_sort identification of biomarkers for sarcoidosis and tuberculosis of the lung using systematic and integrated analysis
topic Database Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397754/
https://www.ncbi.nlm.nih.gov/pubmed/32701935
http://dx.doi.org/10.12659/MSM.925438
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