IRF1 as a potential biomarker in Mycobacterium tuberculosis infection
Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491...
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Published in | Journal of cellular and molecular medicine Vol. 25; no. 15; pp. 7270 - 7279 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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England
John Wiley & Sons, Inc
01.08.2021
John Wiley and Sons Inc |
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Abstract | Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell‐cell adhesion and regulation of T‐cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine‐cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein‐protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up‐regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up‐regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis. |
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AbstractList | Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets,
GSE139825
is lung tissue microarray, and
GSE83456
,
GSE19491
and
GSE50834
are blood microarray. The differential genes of
GSE139825
and
GSE83456
were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell‐cell adhesion and regulation of T‐cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine‐cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein‐protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up‐regulated in patients with TB relative to healthy controls in data sets
GSE19491
and
GSE50834
. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up‐regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis. Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell‐cell adhesion and regulation of T‐cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine‐cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein‐protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up‐regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up‐regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis. |
Author | Wang, Lin Song, Yanzheng Wu, Liwei Wen, Zilu Zhu, Yijun Cheng, Qiliang |
AuthorAffiliation | 3 Department of Scientific Research Shanghai Public Health Clinical Center Fudan University Shanghai China 4 TB Center Shanghai Emerging & Re‐emerging Infectious Diseases Institute Shanghai China 2 Department of Thoracic Surgery Tuberculosis Hospital of Shaanxi Province Xi’an China 1 Department of Thoracic Surgery Shanghai Public Health Clinical Center Fudan University Shanghai China |
AuthorAffiliation_xml | – name: 3 Department of Scientific Research Shanghai Public Health Clinical Center Fudan University Shanghai China – name: 1 Department of Thoracic Surgery Shanghai Public Health Clinical Center Fudan University Shanghai China – name: 2 Department of Thoracic Surgery Tuberculosis Hospital of Shaanxi Province Xi’an China – name: 4 TB Center Shanghai Emerging & Re‐emerging Infectious Diseases Institute Shanghai China |
Author_xml | – sequence: 1 givenname: Liwei orcidid: 0000-0002-3150-2260 surname: Wu fullname: Wu, Liwei organization: Fudan University – sequence: 2 givenname: Qiliang surname: Cheng fullname: Cheng, Qiliang organization: Tuberculosis Hospital of Shaanxi Province – sequence: 3 givenname: Zilu surname: Wen fullname: Wen, Zilu organization: Fudan University – sequence: 4 givenname: Yanzheng surname: Song fullname: Song, Yanzheng email: yanzhengsong@163.com organization: Shanghai Emerging & Re‐emerging Infectious Diseases Institute – sequence: 5 givenname: Yijun surname: Zhu fullname: Zhu, Yijun email: zhuyijun@shphc.org.cn organization: Fudan University – sequence: 6 givenname: Lin surname: Wang fullname: Wang, Lin email: wlxxs2011@163.com organization: Fudan University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34213077$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1186_s13568_024_01691_7 crossref_primary_10_1021_acsinfecdis_4c00374 crossref_primary_10_3389_fcimb_2023_1255905 crossref_primary_10_3390_ijms25116255 |
Cites_doi | 10.7883/yoken.JJID.2014.466 10.1128/AAC.01461-17 10.1038/s42003-020-0941-1 10.1002/jcb.28153 10.1038/s41467-019-09234-6 10.1371/journal.pone.0237574 10.1371/journal.pmed.1002786 10.1080/15548627.2015.1084457 10.1093/nar/gku1003 10.3389/fimmu.2020.00630 10.1101/cshperspect.a021147 10.1093/intimm/dxz048 10.1093/nar/gkv007 10.1093/nar/gkw1092 10.1016/S1470-2045(14)71116-7 10.1074/jbc.RA118.002693 10.1093/nar/gks1193 10.1111/cei.12873 10.1186/1752-0509-8-S4-S11 10.1016/j.ebiom.2016.12.009 10.1172/JCI96148 10.1093/bioinformatics/btq675 |
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Copyright | 2021 The Authors. published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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Keywords | differentially expressed gene (DEG) network analysis protein interaction biomarker tuberculosis (TB) protein |
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License | Attribution 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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Notes | Funding information This research was supported by a grant from the Thirteen‐Fifth Mega‐Scientific Project on “prevention and treatment of AIDS, viral hepatitis and other infectious diseases” (grant no. 2017ZX10201301‐003‐002) Liwei Wu, Qiliang Chen and Zilu Wen are co‐first authors ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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References | 2015; 68 2018; 120 2017; 61 2015; 5 2015; 16 2020; 3 2018; 293 2019; 31 2017; 15 2018; 128 2019; 10 2013; 41 2015; 43 2017; 45 2019; 16 2020; 15 2020; 11 2017; 187 2011; 27 2014; 8 2016; 12 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_24_1 e_1_2_8_14_1 e_1_2_8_15_1 e_1_2_8_16_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_10_1 e_1_2_8_21_1 e_1_2_8_11_1 e_1_2_8_22_1 e_1_2_8_12_1 e_1_2_8_23_1 |
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Snippet | Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose... |
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SubjectTerms | biomarker Biomarkers Biomarkers - metabolism Cell activation Cell adhesion Cell adhesion & migration Cytokines Cytokines - metabolism differentially expressed gene (DEG) DNA microarrays Folic acid Gene expression Gene Regulatory Networks Genomes Health care HIV Human immunodeficiency virus Humans Infections Interferon regulatory factor 1 Interferon Regulatory Factor-1 - genetics Interferon Regulatory Factor-1 - metabolism network analysis Online data bases Open source software Original protein protein interaction Protein Interaction Maps Proteins Public health Statistical analysis Transcription activation Transcriptome Tuberculosis tuberculosis (TB) Tuberculosis, Pulmonary - genetics Tuberculosis, Pulmonary - metabolism Tuberculosis, Pulmonary - pathology Up-Regulation |
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Title | IRF1 as a potential biomarker in Mycobacterium tuberculosis infection |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fjcmm.16756 https://www.ncbi.nlm.nih.gov/pubmed/34213077 https://www.proquest.com/docview/2557758228 https://search.proquest.com/docview/2548416346 https://pubmed.ncbi.nlm.nih.gov/PMC8335664 |
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