Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers

The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers. GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein...

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Published inFrontiers in genetics Vol. 13; p. 832739
Main Authors Wen, Zilu, Wu, Liwei, Wang, Lin, Ou, Qinfang, Ma, Hui, Wu, Qihang, Zhang, Shulin, Song, Yanzheng
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 07.03.2022
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Summary:The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers. GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein network analysis and hub genes analysis. Based on our previous study, the intersect between WGCNA and differential gene analysis could be used as candidate biomarkers. Then, the enzyme-linked immunosorbent assay was used to validate candidate biomarkers, and receiver operating characteristic was used to assess diagnose ability of candidate biomarkers. A total of 412 differential genes were screened. And we obtained 105 overlapping genes between DEGs and WGCNA. GO and KEGG analysis showed that most of the differential genes were significantly enriched in innate immunity. A total of 15 hub genes were screened, and four of them were verified by Enzyme-linked immunosorbent assay. CCL5 performed well in distinguishing the healthy group from the TB group (AUC = 0.723). And CCL19 performed well in distinguishing the TB group from the ORD groups (AUC = 0.811). CCL19, C1Qb, CCL5 and HLA-DMB may play important role in tuberculosis, which indicated four genes may become effective biomarkers and could be conveniently used to facilitate the individual tuberculosis diagnosis in Chinese people.
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This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Xin Zhou, University of North Carolina at Chapel Hill, United States
Reviewed by: Weinan Zhou, University of Illinois at Urbana-Champaign, United States
These authors have contributed equally to this work
Edited by: Sheng Liu, Indiana University School of Medicine, United States
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2022.832739