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 in | Frontiers in genetics Vol. 13; p. 832739 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
07.03.2022
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Subjects | |
Online Access | Get full text |
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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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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 |