Dataset Information

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis.

ABSTRACT:

Methods

Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA).

Results

In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity.

Conclusion

The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.

SUBMITTER: Chen L

PROVIDER: S-EPMC9674975 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis.

Chen Liang L Hua Jie J He Xiaopu X

Disease markers 20221111

<h4>Methods</h4>Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test datase ...[more]

PMID: 36411825

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Dataset Information

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis.

Methods

Results

Conclusion

Publications

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis.

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