ArticleFrontiers in cellular and infection microbiology2024
Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.
Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.Microbiology spectrum · 2026Article
- CDK1‑induced regulation of p53 phosphorylation at Ser315 mediates cell cycle arrest and apoptosis of macrophages infected with clinical isolates ofMolecular medicine reports · 2026Article
- A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.BMC medical informatics and decision making · 2024Article
- Glycogen synthase kinase 3 inhibition controlsiScience · 2024Article
- Identification of Rv1133c (MetE) as a marker ofFrontiers in immunology · 2024Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Apoptosis is associated with the pathogenesis of Methods: The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated. Results: Six biomarkers including Conclusion: The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of
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