ArticleInfection and drug resistance2019
The blood transcriptional signature for active and latent tuberculosis.
Article in Infection and drug resistance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- 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
- Mycobacterium tuberculosis inhibits METTL14-mediated mCell discovery · 2024Article
- Management of Tuberculosis Infection: Current Situation, Recent Developments and Operational Challenges.Pathogens (Basel, Switzerland) · 2023Review
- Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysis.Respiratory research · 2022Article
- Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis.Scientific reports · 2021Article
- A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks.PLoS computational biology · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough the incidence of tuberculosis (TB) has dropped substantially, it still is a serious threat to human health. And in recent years, the emergence of resistant bacilli and inadequate disease control and prevention has led to a significant rise in the global TB epidemic. It is known that the cause of TB is
methodsWe analyzed the blood gene expression profiles of 69 latent TB patients and 54 active pulmonary TB patients from GEO (Transcript Expression Omnibus) database.
resultsBy applying minimal redundancy maximal relevance and incremental feature selection, we identified 24 signature genes which can predict the TB activation. The support vector machine predictor based on these 24 genes had a sensitivity of 0.907, specificity of 0.913, and accuracy of 0.911, respectively. Although they need to be validated in a large independent dataset, the biological analysis of these 24 genes showed great promise.
conclusionWe found that cytokine production was a key process during TB activation and genes like CYBB, TSPO, CD36, and STAT1 worth further investigation.
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