ArticleTuberculosis (Edinburgh, Scotland)2025
Analysis of the cross-study replicability of tuberculosis gene signatures using 49 curated human transcriptomic datasets.
Article in Tuberculosis (Edinburgh, Scotland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Bioinformatics and experimental analysis identify CHN2 and MEF2C as diagnostic biomarkers for tuberculosis.American journal of translational research · 2026Article
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5 authors.
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Abstract
backgroundTuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Numerous host blood-based gene expression signatures have been proposed in the literature as alternative tools for diagnosing TB infection. However, the generalizability of these signatures to different patient contexts is not well-characterized. There is a pressing need for a well-curated database of TB gene expression studies for the systematic assessment of existing and newly developed TB gene signatures.
resultsWe built curatedTBData, a manually-curated database of 49 human TB transcriptomic studies. This data resource is freely available through GitHub and as an R Bioconductor package that allows users to validate new and existing biomarkers without the challenges of harmonizing heterogeneous studies. We demonstrate the use of this data resource with cross-study comparisons for 72 human host blood-based TB gene signatures. For the comparison of subjects with active TB from healthy controls, 19 gene signatures had weighted mean AUC of 0.90 or greater, with the highest result of 0.94. In active TB disease versus latent TB infection, 7 gene signatures had weighted mean AUC of 0.90 or greater, with a maximum of 0.93.
conclusionsThe curatedTBData data package offers a comprehensive resource of curated human blood-based gene expression and clinically annotated data. This resource will facilitate the development of new signatures that are generalizable across cohorts or more applicable to specific subsets of patients.
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