ArticleEBioMedicine2017
Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks.
Article in EBioMedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.
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Who cites it
66 citing papers in PubMed, 3 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment.PLoS computational biology · 2023Pooled it
- Pooled it
- Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.PLoS medicine · 2019Pooled it
- Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases.Scientific reports · 2020Trial
- Blood transcriptomic signatures predict poor outcomes in drug-susceptible pulmonary tuberculosis in Brazil.American journal of respiratory and critical care medicine · 2026Article
- Network-guided transcriptomics identifies drug-linked host-response biomarkers in tuberculosis disease-state transition.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Immune endotypes in tuberculosis: Keys to decoding disease complexity.Journal of internal medicine · 2026Review
- RISK6 as a Translational Host Transcriptomic Signature for Tuberculosis Diagnosis, Treatment Monitoring, and Risk Stratification.Pathogens (Basel, Switzerland) · 2026Review
- Machine-Learning-Derived, Mechanistically Informed Transcriptomic Signature to Diagnose Active Tuberculosis and Guide Host-Directed Therapy.Diagnostics (Basel, Switzerland) · 2026Article
- From clinical phenotypes to genomic signatures: machine learning integration for precision tuberculosis treatment prediction.Frontiers in bioinformatics · 2026Article
- Functional analysis of distinct factors linked to the development of latent to active tuberculosis.Frontiers in cellular and infection microbiology · 2026Review
- Whole Blood vs Serum-Derived Exosomes for Host and Pathogen-Specific Tuberculosis Biomarker Identification: RNA-Seq-Based Machine-Learning Approach.Biochemical genetics · 2025Article
- A machine learning model and molecular clusters of epigenetic chromatin regulators in tuberculosis based on bioinformatics and clinical samples.Scientific reports · 2025Article
- Blood transcriptomic signatures predict poor treatment outcomes in drug-susceptible pulmonary TB in Brazil.medRxiv : the preprint server for health sciences · 2025Article
- International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis.iScience · 2025Article
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis.Scientific reports · 2024Article
- Blood transcriptomic signatures for symptomatic tuberculosis in an African multicohort study.The European respiratory journal · 2024Article
- Sex-specific blood-derived RNA biomarkers for childhood tuberculosis.Scientific reports · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.