ArticleJournal of clinical microbiology2017
Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis.
Article in Journal of clinical microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- Host blood protein biomarkers to screen for tuberculosis disease: a systematic review and meta-analysis.Journal of clinical microbiology · 2024Pooled it
- MPT64 antigen-induced immune responses as a novel diagnostic tool for tuberculosis.Microbiology spectrum · 2026Article
- Pulmonary Tuberculosis: The Great Mimicker of Lung Cancer.Clinical case reports · 2026Article
- Recent Characterization of Key Proteins to Combat Tuberculosis.Advances in experimental medicine and biology · 2026Review
- Rapid Molecular Diagnostics of Tuberculosis: What Do We Have, What Do We Need?Pulmonary medicine · 2026Review
- Proteomic analysis of plasma unravels dynamic pathways and potential biomarkers indicating disease stages followingmSystems · 2025Article
- Plasma proteomics for biomarker discovery in childhood tuberculosis.Nature communications · 2025Article
- Diagnostic accuracy of point-of-care triage tests for pulmonary tuberculosis using host blood protein biomarkers: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Biomarkers in diagnosing and therapeutic monitoring of tuberculosis: a review.Annals of medicine · 2024Review
- Candidate serum protein biomarkers for active pulmonary tuberculosis diagnosis in tuberculosis endemic settings.BMC infectious diseases · 2024Article
- Single and Combined Serum Proteins Expressed in TB Infection are Candidates for Point-of-care Diagnostic Testing of Active TB Patients in Lambaréné, Gabon.Open forum infectious diseases · 2024Article
- Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.Scientific reports · 2024Article
- Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers.JCI insight · 2024Article
- Diagnostic performance of host protein signatures as a triage test for active pulmonary TB.Journal of clinical microbiology · 2023Article
- The application of Aptamer in biomarker discovery.Biomarker research · 2023Review
- Article
- A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis.iScience · 2022Article
- Diagnostic biomarkers for active tuberculosis: progress and challenges.EMBO molecular medicine · 2022Review
- Species-specific transcriptomic changes upon respiratory syncytial virus infection in cotton rats.Scientific reports · 2022Article
- Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (
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