An integrative and comprehensive analysis of blood transcriptomes combined with machine learning models reveals key signatures for tuberculosis diagnosis and risk stratification

Tuberculosis (TB) remains a major global health challenge, contributing substantially to morbidity and mortality worldwide. The progression from Mycobacterium tuberculosis (Mtb) infection to active disease involves a complex interplay between host immune responses and Mtb’s ability to evade them. Ho...

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Published in:Frontiers in microbiology Vol. 16; p. 1546770
Main Authors: Omrani, Maryam, Ghodousi, Arash, Cirillo, Daniela Maria
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 26.05.2025
Subjects:
biomarkers
blood
machine-learning
Microbiology
Mycobacterium tuberculosis
RNA-seq
machine-learning
Mycobacterium tuberculosis
RNA-seq
blood
biomarkers
ISSN:1664-302X, 1664-302X
Online Access:Get full text
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Summary:Tuberculosis (TB) remains a major global health challenge, contributing substantially to morbidity and mortality worldwide. The progression from Mycobacterium tuberculosis (Mtb) infection to active disease involves a complex interplay between host immune responses and Mtb’s ability to evade them. However, current diagnostic tools, such as interferon-gamma release assays (IGRAs) and tuberculin skin tests (TSTs), have limited ability to distinguish between different stages of TB or to predict the progression from infection to active disease. In this study, we performed an integrative analysis of 324 previously acquired blood transcriptome samples from TB patients, TB contacts, and controls across diverse geographical regions. Differential gene expression analysis revealed distinct transcriptomic signatures in TB patients, highlighting dysregulated pathways related to immune responses, antimicrobial peptides, and extracellular matrix organization. Using machine learning, we identified a 99-transcript signature that accurately distinguished TB patients from controls, demonstrated strong predictive performance across different cohorts, and identified potential progressors or subclinical cases. Validation in an independent dataset comprising 90 TB patients and 20 healthy controls confirmed the robustness of the 10-gene signature (BATF2, FAM20A, FBLN2, AK5, VAMP5, MMP8, KLHDC8B, LINC00402, DEFA3, and GBP6), achieving high area under the curve (AUC) values in both receiver operating characteristic (ROC) and precision–recall analyses. This 10-gene signature offers promising candidates for further validation and the development of diagnostic and prognostic tools, supporting global efforts to improve TB detection and risk stratification.
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Nevim Aygun, Ege University, Türkiye
Edited by: Maria Laura Gennaro, Rutgers University, Newark, United States
Reviewed by: Ana Varela Coelho, Universidade Nova de Lisboa, Portugal
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2025.1546770