ArticleBMC infectious diseases2026
Identification of discriminative plasma protein biomarkers for recent Mycobacterium tuberculosis infection: a pilot data‑independent acquisition mass spectrometry‑based proteomics study.
Article in BMC infectious diseases, 2026. 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.
- Host-response biomarkers for precision tuberculosis: from immune transcriptomic signatures to clinical risk stratification.Frontiers in cell and developmental biology · 2026Review
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8 authors.
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Abstract
backgroundRecently acquired Mycobacterium tuberculosis (M.tb) infection is associated with a higher risk of progression to active tuberculosis (TB), yet current diagnostic tools cannot distinguish recent from remote latent TB infection (LTBI).
methodsPlasma samples from 26 individuals with recently acquired LTBI, 26 with remotely acquired LTBI and 26 bacteriologically confirmed TB patients, were analyzed using a data-independent acquisition mass spectrometry (DIA-MS)-based proteomics approach. Differentially expressed proteins (DEPs) and enriched biological pathways associated with infection statuses were identified.
resultsPrincipal component analysis revealed that individuals with recently acquired LTBI and those with active TB exhibited comparable proteomic profiles, while the group with remotely acquired LTBI demonstrated significantly distinct expression patterns. Differential abundance analysis revealed 470 DEPs between the remotely and recently acquired LTBI groups, and 171 between the recent LTBI and active TB groups. These dysregulated proteins were primarily enriched in pathways related to complement and coagulation cascades, cytoskeletal remodeling, and cell adhesion. By integrating hub proteins from both DEPs and WGCNA modules, and utilizing LASSO-based feature selection, a five-protein panel was identified, comprising ACTR3, ITGB2, RELN, TUBB1, and ITGB5. This panel demonstrated a robust capacity to differentiate between infection statuses, with AUC values exceeding 0.90 across all pairwise group comparisons. Furthermore, the identified signature also displayed significant differential expression across four independent transcriptomic datasets.
conclusionThese findings highlight the potential utility of plasma protein signatures in identifying individuals with recently acquired LTBI, providing a rationale for risk stratification to improve targeted LTBI interventions. CLINICAL TRIAL NUMBER: Not applicable.
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