ArticleEmerging microbes & infections2024
Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis.
Article in Emerging microbes & infections, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Emerging insights into CC and CXC chemokines and their receptors in Mycobacterium tuberculosis infection.FEBS open bio · 2026Review
- Plasma protein signatures altered before and after tuberculosis diagnosis in a population-based cohort.Clinical proteomics · 2026Article
- Prediction of Tuberculosis Risk in the Elderly Population of Eastern China: Development and Validation of Multiple Machine Learning Models.Infection and drug resistance · 2026Article
- Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.World journal of urology · 2025Article
- International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis.iScience · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Plasma proteomics for biomarker discovery in childhood tuberculosis.Nature communications · 2025Article
- Identification of inflammatory protein biomarkers for predicting the different subtype of adult with tuberculosis: an Olink proteomic study.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS.Frontiers in cellular and infection microbiology · 2025Article
- Multimodal machine learning-based model for differentiating nontuberculous mycobacteria fromFrontiers in public health · 2025Article
- Host Circulating Immunometabolism-Associated Biomarkers for Early Diagnosis of Active Tuberculosis: Multi-Omics Screening with Experimental Validation.Journal of inflammation research · 2025Article
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Authors and funding
11 authors.
Funding
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Abstract
Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.
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