Urine biomarkers of pulmonary tuberculosis

Khimova, Elena; Gonzalo, Ximena; Yulia, Popova; Eliseev, P. I.; Mariandyshev, Andrei; Nikolayevskyy, Vladyslav; Broda, Agnieszka; Drobniewski, Francis

doi:10.1080/17476348.2022.2090341

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…(1) Criteria for TB disease: One of the test results of microbial tuberculosis in any sample type and test type is positive [31], not just the sputum specimen.…”

Section: Relevant Clinical Trial Criteriamentioning

confidence: 99%

A Retroactive Study on Factors Influencing the Efficacy of Treatment for Tuberculosis Patients with HIV: based on the data from 2010 to 2020 in Shanghai, China

Dong,

Zhang,

et al. 2023

Preprint

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At present, the factors influencing Tuberculosis (TB) treatment effectiveness in HIV/TB co-infected patients need to be supported by more substantial real-world evidence. A retrospective study is conducted to fill the vacancy. 461 TB patients with HIV are defined as 742 samples according to each TB detection period. 7788 valid treatment records corresponding to 17 drug compositions for TB and 150 clinical indicators with more than 100 records are used to conduct data mining with consensus clustering, Fisher’s exact test, stratified analysis, and three modeling approaches, including logistic regression, support vector machine, and random forest. We find that A CD4+T cell count of 42 cells per μL may serve as a sensitive classification standard for the immune level to assist in evaluating or predicting the efficacy of TB (P=0.007); Rifabutin and levofloxacin alone or in combination may be more effective than other first- and second-line anti-TB agents in combination (P=0.037); Samples with low immune levels (CD4≦42) may be more resistant to first-line TB drugs (P=0.049); Age (P=0.015), bicarbonate radical (P=0.007), high-density lipoprotein cholesterol (P=0.026), pre-treatment CD8+T cell count (P=0.015, age<60, male), neutrophil percentage (P=0.033, age<60), rifabutin (P=0.010, age<60), and cycloserine (P=0.027, age<60) may influence the TB treatment effectiveness; More evidence is needed to support the relationship between pre-treatment clinical indicators or drug regimens and TB treatment effectiveness (The best AUC is 0.560∼0.763); The percentage of lymphocytes (P=0.028) can be used as an effective TB therapeutic target. These perspectives supplement knowledge in relevant clinical aspects.

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“…(1) Criteria for TB disease: One of the test results of microbial tuberculosis in any sample type and test type is positive [31], not just the sputum specimen.…”

Section: Relevant Clinical Trial Criteriamentioning

confidence: 99%

A Retroactive Study on Factors Influencing the Efficacy of Treatment for Tuberculosis Patients with HIV: based on the data from 2010 to 2020 in Shanghai, China

Dong,

Zhang,

et al. 2023

Preprint

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“…Therefore, simple, specific, sensitive, non-sputum-based, rapid, and precise diagnostic methods for active TB Genes 2024, 15, 434 2 of 18 need to be developed to improve diagnostic efficiency and reduce transmission rates [5]. In the last decade, biomarkers derived from blood, saliva, urine, etc., have been studied intensively for the development of novel diagnostic methods for PTB [6,7]. Blood-based biomarker diagnostics have the advantages of quick sample collection, quantification, and point-of-care (POC) tests and are used for predicting active TB infections and determining the progression of Mtb infections in individuals who are at risk of developing active TB [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis

Arya,

Shakya,

Chaurasia

et al. 2024

Genes

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Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein–protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC—0.993, sensitivity—93.8%, specificity—100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

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“…It is therefore urgently necessary to study and development non-sputum-based, simple, sensitive, and specific tests for diagnosing PTB. The biomarkers of PTB have been increasingly explored in the last three years owing to several studies on the identification of novel diagnostic biomarkers and development of novel diagnostic methods for PTB ( Khambati et al, 2021 ; Morrison and Mcshane, 2021 ; Khimova et al, 2022 ). These studies have paved the way for the diagnosis and identification of novel biomarkers of PTB.…”

Section: Introductionmentioning

confidence: 99%

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Zhu

Liu

2023

Front. Genet.

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Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

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Urine biomarkers of pulmonary tuberculosis

Cited by 5 publications

References 35 publications

A Retroactive Study on Factors Influencing the Efficacy of Treatment for Tuberculosis Patients with HIV: based on the data from 2010 to 2020 in Shanghai, China

A Retroactive Study on Factors Influencing the Efficacy of Treatment for Tuberculosis Patients with HIV: based on the data from 2010 to 2020 in Shanghai, China

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

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