Differential expression analysis of miRNAs in macrophage-derived exosomes in the tuberculosis-infected bone microenvironment

Sun, Zhicheng; Pang, Xiaoyang; Wang, Xiyang; Zeng, Hao

doi:10.3389/fmicb.2023.1236012

Cited by 5 publications

(2 citation statements)

References 35 publications

(32 reference statements)

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“…SCI results from the progression of STB. Given the low early detection rate and poor prognosis of STB patients, there is a growing focus on identifying biomarkers for the occurrence and development of this disease 16 , 17 , 31 , 32 . Several clinical indicators related to the progression and prognosis of STB have been investigated.…”

Section: Discussionmentioning

confidence: 99%

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Feng,

Wang,

Liu

et al. 2024

Sci Rep

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Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

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Section: Discussionmentioning

confidence: 99%

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Feng,

Wang,

Liu

et al. 2024

Sci Rep

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“…Hence, the imperative lies in the identification of TB infection biomarkers to facilitate the diagnosis of active TB. Among the assessed TB biomarkers, microRNA (miRNA) is one such type under evaluation ( 2 ). miRNAs have been proposed as a novel biological diagnostic indicator in various settings encompassing diabetes, heart disease, infectious diseases, pregnancy, cancer, and psoriasis.…”

Section: Introductionmentioning

confidence: 99%

miR-29 as diagnostic biomarkers for tuberculosis: a systematic review and meta-analysis

He,

Xiong,

Huang

2024

Front. Public Health

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BackgroundThe timely diagnosis of tuberculosis through innovative biomarkers that do not rely on sputum samples is a primary focus for strategies aimed at eradicating tuberculosis. miR-29 is an important regulator of tuberculosis pathogenesis. Its differential expression pattern in healthy, latent, and active people who develop tuberculosis has revealed its potential as a biomarker in recent studies. Therefore, a systematic review and meta-analysis were performed for the role of miR-29 in the diagnosis of tuberculosis.MethodsEMBASE, PubMed, CNKI, Web of Science, and Cochrane Library databases were searched utilizing predefined keywords for literature published from 2000 to February 2024.Included in the analysis were studies reporting on the accuracy of miR-29 in the diagnosis of tuberculosis, while articles assessing other small RNAs were not considered. All types of study designs, including case–control, cross-sectional, and cohort studies, were included, whether prospectively or retrospectively sampled, and the quality of included studies was determined utilizing the QUADAS-2 tool. Publication bias was analyzed via the construction of funnel plots. Heterogeneity among studies and summary results for specificity, sensitivity, and diagnostic odds ratio (DOR) are depicted in forest plots.ResultsA total of 227 studies were acquired from the various databases, and 18 articles were selected for quantitative analysis. These articles encompassed a total of 2,825 subjects, primarily sourced from the Asian region. Patient specimens, including sputum, peripheral blood mononuclear cells, cerebrospinal fluid and serum/plasma samples, were collected upon admission and during hospitalization for tuberculosis testing. miR-29a had an overall sensitivity of 82% (95% CI 77, 85%) and an overall specificity of 82% (95% CI 78, 86%) for detecting tuberculosis. DOR was 21 (95% CI 16–28), and the area under the curve was 0.89 (95% CI 0.86, 0.91). miR-29a had slightly different diagnostic efficacy in different specimens. miR-29a showed good performance in both the diagnosis of pulmonary tuberculosis and extrapulmonary tuberculosis. miR-29b and miR-29c also had a good performance in diagnosis of tuberculosis.ConclusionAs can be seen from the diagnostic performance of miR-29, miR-29 can be used as a potential biomarker for the rapid detection of tuberculosis.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=461107.

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Proteome overview of exosome derived from plasma of cows infected with Mycobacterium bovis

Zhou,

Wu,

Zhang

et al. 2024

Tuberculosis

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Differential expression analysis of miRNAs in macrophage-derived exosomes in the tuberculosis-infected bone microenvironment

Cited by 5 publications

References 35 publications

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

miR-29 as diagnostic biomarkers for tuberculosis: a systematic review and meta-analysis

Proteome overview of exosome derived from plasma of cows infected with Mycobacterium bovis

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