ArticleFrontiers in microbiology2022
Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Construction of a diagnostic model for tuberculosis based on long non-coding RNA.Annals of medicine · 2026Article
- Long non-coding RNAs in tuberculosis: Immune regulation and clinical biomarker potential.iScience · 2026Review
- Long non-coding RNA transcripts inNon-coding RNA research · 2025Review
- Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.Frontiers in cellular and infection microbiology · 2025Review
- Identification of a circulating long non-coding RNA signature panel in plasma as a novel biomarker for the detection of acute/early-stage HIV-1 infection.Biomarker research · 2024Article
- Integrating pathogen- and host-derived blood biomarkers for enhanced tuberculosis diagnosis: a comprehensive review.Frontiers in immunology · 2024Review
- Advances of Long Non-Coding RNAs as Potential Biomarkers for Tuberculosis: New Hope for Diagnosis?Pharmaceutics · 2023Review
- Immune regulation and emerging roles of noncoding RNAs inFrontiers in immunology · 2022Review
- Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model. Methods: A total of 2,759 subjects were included in this study, including 12 in the primary screening cohort [7 TB patients and 5 healthy controls (HCs)] and 2,747 in the selection cohort (798 TB patients, 299 patients with non-TB lung disease, and 1,650 HCs). An Affymetrix HTA2.0 array and qRT-PCR were applied to screen new specific lncRNA markers for TB in individual nucleated cells from host peripheral blood. A ML algorithm was established to combine the patients' EHR information and lncRNA data Results: Two differentially expressed lncRNAs (TCONS_00001838 and n406498) were identified ( Conclusion: LncRNAs TCONS_00001838 and n406498 have the potential to become new molecular markers for PTB, and the nomogram of "LncRNA + EHR" model is expected to be effective for the early clinical diagnosis of TB.
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Registered trials
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