ArticleBMC bioinformatics2016
Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.PLoS computational biology · 2022Pooled it
- Pooled it
- Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases.Scientific reports · 2020Trial
- Activation of the Nuclear Factor Kappa-light-chain-enhancer of Activated B-cells Pathway Induces a Pro-inflammatory Network in Patients with Pulmonary Tuberculosis.Annals of African medicine · 2026Article
- Immune-Inflammatory Hub Genes Intersecting with a Ferroptosis-Associated Gene Set in Active Tuberculosis: A Multi-Dataset Bioinformatics Study.International journal of molecular sciences · 2026Article
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Machine-Learning-Derived, Mechanistically Informed Transcriptomic Signature to Diagnose Active Tuberculosis and Guide Host-Directed Therapy.Diagnostics (Basel, Switzerland) · 2026Article
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis.iScience · 2025Article
- The Evolving Landscape of Host Biomarkers for Diagnosis and Monitoring of Tuberculosis.Biomedicines · 2025Review
- Long non-coding RNA transcripts inNon-coding RNA research · 2025Review
- Identification and assessment of hub genes and miRNAs coregulatory associated with immune infiltrations and drug interactions in latent tuberculosis based on MicroarrayData analysis, molecular docking, and dynamic simulation.Biochemistry and biophysics reports · 2025Article
- Identification and validation of NETs-related biomarkers in active tuberculosis through bioinformatics analysis and machine learning algorithms.Frontiers in immunology · 2025Article
- Tuberculosis diagnosis using artificial intelligence: current trends and future prospects.Frontiers in medicine · 2025Review
- Harnessing the host response for precision infectious disease diagnosis.Clinical microbiology reviews · 2024Review
- Identification ofAnnals of medicine · 2024Article
- Decoding differentially expressed genes to identify potential immunity associated biomarkers in Tuberculosis: An integrative bioinformatics approach.Biochemistry and biophysics reports · 2024Article
- Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis.Scientific reports · 2024Article
- Suppression of host gene expression is associated with latent TB infection: a possible diagnostic biomarker.Scientific reports · 2024Article
- Transcriptomics-based anti-tuberculous mechanism of traditional Chinese polyherbal preparation NiuBeiXiaoHe intermediates.Frontiers in pharmacology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population.
resultsBioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance.
conclusionsOur study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.