ArticleScientific reports2024
Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.Microbiology spectrum · 2026Article
- Immune Dysregulation in HIV-TB Co-Infection: Role of Cytokines and T Cell Biomarkers-A Narrative Review.Pathogens (Basel, Switzerland) · 2026Review
- Article
- ANKRD22 contributes to NF-κB-associated inflammatory responses in alveolar type II epithelial cells during PMFrontiers in immunology · 2026Article
- Alterations in the Immune Response in Individuals with Latent Tuberculosis Infection.Pathogens (Basel, Switzerland) · 2025Review
- Article
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6 authors.
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Abstract
backgroundDistinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB.
methodsThirteen datasets were downloaded from the gene expression omnibus (GEO) database. Three datasets were selected as discovery datasets, and the hub genes were discovered through WGCNA. In the training cohort, we use machine learning to establish the signature, verify the authentication ability of the signature in the remaining datasets, and compare it with other signatures. Cluster analysis was carried out on ATB cases, immune cell infiltration analysis, GSVA analysis, and drug sensitivity analysis were carried out on different clusters.
resultsIn the discovery datasets, we discovered five hub genes. A signature (SLC26A8, ANKRD22, and FCGR1B) is obtained in the training cohort. In the total cohort, the three-gene signature can separate LTBI from ATB (the total area under ROC curve (AUC) is 0.801, 95% CI 0.771-0.830). Compared with other author's signatures, our signature shows good identification ability. Immunological analysis showed that SLC26A8, ANKRD22, and FCGR1B were closely related to the infiltration of immune cells. According to the expression of the three genes, ATB can be divided into two clusters, which are different in immune cell infiltration analysis, gene set variation, and drug sensitivity.
conclusionOur study produced an immune-related three-gene signature to distinguish LTBI from ATB, which may help us to manage and treat tuberculosis patients.
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