A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction

Long, Nguyen Phuoc; Phat, Nguyen Ky; Yen, Nguyen Thi Hai; Park, Seongoh; Park, Yumi; Cho, Yong‐Soon; Shin, Jae‐Gook

doi:10.1016/j.tube.2021.102138

Cited by 13 publications

(15 citation statements)

References 51 publications

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“…It has been found that Mycobacterium tuberculosis can promote the expansion of regulatory T cells by inducing CD274 gene in dendritic cells ( 24 ). In addition, it has been shown that 10 biomarkers, including CD274 and EGF, can be used for the diagnosis of tuberculosis, the differential diagnosis of latent tuberculosis infection/active tuberculosis, and the risk of progression to active tuberculosis ( 25 ). PRKCZ is a member of the PKC family, and the gene level of the PKC family can be used as a predictor of response to immunotherapy ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

“…PRKCZ is a member of the PKC family, and the gene level of the PKC family can be used as a predictor of response to immunotherapy ( 26 ). The above analysis indicates that these six genes (AKT1, TP53, EGF, ARF1, CD274 and PKCZ) are all related to the immune system of the host and involved in the pathogenesis of tuberculosis, EGF and CD274 have been used as biomarkers for the diagnosis of tuberculosis ( 21 , 25 ). Therefore, we suggest that EGF and CD274 are potential drug targets and diagnostic targets for TB, and AKT1, TP53, ARF1 and PRKCZ may be new potential drug targets and diagnostic targets for TB.…”

Section: Discussionmentioning

confidence: 99%

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Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network

Yang

Sheng

et al. 2023

Front. Immunol.

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PurposeTuberculosis is common infectious diseases, characterized by infectivity, concealment and chronicity, and the early diagnosis is helpful to block the spread of tuberculosis and reduce the resistance of Mycobacterium tuberculosis to anti-tuberculosis drugs. At present, there are obvious limitations in the application of clinical detection methods used for the early diagnosis of tuberculosis. RNA sequencing (RNA-Seq) has become an economical and accurate gene sequencing method for quantifying transcripts and detecting unknown RNA species.MethodsA peripheral blood mRNA sequencing was used to screen the differentially expressed genes between healthy people and tuberculosis patients. A protein-protein interaction (PPI) network of differentially expressed genes was constructed through Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The potential diagnostic targets of tuberculosis were screened by the calculation of degree, betweenness and closeness in Cytoscape 3.9.1 software. Finally, the functional pathways and the molecular mechanism of tuberculosis were clarified in combination of the prediction results of key gene miRNAs, and by Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia Genes and Genomes (KEGG) pathway annotation analysis.Results556 Differential genes of tuberculosis were screened out by mRNA sequencing. Six key genes (AKT1, TP53, EGF, ARF1, CD274 and PRKCZ) were screened as the potential diagnostic targets for tuberculosis by analyzing the PPI regulatory network and using three algorithms. Three pathways related to the pathogenesis of tuberculosis were identified by KEGG pathway analysis, and two key miRNAs (has-miR-150-5p and has-miR-25-3p) that might participate in the pathogenesis of tuberculosis were screened out by constructing a miRNA-mRNA pathway regulatory network.ConclusionSix key genes and two important miRNAs that could regulate them were screened out by mRNA sequencing. The 6 key genes and 2 important miRNAs may participate in the pathogenesis of infection and invasion of Mycobacterium tuberculosis through herpes simplex virus 1 infection, endocytosis and B cell receptor signaling pathways.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network

Yang

Sheng

et al. 2023

Front. Immunol.

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“…We previously developed the Long10 signature comprising 10 genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) that were consistently downregulated during TB treatment. The signature displayed comparable performance to other signatures for TB diagnosis, treatment monitoring, and risk assessment (16). The satisfactory performance of the RISK6 and Long10 signatures suggests that a combination of transcriptomic biosignatures can be useful for multiple aspects of TB management.…”

Section: Introductionmentioning

confidence: 92%

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Phat,

Tien,

Anh

et al. 2023

Front. Immunol.

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BackgroundThe optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear. MethodologyIn the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response. Results and discussionThe expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkersConclusionsLipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

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“…We have shown whole blood gene signatures as a correlate of immune status responding to Mtb bacterial load during TB progression. Accumulated evidence has also demonstrated the utility of gene signatures in treatment monitoring along with Mtb elimination (3,16,(30)(31)(32)(33)(34)(35). Here we interrogated whether our gene signature model could be used to infer treatment responses as well as be predictive of clinical outcomes (cure, failure, and recurrence).…”

Section: Validation Of the Gene Signature Models In Treatment Monitor...mentioning

confidence: 99%

“…Several whole blood diagnostic gene signatures based on host immune responses have demonstrated the ability to accurately distinguish between ATB and healthy controls (HC), as well as ATB and disease manifestations such as other pulmonary diseases (OD) or LTBI (3,(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Further, some studies have also demonstrated the use of gene signatures in predicting progression from LTBI to ATB (3,5,11,13,(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and in monitoring treatment response (3,8,16,(30)(31)(32)(33)(34)(35). For TB gene signature discovery, most studies utilized a conventional approach beginning with a single cohort with differential gene expression analysis between ATB and different clinical conditions (LTBI, OD or HC), followed by multivariate modeling to define the gene signature.…”

Section: Introductionmentioning

confidence: 99%

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

Vargas

Abbott

Frahm

et al. 2022

Preprint

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While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximated the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

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A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction

Cited by 13 publications

References 51 publications

Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network

Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

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