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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.
Afiliação
  • Long NP; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
  • Phat NK; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
  • Yen NTH; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
  • Park S; Department of Statistics, Sungshin Women's University, Seoul, 02844, Republic of Korea.
  • Park Y; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea.
  • Cho YS; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Department of Clinical Pharmacology, Inje Univers
  • Shin JG; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Department of Clinical Pharmacology, Inje Univers
Tuberculosis (Edinb) ; 131: 102138, 2021 12.
Article em En | MEDLINE | ID: mdl-34801869
The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article