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一种用于监测结核病治疗和预测治疗结局的 10 基因生物标志物。

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

机构信息

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 Statistics, Sungshin Women's University, Seoul, 02844, Republic of Korea.

出版信息

Tuberculosis (Edinb). 2021 Dec;131:102138. doi: 10.1016/j.tube.2021.102138. Epub 2021 Oct 8.

Abstract

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.

摘要

血液转录组生物标志物在结核病(TB)治疗监测和预后预测中的临床应用仍然有限。本研究旨在基于治疗过程中基因表达的动态反应,发现和验证用于肺结核治疗监测和预后预测的生物标志物。特别是通过时间序列比较来识别差异表达基因(DEGs)。随后,选择在治疗过程中表达单调变化的 DEGs。十个一致下调的基因(CD274、KIF1B、IL15、TLR1、TLR5、FCGR1A、GBP1、NOD2、GBP2、EGF)在治疗监测方面表现出显著的潜力,通过生物学和技术验证得到了证实。此外,该生物标志物在预测治愈和复发患者方面也具有潜力。此外,该生物标志物可用于结核病诊断、潜伏性结核感染/活动性 TB 鉴别诊断以及进展为活动性 TB 的风险评估。与其他生物标志物的 10 基因生物标志物的基准分析表明,在测试数据集上具有相当的性能。总之,我们建立了一个代表结核病治疗动态反应的 10 基因转录组生物标志物。需要进一步的研究来验证、完善和将该生物标志物转化为精确的检测方法,以协助在广泛的结核病管理中做出临床决策。

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