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具有成本效益的全转录组测序景观和在活跃结核病中的诊断潜在生物标志物

Zaiqiao Sun¹, Boxiao He¹, Zhifeng Yang²

¹State Key Laboratory of Virology, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Department of Clinical Oncology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei Province 430072, China.

ACS infectious diseases

|June 4, 2024

概括

一种新的低成本测序方法,双适配器定向捕获测序 (DADCSeq),有助于结核病的诊断. 这种方法有效地区分活跃结核病和潜伏或健康状态,使用血液样本.

科学领域:

基因组学和传染病诊断传染病诊断

背景情况:

结核病 (TB) 仍然是一个重大的全球健康威胁,由于缺乏快速准确的诊断工具而受到阻碍.
现有的诊断方法往往在速度和精度方面不足,延迟了关键的治疗干预措施.

研究的目的:

制定一个有效和可访问的策略来诊断结核病.
创建可靠的多mRNA风险概率模型,以区分活跃结核病与潜伏结核病和健康对照.

主要方法:

开发双适配器定向捕获测序 (DADCSeq),一种低成本的全转录组测序技术.
DADCSeq应用于来自活跃结核病,潜伏结核病和健康个体的外周血液单核细胞 (PBMC).
核心差异表达基因的计算识别,以构建结核病多mRNA风险概率模型 (TBMMRP).

主要成果:

该TBMMRP模型在准确区分活跃结核病患者与潜伏结核病患者和健康对照中表现出强的性能.
跨独立的跨平台队列的验证证实了有利的诊断准确性,超过或匹配以前的诊断标记.
DADCSeq方法被证明是整个转录组测序的简单操作和成本效益.

结论:

一个低成本和有效的结核病诊断策略已经成功开发出来.
DADCSeq方法和TBMMRP模型为结核病诊断提供了一个有前途的进步.
随着临床队伍的增长,该策略有可能扩展到诊断其他疾病.

关键词:

机器学习是机器学习.结核病是一种疾病.生物标志物生物标志物整个转录基因组的测序.

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