Identification of eight-protein biosignature for diagnosis of tuberculosis

Author:

Yang Qianting,Chen Qi,Zhang Mingxia,Cai Yi,Yang Fan,Zhang Jieyun,Deng Guofang,Ye Taosheng,Deng Qunyi,Li Guobao,Zhang Huihua,Yi Yuhua,Huang Ruo-Pan,Chen XinchunORCID

Abstract

BackgroundBiomarker-based tests for diagnosing TB currently rely on detecting Mycobacterium tuberculosis (Mtb) antigen-specific cellular responses. While this approach can detect Mtb infection, it is not efficient in diagnosing TB, especially for patients who lack aetiological evidence of the disease.MethodsWe prospectively enrolled three cohorts for our study for a total of 630 subjects, including 160 individuals to screen protein biomarkers of TB, 368 individuals to establish and test the predictive model and 102 individuals for biomarker validation. Whole blood cultures were stimulated with pooled Mtb-peptides or mitogen, and 640 proteins within the culture supernatant were analysed simultaneously using an antibody-based array. Sixteen candidate biomarkers of TB identified during screening were then developed into a custom multiplexed antibody array for biomarker validation.ResultsA two-round screening strategy identified eight-protein biomarkers of TB: I-TAC, I-309, MIG, Granulysin, FAP, MEP1B, Furin and LYVE-1. The sensitivity and specificity of the eight-protein biosignature in diagnosing TB were determined for the training (n=276), test (n=92) and prediction (n=102) cohorts. The training cohort had a 100% specificity (95% CI 98% to 100%) and 100% sensitivity (95% CI 96% to 100%) using a random forest algorithm approach by cross-validation. In the test cohort, the specificity and sensitivity were 83% (95% CI 71% to 91%) and 76% (95% CI 56% to 90%), respectively. In the prediction cohort, the specificity was 84% (95% CI 74% to 92%) and the sensitivity was 75% (95% CI 57% to 89%).ConclusionsAn eight-protein biosignature to diagnose TB in a high-burden TB clinical setting was identified.

Funder

National Science and Technology Major Project

Guangdong Provincial Science and Technology Program

Science and Technology Project of Shenzhen

National Natural Science Foundation of China

Jin Qi team of Sanming Project of Medicine in Shenzhen

Publisher

BMJ

Subject

Pulmonary and Respiratory Medicine

Reference36 articles.

1. World Health Organisation . Global tuberculosis report 2019.

2. Tuberculosis: advances and challenges in development of new diagnostics and biomarkers

3. Indicators for prediction of Mycobacterium tuberculosis positivity detected with bronchoalveolar lavage fluid;Liu;Infect Dis Poverty,2018

4. Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice

5. WHO . High-priority target product profi les for new tuberculosis diagnostics: report of a consensus meeting. Geneva: World Health Organization, 2014.

全球学者库

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"全球学者库"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前全球学者库共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2023 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3