摘要
青少年(adolescents and young adults,AYA)是结核病的主要患病群体之一。为了研究针对这个群体的独特性诊断标志物,本研究招募了81名AYA受试者,通过定量蛋白质组学的方法,绘制了AYA结核病患者高质量的血清蛋白质图谱。血清蛋白组数据表明,活动性肺结核(active tuberculosis,ATB)患者中血红蛋白和载脂蛋白相对丰度显著降低。通路富集分析表明,ATB组下调蛋白显著富集于抗氧化、细胞脱毒相关通路,表明存在广泛的氧化应激损伤。通过随机森林(random forest,RF)和极致梯度提升(extreme gradient boosting,XGBoost)联合评估蛋白重要性,获得了一组区分ATB和非ATB的候选蛋白标志物。基于特征递归消除的支持向量机算法,发现载脂蛋白A1(apolipoprotein A-I,APOA1)、血红蛋白亚基α(hemoglobin subunit alpha-1,HBA1)、血红蛋白亚基β(hemoglobin subunit beta,HBB)这3个蛋白的组合在诊断ATB过程中具有最高的准确性和敏感性。临床生化常规检测的血红蛋白(hemoglobin,HGB)和白蛋白(albumin,ALB)含量可以作为评估APOA1、HBB蛋白表达变化的血液生化指标。本研究建立了AYA结核病患者的血清蛋白组全貌,获得了该类群结核病的新诊断标志物。
Adolescents and young adults(AYAs)are one of the major populations susceptible to tuberculosis.However,little is known about the unique characteristics and diagnostic biomarkers of tuberculosis in this population.In this study,81 AYAs were recruited,and the high-quality serum proteome of the AYAs with tuberculosis was profiled by quantitative proteomics.The data of serum proteomics indicated that the relative abundance of hemoglobin and apolipoprotein was significantly reduced in the patients with active tuberculosis(ATB).The pathway enrichment analysis showed that the downregulated proteins in the ATB group were mainly involved in the antioxidant and cell detoxification pathways,indicating extensive oxidative stress damage.Random forest(RF)and extreme gradient boosting(XGBoost)were employed to evaluate protein importance,which yielded a set of candidate proteins that can distinguish between ATB and non-ATB.The analysis with the support vector machine algorithm(recursive feature elimination)suggested that the combination of apolipoprotein A-I(APOA1),hemoglobin subunit beta(HBB),and hemoglobin subunit alpha-1(HBA1)had the highest accuracy and sensitivity in diagnosing ATB.Meanwhile,the levels of hemoglobin(HGB)and albumin(ALB)can be used as blood biochemical indicators to evaluate changes in the protein levels of APOA1 and HBB.This study established the serum proteome landscape of AYAs with tuberculosis and identified new biomarkers for the diagnosis of tuberculosis in this population.
作者
陈禹
许鸿翔
田瑶
何谦
赵晓云
张国斌
谢建平
CHEN Yu;XU Hongxiang;TIAN Yao;HE Qian;ZHAO Xiaoyun;ZHANG Guobin;XIE Jianping(Department of Tuberculosis,China Shenyang the Tenth People’s Hospital(China Shenyang Chest Hospital),Shenyang 110044,Liaoning,China;Institute of Modern Biomedical Research,School of Life Sciences,Southwest University,Chongqing 400715,China;School of Public Health,China Medical University,Shenyang 110044,Liaoning,China;School of Life Science and Biopharmaceutics,Shenyang Pharmaceutical University,Shenyang 110016,Liaoning,China;Shenyang Disease Control Center,Shenyang 110623,Liaoning,China)
出处
《生物工程学报》
北大核心
2025年第4期1478-1489,共12页
Chinese Journal of Biotechnology
基金
2021沈阳市科学技术计划(21-173-9-72)
2022年辽宁省科学技术计划面上项目(2022-MS-432)。
关键词
结核病
血清蛋白组学
机器学习
诊断标志物
载脂蛋白
tuberculosis
serum proteomics
machine learning
diagnostic biomarkers
apolipoprotein
