南方科技大学知识苑(SUSTech KC): Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis

题名	Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis
作者	Yao, Fusheng 1; Zhang, Ruiqi1 ; Lin, Qiao 2; Xu, Hui 1; Li, Wei 3; Ou, Min 1; Huang, Yiting 3; Li, Guobao 1; Xu, Yuzhong 2; Song, Jiaping 3; Zhang, Guoliang 1
通讯作者	Zhang, Guoliang
发表日期	2024-12-31
DOI	10.1080/22221751.2024.2370399
发表期刊	EMERGING MICROBES & INFECTIONS 影响因子和分区
EISSN	2222-1751
卷号	13 期号:1
摘要	Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFN gamma and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.
关键词	Tuberculosis diagnosis PEA biomarker machine learning
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	第一 ; 通讯
资助项目	National Natural Science Foundation of China[82170009] ; National Key Research and Development Plan[2021YFA1300902] ; Guangdong Science Fund for Distinguished Young Scholars[0620220214] ; State Key Laboratory of Respiratory Diseases Open Project[SKLRD-OP-202324] ; Shenzhen Scientific and Technological Foundation["KCXFZ20211020163545004","RCJC20221008092726022"]
WOS研究方向	Immunology ; Infectious Diseases ; Microbiology
WOS类目	Immunology ; Infectious Diseases ; Microbiology
WOS记录号	WOS:001262151600001
出版者	TAYLOR & FRANCIS LTD
来源库	Web of Science
引用统计
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/786688
专题	南方科技大学第二附属医院南方科技大学第一附属医院
作者单位	1.Southern Univ Sci & Technol, Shenzhen Peoples Hosp 3, Natl Clin Res Ctr Infect Dis, Shenzhen 518112, Peoples R China 2.Shenzhen Univ, Baoan Peoples Hosp Shenzhen, Affiliated Hosp 2, Shenzhen, Peoples R China 3.iCarbonX, Zhuhai ICXIVD Biotechnol Co Ltd, Zhuhai, Peoples R China
第一作者单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
通讯作者单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
第一作者的第一单位	南方科技大学第二附属医院; 南方科技大学第一附属医院
推荐引用方式 GB/T 7714	Yao, Fusheng,Zhang, Ruiqi,Lin, Qiao,et al. Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis[J]. EMERGING MICROBES & INFECTIONS,2024,13(1).
APA	Yao, Fusheng.,Zhang, Ruiqi.,Lin, Qiao.,Xu, Hui.,Li, Wei.,...&Zhang, Guoliang.(2024).Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis.EMERGING MICROBES & INFECTIONS,13(1).
MLA	Yao, Fusheng,et al."Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis".EMERGING MICROBES & INFECTIONS 13.1(2024).