Yu Jiajia, Yuan Jinfeng, Liu Zhidong, Ye Huan, Lin Minggui, Ma Liping, Liu Rongmei, Ding Weimin, Li Li, Ma Tianyu, Tang Shenjie, Pang Yu
Department of Infectious Diseases and Clinical Microbiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
Department of Bacteriology and Immunology, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, 101149, China.
Clin Proteomics. 2024 Dec 18;21(1):66. doi: 10.1186/s12014-024-09514-4.
Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.
In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis.
A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570.
Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.
结核病（TB）诊断监测对临床决策至关重要，宿主生物标志物似乎发挥着重要作用。目前可用的结核病检测诊断技术并不完善。在本研究中，我们旨在通过尿液代谢组学和蛋白质组学分析确定用于诊断肺结核（PTB）的生物标志物。
在该研究中，收集了40例PTB患者、40例肺癌（LCA）患者、40例社区获得性肺炎（CAP）患者以及40例健康对照（HC）的尿液。基于随机森林（RF）分析选择生物标志物组。
通过成对比较共检测到3868种蛋白质和1272种注释代谢特征。以AUC≥0.80作为临界值，我们挑选出5种用于PTB诊断的蛋白质生物标志物。这5种蛋白质组成的生物标志物组对PTB/HC、PTB/CAP和PTB/LCA的AUC分别为0.9840、0.9680和0.9310。此外，还选择了5种代谢生物标志物用于鉴别诊断。通过使用这5种代谢物组成的生物标志物组，我们可以区分PTB/HC，AUC为0.9940；区分PTB/CAP，AUC为0.8920；区分PTB/LCA，AUC为0.8570。
我们的数据表明，代谢组学和蛋白质组学分析能够识别出一种新型尿液生物标志物组，用于高灵敏度和特异性地诊断PTB。受试者工作特征曲线分析表明，通过这些尿液生物标志物进行PTB的非侵入性临床诊断是可行的。
