华西医学

华西医学

血清蛋白质组学技术诊断肺结核的临床运用:基于 Meta 分析的证据

查看全文

目的通过 Meta 分析综合评估血清蛋白质组学技术对肺结核的临床诊断价值。方法在 Scopus、PubMed、万方、中国知网、维普等数据库检索 2018 年 3 月以前利用血清蛋白质组学技术诊断肺结核的论文,利用诊断性试验质量评价工具-2 评价纳入文献的质量,计算汇总灵敏度、特异度、似然比与诊断优势比,绘制汇总受试者工作特征曲线并计算曲线下面积。结果共纳入 10 篇文献,合计 2 433 例患者。Meta 分析结果显示血清蛋白质组学技术诊断肺结核的灵敏度为 0.86,特异度为 0.88,阳性似然比为 6.72,阴性似然比为 0.17,诊断优势比为 46.84,汇总受试者工作特征曲线下面积为 0.93。结论血清蛋白质组学技术对肺结核具有良好的诊断价值,可作为诊断肺结核的新技术。

ObjectiveTo summarize the overall diagnostic accuracy of serum proteomic assay for pulmonary tuberculosis through a Meta-analysis.MethodsStudies regarding the diagnostic utility of serum proteomic assay for pulmonary tuberculosis were searched in Scopus, PubMed, Wanfang, China National Knowledge Infrastructure, and CQVIP. The methodical quality was evaluated by Quality Assessment for Studies of Diagnostic Accuracy Studies-2 tool. The pooled sensitivity, specificity, positive/negative likelihood ratios, and diagnostic odds ratio were calculated. Summary receiver operating characteristic curve was generated and the area under the curve was calculated.ResultsThere were 10 articles with 2 433 patients included in this study, containing 1 191 cases and 1 242 controls. The pooled sensitivity, specificity, positive/negative likehood ratios, and diagnostic odds ratio were 0.86, 0.88, 6.72, 0.17, and 46.84, respectively. The area under the curve was 0.93.ConclusionSerum proteomic assay plays a role in diagnosing pulmonary tuberculosis, and proteomic assay represents a novel and useful method for diagnosing pulmonary tuberculosis.

关键词: 蛋白质组学; 肺结核; 诊断; Meta 分析

Key words: Proteomic assay; Pulmonary tuberculosis; Diagnosis; Meta-analysis

引用本文: 李大江, 秦江月, 刘坤, 申永春, 陈雪融, 唐小燕. 血清蛋白质组学技术诊断肺结核的临床运用:基于 Meta 分析的证据. 华西医学, 2018, 33(8): 977-983. doi: 10.7507/1002-0179.201807133 复制

登录后 ,请手动点击刷新查看全文内容。 没有账号,
登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. 世界卫生组织. 2017全球结核病报告. (2018-01-28)[2018-06-01]. http://www.who.int/tb/publications/global_report/gtbr2017_executive_summary_zh.pdf?ua=1.
2. 陈大川, 王在义. 肺结核诊断的研究进展. 临床肺科杂志, 2016, 21(1): 145-148.
3. Lawn SD. Diagnosis of pulmonary tuberculosis. Curr Opin Pulm Med, 2013, 19(3): 280-288.
4. 孙会姗, 潘丽萍, 贾红彦, 等. 血清蛋白质组学技术在结核病诊断中的研究进展. 中华结核和呼吸杂志, 2017, 40(5): 363-365.
5. Agranoff D, Fernandez-Reyes D, Papadopoulos MC, et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet, 2006, 368(9540): 1012-1021.
6. Liu Q, Pan L, Han F, et al. Proteomic profiling for plasma biomarkers of tuberculosis progression. Mol Med Rep, 2018, 18(2): 1551-1559.
7. 刘琦, 刘新宇, 张仁卿, 等. 汉、藏族肺结核患者血清差异蛋白质的质谱分析. 第三军医大学学报, 2010, 32(18): 1986-1990.
8. Liu J, Jiang T, Jiang F, et al. Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis. Int J Clin Exp Med, 2015, 8(2): 2077-2085.
9. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
10. 温红侠, 陈一强, 何敏, 等. 采用蛋白芯片筛选活动性肺结核血清标志物. 中华结核和呼吸杂志, 2008, 31(1): 63-64.
11. 吴雪琼, 张俊仙, 梁艳, 等. 应用蛋白质谱建立活动性肺结核病的血清诊断模型. 中华微生物学和免疫学杂志, 2008, 28(11): 1040-1043.
12. Liu Q, Chen X, Hu C, et al. Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry. Lung, 2010, 188(1): 15-23.
13. 翁丽珍, 王琳, 李学玲, 等. 肺结核蛋白指纹图谱诊断技术研究. 中国人兽共患病学报, 2010, 26(11): 1048-1051.
14. Deng C, Lin M, Hu C, et al. Exploring serological classification tree model of active pulmonary tuberculosis by magnetic beads pretreatment and MALDI-TOF MS analysis. Scand J Immunol, 2011, 74(4): 397-405.
15. Liu JY, Jin L, Zhao MY, et al. New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Clin Chem Lab Med, 2011, 49(10): 1727-1733.
16. 王琳, 翁丽珍, 李学玲, 等. 蛋白质指纹图谱技术在肺结核、肺癌鉴别诊断中的应用研究. 中国防痨杂志, 2011, 33(4): 209-213.
17. Zhang J, Wu X, Shi L, et al. Diagnostic serum proteomic analysis in patients with active tuberculosis. Clin Chim Acta, 2012, 413(9/10): 883-887.
18. Liu J, Jiang T, Wei L, et al. The discovery and identification of a candidate proteomic biomarker of active tuberculosis. BMC Infect Dis, 2013, 13(1): 506.
19. De Groote MA, Sterling DG, Hraha T, et al. Discovery and validation of a six-marker serum protein signature for the diagnosis of active pulmonary tuberculosis. J Clin Microbiol, 2017, 55(10): 3057-3071.
20. Zhang X, Liu F, Li Q, et al. A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection. Diagn Microbiol Infect Dis, 2014, 79(4): 432-437.
21. Zhang L, Wang Q, Wang W, et al. Identification of putative biomarkers for the serodiagnosis of drug-resistant Mycobacterium tuberculosis. Proteome Sci, 2012, 10: 12.
22. Nahid P, Bliven-Sizemore E, Jarlsberg LG, et al. Aptamer-based proteomic signature of intensive phase treatment response in pulmonary tuberculosis. Tuberculosis (Edinb), 2014, 94(3): 187-196.
23. Xu D, Li Y, Li X, et al. Serum protein S100A9, SOD3, and MMP9 as new diagnostic biomarkers for pulmonary tuberculosis by iTRAQ-coupled two-dimensional LC-MS/MS. Proteomics, 2015, 15(1): 58-67.