Serum Protein Profiling of Smear-Positive and Smear-Negative Pulmonary Tuberculosis Using SELDI-TOF Mass Spectrometry
Serum Protein Profiling of Smear-Positive and Smear-Negative Pulmonary Tuberculosis Using SELDI-TOF Mass Spectrometry
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DOI：
10.1007/s00408-009-9199-6
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发表时间：
2010-01-01
期刊：
影响因子：
5
通讯作者：
Wen, Fuqiang
中科院分区：
文献类型：
--
作者：
Liu, Qi;Chen, Xuerong;Wen, Fuqiang
本研究旨在应用表面增强激光解吸电离飞行时间质谱（SELDI-TOF MS）技术检测涂阳和涂阴肺结核患者血清中的新生物标志物，并建立相应的诊断模型。采用SELDI-TOF MS分析了155份痰涂片阳性肺结核（SPPTB）、痰涂片阴性肺结核（SNPTB）患者和非结核（non-TB）对照者的血清样本。来自31例SPPTB患者、22例SNPTB患者和42例非TB对照的光谱的分类树用于开发在训练集中分别区分它们的最佳分类树。然后，用另一个独立的盲法测试集挑战分类树的有效性，该测试集包括20名SPPTB患者，14名SNPTB患者和26名非TB对照。SNPTB患者和非TB对照也使用相同的方法单独分析。最佳决策树模型具有在训练集中确定的质荷比（m/z）为4821.45、3443.22、9284.93、4473.86、4702.84、3443.22、5343.26、3398.27和3193.61的一组9种生物标志物，可以分别检测到93.55%、95.46%和88.09%的对SPPTB患者、SNPTB患者和非TB对照标本进行分类的准确性。使用相同的分类树，一个独立的，设盲的测试集的验证给出了80.77%的控制，75.00%的SPPTB和71.43%的SNPTB样品的准确性。由于SNPTB患者和非TB对照之间的峰显示差异，简化的树状图（m/z 4821.45，4792.74）显示区分SNPTB患者和非TB对照的分类效率为85.94%（灵敏度86.36%和特异性85.71%）。对14例SNPTB患者和26例非结核对照者进行独立盲法检测，其诊断SNPTB的准确率为81.59%（敏感性78.57%，特异性84.62%）。SPPTB和SNPTB患者中可能存在特异性蛋白/肽的变化，这些变化可用于区分SPPTB和SNPTB，并可用于寻找和鉴定结核病生物学过程中的相关蛋白。
The focus of this study was to detect novel sera biomarkers for smear-positive and smear-negative pulmonary tuberculosis and to establish respective diagnostic models using the surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) technique. A total of 155 sera samples from smear-positive pulmonary tuberculosis (SPPTB) and smear-negative pulmonary tuberculosis (SNPTB) patients and non-tuberculosis (non-TB) controls were analyzed with SELDI-TOF MS. The study was divided into a preliminary training set and a blinded testing set. A classification tree of spectra derived from 31 SPPTB patients, 22 SNPTB patients, and 42 non-TB controls were used to develop an optimal classification tree that discriminated them respectively in the training set. Then, the validity of the classification tree was challenged with another independent blinded testing set, which included 20 SPPTB patients, 14 SNPTB patients, and 26 non-TB controls. SNPTB patients and non-TB controls also were analyzed alone using the same method. The optimal decision tree model with a panel of nine biomarkers with mass:charge ratios (m/z) of 4821.45, 3443.22, 9284.93, 4473.86, 4702.84, 3443.22, 5343.26, 3398.27, and 3193.61 determined in the training set could detect 93.55%, 95.46%, and 88.09% accuracy for classifying SPPTB patients, SNPTB patients, and non-TB controls specimens, respectively. Validation of an independent, blinded testing set gave an accuracy of 80.77% for controls, 75.00% for SPPTB, and 71.43% for SNPTB samples using the same classification tree. With the peaks displaying differences between SNPTB patients and non-TB controls, a simplified dendrogram (m/z 4821.45, 4792.74) demonstrated classification efficacy of 85.94% (sensitivity 86.36% and specificity 85.71%) for distinguishing SNPTB patients from non-TB controls. The independent blinded testing set containing 14 SNPTB patients and 26 non-TB controls gained an accuracy of 81.59% (sensitivity 78.57% and specificity 84.62%) for diagnosing SNPTB. Special proteins/peptides may change in SPPTB and SNPTB patients and those changes may be used to distinguish them with the proper discriminant analytical method and to pursue and identify some involved proteins underlying the biological process of tuberculosis.