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采用 SELDI-TOF 质谱技术对涂阳和涂阴肺结核患者血清蛋白进行分析。

Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry.

机构信息

Division of Pulmonary Disease, State Key Laboratory of Biotherapy, Department of Respiratory Medicine, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.

出版信息

Lung. 2010 Jan-Feb;188(1):15-23. doi: 10.1007/s00408-009-9199-6. Epub 2009 Dec 9.

Abstract

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.

摘要

本研究的重点是检测涂阳和涂阴肺结核的新型血清生物标志物,并利用表面增强激光解吸电离飞行时间质谱(SELDI-TOF MS)技术建立各自的诊断模型。采用 SELDI-TOF MS 对 155 例涂阳肺结核(SPPTB)、涂阴肺结核(SNPTB)患者和非结核(非 TB)对照者的血清样本进行分析。该研究分为初步训练集和盲测检验集。利用源自 31 例 SPPTB 患者、22 例 SNPTB 患者和 42 例非 TB 对照者的谱图构建分类树,以分别在训练集中开发出最佳的分类树,从而对它们进行区分。然后,利用另一个包含 20 例 SPPTB 患者、14 例 SNPTB 患者和 26 例非 TB 对照者的独立盲测检验集对分类树的有效性进行挑战。同样,也使用相同的方法对 SNPTB 患者和非 TB 对照者单独进行分析。在训练集中确定的一组具有质荷比(m/z)为 4821.45、3443.22、9284.93、4473.86、4702.84、3443.22、5343.26、3398.27 和 3193.61 的 9 种标志物的最佳决策树模型可以分别检测出 93.55%、95.46%和 88.09%的准确率,用于对 SPPTB 患者、SNPTB 患者和非 TB 对照者的样本进行分类。利用相同的分类树对独立的盲测检验集进行验证,结果显示对对照者的准确率为 80.77%,对 SPPTB 的准确率为 75.00%,对 SNPTB 的准确率为 71.43%。对于 SNPTB 患者和非 TB 对照者之间存在差异的峰,简化的聚类图(m/z 4821.45、4792.74)显示出区分 SNPTB 患者和非 TB 对照者的 85.94%的分类功效(敏感性 86.36%,特异性 85.71%)。包含 14 例 SNPTB 患者和 26 例非 TB 对照者的独立盲测检验集对 SNPTB 的诊断准确率为 81.59%(敏感性 78.57%,特异性 84.62%)。在 SPPTB 和 SNPTB 患者中,某些特殊蛋白质/肽可能会发生变化,这些变化可用于使用适当的判别分析方法进行区分,并进一步探索和鉴定与结核病生物学过程相关的某些参与蛋白。

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