使用表面增强激光解吸电离飞行时间质谱技术进行原发性免疫性血小板减少症诊断鉴别中的血小板蛋白质组学

Platelet proteomics in diagnostic differentiation of primary immune thrombocytopenia using SELDI-TOF-MS.

作者信息

Zhang Hong-Wei, Zhou Pan, Wang Kai-Zheng, Liu Jin-Bo, Huang Yuan-Shuai, Tu Ye-Tao, Deng Zheng-Hua, Zhu Xi-Dan, Hang Yong-Lun

机构信息

Department of Blood Transfusion, the Affiliated Hospital of Luzhou Medical College, Luzhou, Sichuan, China.

Department of Clinical Laboratory, the Affiliated Hospital of Luzhou Medical College, Luzhou, Sichuan, China.

出版信息

Clin Chim Acta. 2016 Apr 1;455:75-9. doi: 10.1016/j.cca.2016.01.028. Epub 2016 Jan 28.

DOI:10.1016/j.cca.2016.01.028

PMID:26826395

Abstract

BACKGROUND

Primary immune thrombocytopenic purpura (pITP) is defined as isolated autoimmune thrombocytopenia with idiopathic low platelet count, normal bone marrow, and unexplained causes of thrombocytopenia. Currently there is no definite criterion for ITP diagnosis.

METHODS

We conducted proteomic screen of patients with pITP, secondary immune thrombocytopenia (sITP), and healthy controls using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The proteomic profiles were obtained from platelet lysate samples of 82 healthy adult controls, 64 pITP, and 70 sITP patients, from which we screened marker proteins with significant differences, and constructed a diagnosis model using the artificial neural network (ANN) technique.

RESULTS

We identified 6 marker proteins in the platelet lysates of pITP patients. This diagnosis method differentiated pITP patients from sITP effectively with a sensitivity of 96.9% (31/32), a specificity of 71.0% (54/76), and the area under the ROC curve of 0.864 in the training set, and a sensitivity of 87.5% (28/32), a specificity of 69.7% (53/76), and a positive predictive value of 75.0% (81/108) in the test set.

CONCLUSION

The artificial neural network model based on platelet protein profiling established a potential pITP diagnosis platform.

摘要

背景

原发性免疫性血小板减少症（pITP）被定义为孤立性自身免疫性血小板减少症，伴有特发性低血小板计数、正常骨髓以及血小板减少症的不明原因。目前，ITP诊断尚无明确标准。

方法

我们使用表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）对pITP患者、继发性免疫性血小板减少症（sITP）患者和健康对照进行蛋白质组学筛查。从82名健康成人对照、64名pITP患者和70名sITP患者的血小板裂解物样本中获得蛋白质组图谱，从中筛选出具有显著差异的标志物蛋白，并使用人工神经网络（ANN）技术构建诊断模型。

结果

我们在pITP患者的血小板裂解物中鉴定出6种标志物蛋白。这种诊断方法在训练集中能有效区分pITP患者和sITP患者，敏感性为96.9%（31/32），特异性为71.0%（54/76），ROC曲线下面积为0.864；在测试集中，敏感性为87.5%（28/32），特异性为69.7%（53/76），阳性预测值为75.0%（81/108）。