中文摘要：

目的探讨帕金森病（Parkinson’s disease,PD）患者区别于正常人的血清蛋白质差异表达。方法选择原发性PD患者35例和正常人35例,用弱阳离子交换（weak cationic exchange,WCX）磁珠捕获血清蛋白质组分,用MALDI-TOF-MS（matrix assisted laser desorption/ionization time of flight mass spectrometer）检测各样品的蛋白质质谱,统计学筛选差异表达分子,监督神经网络算法建立区分模型,盲法验证。结果在PD组和对照组之间筛查到8个差异分子（非参数检验Z值范围为-4.458～-3.059,P〈0.05）。以监督神经网络算法建立区分模型,其判断正确率为81.4%。对25例新样本的盲法验证结果显示,模型的正确率为76.0%。结论PD患者血清蛋白质的表达谱有别于正常人。蛋白质组学数据结合生物信息学方法可能有助于PD的诊断。

英文摘要：

Objective To study the differential expression of serum low molecular weight proteins in Parkinson＇ s disease （PD） patients and normal subjects. Methods Serum samples from 35 idiopathic PD patients and 35 normal subjects were selected. Serum proteins were captured by weak cationic exchange（WCX） magnetic beads. Molecular weight of the proteins in beads-binding fraction was detected by MALDI-TOF-MS. Differential expression molecules in PD patients and normal subject were screened by statistics. A classification model was constructed by bioinformatics tools like Supervised Neural Network （SNN） , and was validated by using 25 newly recruited samples. Results A total of 8 discriminating M/Z peaks related to PD were identified （ P 〈 0.05, nonparametric test, Z ： -4. 458 - -3. 059）. The classification model based on SNN generated a separation between PD and healthy controls. The correct rate was 81.4% for training set, and was 70.0% for 25 newly recruited samples. Conclusion Protein expression in serum of PD patients is different from the normal controls. Serum proteomics data combined with bioinformatics approaches may contribute to the diagnosis of PD.