首都医科大学学报 ›› 2009, Vol. 30 ›› Issue (5): 597-600.doi: 10.3969/j.issn.1006-7795.2009.05.006

• PD的发病机制与早期诊断 • 上一篇    下一篇

帕金森病患者血清低分子量蛋白质差异表达分析

李尧华1, 叶懿文1, 李昕1, 于顺1, 杨慧2, 陈彪1   

  1. 1. 首都医科大学宣武医院老年病研究所神经生物学研究室,神经变性病教育部重点实验室;2. 首都医科大学神经科学研究所
  • 收稿日期:2009-07-16 修回日期:1900-01-01 出版日期:2009-10-21 发布日期:2009-10-21
  • 通讯作者: 李尧华

Differential Expression of Low Molecular Weight Proteins in Sera of Parkinson's Disease Patients and Normal Subjects

LI Yao-hua1, YE Yi-wen1, LI Xin1, YU Shun1, YANG Hui2, CHEN Biao1   

  1. 1. Department of Neurobiology, Beijing Institute of Geriatrics; Xuanwu Hospital, Capital Medical University, Key Laboratory for Neurodegenerative Diseases of Ministry of Education;2. Institute for Neuroscience, Capital Medical University
  • Received:2009-07-16 Revised:1900-01-01 Online:2009-10-21 Published:2009-10-21

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。

关键词: 帕金森病, 蛋白质组学, 生物标志物, 诊断

Abstract: 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.

Key words: Parkinson's disease, proteomics, biomarkers, diagnosis

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