基础医学与临床 ›› 2010, Vol. 30 ›› Issue (3): 263-267.

• 研究论文 • 上一篇    下一篇

基于血清蛋白组学的慢性肾衰预测模型探讨

何磊 程亚伟 廖萍 胡衡 金亚明 李福凤 王文静 钱鹏 王忆勤   

  1. 上海中医药大学中医证实验室
  • 收稿日期:2009-04-24 修回日期:2009-05-30 出版日期:2010-03-05 发布日期:2011-05-04

Exploring on the Prediction Model of Chronic Renal Failure based on Serum Proteomics

Lei HE, Ya-wei CHENG, Ping LIAO, Heng HU, Ya-ming JIN, Fu-feng LI, Wen-jing WANG, Peng QIAN, Yi-qin WANG   

  1. Laboratory of Syndrome of TCM , Shanghai University of Traditional Chinese Medicine
  • Received:2009-04-24 Revised:2009-05-30 Online:2010-03-05 Published:2011-05-04

摘要: 目的 通过比较慢性肾衰(CRF)患者与正常人血清蛋白表达谱的差异,筛选血清蛋白标志物并建立诊断模型,探讨其在慢性肾衰血清学诊断中的意义。方法 收集62例CRF患者和28例正常人的血清,经表面增强激光解析离子化飞行时间质谱(SELDI-TOF-MS)检验并筛选血清蛋白标志物。经生物信息学分析建立预测模型并进行验证。结果 在质荷比(m/z)1500~30000范围内,检测到51个有效蛋白峰,发现有19个峰有显著差异 (P<0.001),其中18个峰呈低表达,1个峰呈高表达;且CRF组和正常组的聚类性质明显不同;组内样本彼此靠近,组间样本彼此分开。构建的"慢性肾衰组-正常组"诊断决策树模型,预测正确率为87.8%,灵敏度为87.1%,特异度为89.3%。结论 该决策树模型能对慢性肾衰做出较为准确的预测判断,为慢性肾衰的临床早期发现提供一定的实验依据

关键词: 慢性肾功能衰竭, CM10蛋白芯片, 血清

Abstract: Objective To Screen serum protein markers related to CRF and establish the diagnosis model, exploring and discussing its significance in serodiagnosis by comparing differences of serum protein spectrum expression between patients with chronic renal failure(CRF) and control group.Methods Collecting 62 patients of CRF and control group with 28 normal ones.Serum samples were tested by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS).The data were analyzed to screen serum proteomic biomarkers.By bioinformatics analysis ,decision classification tree models were to be established and tested.Results A total of 19 effective protein peaks were significantly different between CRF and normal control (P<0.001) at m/z range of 1500 to 30000,among which 18 showed low expression and 1 showed high expression in CRF. CRF and normal control was obviously different from the nature of the clustering; and samples of each group near each other, inter-group samples from each other. By the bioinformatics analysis , establishing a "CRF-normal controls " of the Diagnostic decision tree model, which was 87.8% in prediction accuracy rate with a sensitivity of 87.1% and a specificity of 89.3%.Conclusions Diagnostic decision tree model made more accurate judgments prediction and provided the experimental evidence for early clinical detection

Key words: Chronic renal failure, CM10 protein chip, serum