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脑膜瘤和脑良性肿瘤及脑外伤患者脑脊液蛋白质谱差异表达模型的研究
引用本文:刘建,郑树,余捷凯,刘伟国,胡末伟.脑膜瘤和脑良性肿瘤及脑外伤患者脑脊液蛋白质谱差异表达模型的研究[J].中华检验医学杂志,2004,27(10):638-641.
作者姓名:刘建  郑树  余捷凯  刘伟国  胡末伟
作者单位:1. 310031,杭州,浙江大学附属第二医院,肿瘤研究所
2. 310031,杭州,浙江大学附属第二医院,脑外科
基金项目:国家重点基础研究发展规划资助项目 (G19980 5 12 0 0 )
摘    要:目的用表面增强激光解吸离子化飞行时间质谱(SELDI-TOF-MS)和生物信息学分析技术寻找脑脊液中能鉴别脑膜瘤和其他脑良性肿瘤及脑外伤诊断的新标志物。方法收集14例脑膜瘤、9例其他脑良性肿瘤和27例轻度脑外伤患者的脑脊液标本,用H4蛋白芯片和SELDI-TOF-MS检测蛋白质谱的表达。用Biomarker PattemsTM Software分析软件进行数据处理,建立区分脑膜瘤和脑外伤患者脑脊液中蛋白质谱差异表达模型和区分脑膜瘤和其他脑良性肿瘤脑脊液中蛋白质谱差异表达模型;并用2个模型对各2例脑膜瘤、其他脑良性肿瘤和脑外伤患者进行盲法交叉验证。结果用5个质荷比峰建立的区分脑膜瘤和脑外伤脑脊液蛋白质谱差异表达模型的准确率为98%(49/50).敏感性为100%(14/14),特异性为96.3%(26/27),阳性预测率为93.3%(14/15),阴性预测率为100%(27/27)。用4个质荷比峰建立的区分脑膜瘤和其他脑良性肿瘤脑脊液蛋白质谱差异表达模型的准确率为96%(48/50),敏感性为100%(14/14),特异性为94.4%(34/36),阳性预测率为92.3%(14/16),阴性预测率为100%(34/34)。盲法交叉验证,第一个模型能将脑肿瘤、其他脑良性肿瘤和脑外伤全部正判,第二个模型将1例脑外伤误判。结论用SELDI-TOF-MS技术平台和生物信息学技术初步建立的区分脑膜瘤与脑外伤和其他脑良性肿瘤的脑脊液蛋白差异表达模型,为寻找脑膜瘤中新的肿瘤标志物进行蛋白质组学研究开辟了一条新的途径。

关 键 词:脑外伤  脑膜瘤  良性肿瘤  脑脊液蛋白  患者  蛋白质组学  差异表达  SELDI  质谱  特异性
修稿时间:2003年11月18

SELDI-TOF analysis of cerebrospinal protein profiling to build the peptide patterns for distinguishing meningiomas from brain traumas and other benign brain tumors
LIU Jian,ZHENG Shu,YU Jie-kai,LIU Wei-guo,HU mo-wei. Cancer Institute,the Second Affiliated Hospital Zhejiang University,College of Medicine,Hangzhou ,China.SELDI-TOF analysis of cerebrospinal protein profiling to build the peptide patterns for distinguishing meningiomas from brain traumas and other benign brain tumors[J].Chinese Journal of Laboratory Medicine,2004,27(10):638-641.
Authors:LIU Jian  ZHENG Shu  YU Jie-kai  LIU Wei-guo  HU mo-wei Cancer Institute  the Second Affiliated Hospital Zhejiang University  College of Medicine  Hangzhou  China
Institution:LIU Jian,ZHENG Shu,YU Jie-kai,LIU Wei-guo,HU mo-wei. Cancer Institute,the Second Affiliated Hospital Zhejiang University,College of Medicine,Hangzhou 310009,China
Abstract:Objective To analyze the spectrometric cerebrospinal protein profiling of meningioma, other benign brain tumor and brain trauma by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) coupled with bio-informatics, to develop and evaluate the two spectrometric peptide patterns, and to exploit the diagnosis for distinguishing meningioma from other benign brain tumor and brain trauma by proteomic technology and informatics.Methods The cerebrospinal samples of 14 cases of meningiomas,9 cases of other benign tumors and 27 cases of light degree brain traumas were collected, the spectrometric protein profiling was detected with H4 protein chips and SELDI-TOF-MS, the data were analyzed by Biomarker PatternsTM Software provided by Cipergen Corp. The two spectrometric peptide patterns for distinguishing meningiomas from brain traumas and other benign tumors were set up. Then 2 cases of meningioma,2 cases of other brain benign tumor and 2 cases of brain trauma were used to evaluate the two patterns.Results It was successful to develop and evaluate the peptide pattern for distinguishing meningiomas from brain traumas, the accuracy was 98%, sensitivity was 100%, specificity was 96.3%, positive predictive value was 93.3%, negative predictive value was 100%. It was successful to develop and evaluate the peptide pattern for distinguishing meningiomas from other benign tumors, the accuracy was 96%, sensitivity was 100%, specificity was 94.4%, positive predictive value was 92.3%, negative predictive value was 100%. In validated proceeding, all meningiomas, the brain benign tumors and 1 cases of brain trauma were judged correctly. Only one case of brain traumas was misjudged. Therefore, these two peptide patterns were validated for their effective primarily.Conclusions It is successful to develop and evaluate the different spectrometric protein profiling patterns of meningioma, other benign brain tumor and non-brain-tumor by SELDI-TOF-MS and bio-informatics. This broadens a new application platform to discovery tumor biomarkers of meningioma.
Keywords:Meningioma  Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry  Brain injuries  Computational biology
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