胶质瘤脑脊液蛋白质指纹图诊断模型的建立及其在临床诊断中的应用  

Establishment of diagnostic model of cerebrospinal protein fingerprint pattern for glioma and its clinical application

作　　者：刘建[1] 郑树[1] 余捷凯[1] 俞学斌 刘伟国[1] 张建民[1] 胡汛[1] 

机构地区：[1]浙江大学医学院附属第二医院,浙江杭州310009 [2]浙江省绍兴市第一人民医院,浙江绍兴312000

出　　处：《浙江大学学报（医学版）》2005年第2期141-147,共7页Journal of Zhejiang University(Medical Sciences)

基　　金：国家重点基础研究发展规划项目(G1998051200)

摘　　要：目的SELDI-TOF建立和评估区分脑胶质瘤与非脑肿瘤、脑胶质瘤与脑良性肿瘤的脑脊液蛋白指纹图诊断模型。方法收集脑胶质瘤、脑良性肿瘤和轻度脑外伤患者的脑脊液共75份,其中50份胶质瘤和非脑肿瘤脑脊液标本,随机分为训练组33份(17例胶质瘤,16例非脑肿瘤)和盲法测试组17份(5例胶质瘤,12例非脑肿瘤),检测结合在H4蛋白芯片上的蛋白质,获得脑肿瘤和非脑肿瘤的蛋白表达质谱图,用matlab操作平台的人工神经网络分析收集的数据,建立了区分脑胶质瘤与非脑肿瘤的脑脊液蛋白指纹图诊断模型。脑胶质瘤和脑良性肿瘤47份标本,随机分为训练集31份(13例胶质瘤,18例脑良性肿瘤)和盲法测试集16份(9例胶质瘤,7例脑良性肿瘤),运用同样方法分析收集的数据,建立了区分脑胶质瘤与脑良性肿瘤的蛋白指纹图诊断模型。同时运用支持向量机对上述人工神经网络的结果进行验证,二者结果非常相似。结果1建立了区分胶质瘤与非脑肿瘤的脑脊液蛋白指纹图诊断模型,盲法测试胶质瘤诊断的敏感性和特异性分别为100%和91.7%。2建立了区分胶质瘤与脑良性肿瘤的脑脊液蛋白指纹图诊断模型,盲法测试胶质瘤诊断的敏感性和特异性分别为88.9%和100%。结论研究建立的诊断模型为胶质瘤的临床诊断尤其是定性诊断提供了一条崭新的途径。Objective: To establish the diagnostic model of cerebrospinal protein profile for gliomas by ~surface ^-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics. Methods: Seventy-five samples of cerebrospinal fluid from patients with gliomas,benign brain tumors and mild brain traumas were collected.A total of 50 samples from gliomas and non-brain-tumors were divided into training sets (33 cases including 17 gliomas and 16 non-brain-tumors ) and testing sets (17 cases including 5 gliomas and 12 non-brain-tumors ).The cerebrospinal proteins bound to H4 chip were detected by SELDI-TOF MS,the profiles of cerebrospinal protein were gained and then analyzed with artificial neural network algorithm (ANN); and the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was established.Forty-seven of cerebrospinal samples of gliomas and benign brain tumors were divided into training sets (31 cases including 13 gliomas and 18 benign brain tumors) and testing sets (16 cases including 9 gliomas and 7 benign brain tumors ),the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from benign brain tumors was established based on the same method.The support vector machine (SVM) algorithm was also used for evaluation,both results were very similar,but the result derived from ANN was more stable than that from SVM. Results: The diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was astablished and was challenged with the test set randomly,the sensitivity and specificity were 100% and 91.7%,respectively.The cerebrospinal protein profiling model for differentiating gliomas from benign brain tumors was also developed and was challenged with the test set randomly,the sensitivity and specificity were 88.9% and 100%,respectively. Conclusions: The technology of SELDI-TOF MS which combined with analysis tools of bioinformatics is a novel effective method for screening and

关 键 词：表面加强解析/电离-飞行时间-质谱仪 神经网络(计算机) 支持向量机 诊断模型 脑肿瘤/诊断 神经胶质瘸/诊断 肿瘤标记物 生物学/脑脊液 

分 类 号：R739.41[医药卫生—肿瘤]