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高发区自然人群贲门癌血清蛋白指纹图诊断模型的建立及临床价值 被引量:4

Establishment of diagnostic model of serum protein fingerprint patterns for cardia cancer in high incidence area and its clinical value
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摘要 目的用表面增强激光解吸离子化飞行时间质谱(SELD I-TOF-MS)分析食管癌高发区自然人群中贲门癌和正常对照血清蛋白表达谱的改变,筛选并建立高发区贲门癌血清蛋白指纹图诊断模型并探究其临床价值。方法采用CM10蛋白芯片及SELD I-TOF-MS技术对34例贲门癌和38例正常对照者血清蛋白指纹图谱进行检测,所得结果用ZUC I-蛋白芯片数据分析系统(ZUC I-Prote in Ch ip Data Analyze System)软件包分析,建立贲门癌蛋白指纹图诊断模型,并用留一法交叉验证作为评估模型、判别效果的方法。结果通过软件包运算,用3个质荷比峰(5 643.457 93、8 570.821 26、15 940.153 3 m/z)建立了贲门癌蛋白指纹图诊断模型,其准确度93.06%,敏感度85.29%,特异度100%,阳性预测值100%。结论本组建立的诊断模型可以有效区分贲门癌和健康人,为肿瘤高发区贲门癌的诊断与筛查提供了一条崭新途径。 Objective To analyze the alterations of serum protein patterns in cardia cancer patients coming from high incidence area of esophageal cancer by surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS), screen and build diagnostic model of cardia cancer in high incidence area and investigate its clinical value. Methods SELDI-TOF-MS and CM10 protein chip were used to detect the serum protein patterns of 34 cases of cardia cancer patients and 38 cases of healthy controls. The data was analyzed and the diagnostic model was established by using ZUCI-protein chip data analyze system software package. The diagnostic model was evaluated and validated by leave one cross validation. Results A diagnostic model consisting of three protein peaks (5 643. 457 93,8 570. 821 26,15 940. 153 3 m/z) could do the best in the diagnosis between cardia cancer and controls. Its accuracy was 93.06%, sensitivity 85.29%, specificity 100%, positive value 100%. Conclusion The diagnostic model formed by three protein peaks can do the best in discriminating cardia cancer from controls. It provides a new approach for diagnosing and screening cardia cancer in high incidence area of esophageal cancer.
出处 《临床荟萃》 CAS 北大核心 2006年第12期841-844,共4页 Clinical Focus
基金 河北省科技计划项目基金资助(No:052761361 No:03276196D-33) 河北省重大攻关课题基金资助(No:03276198D)
关键词 贲门癌 表面增强激光解吸离子化飞行时间质谱 蛋白芯片 诊断模型 留-法交叉验证 cardia cancer surface enhanced laser desorption/ionization time-of-flight mass spectrometry protein chip diagnostic model leave one cross validation
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