【作者】 张园；

【导师】 黄艳春；

【作者基本信息】 新疆医科大学 ， 肿瘤学， 2009， 硕士

【摘要】 目的:通过表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术分析大肠癌患者和正常对照血清蛋白质组图谱的改变,筛选大肠癌的差异蛋白,建立和优化大肠癌血清蛋白质组图谱的模型,并探究其临床价值。材料和方法:将102例血清标本(大肠癌53例,正常对照49例)随机分成训练组75例,测试组27例。用CM10蛋白芯片及SELDI-TOF-MS技术对训练组75例血清标本(大肠癌37例,正常对照38例)进行蛋白质组图谱检测,用留一法交叉验证作为评估模型、判别效果的方法,验证上述诊断模型的区分能力。并对测试组27例未知的血清进行血清蛋白质谱测定,盲法验证该模型。同期对102例血清标本采用电化学发光法检测癌胚抗原(CEA)水平。结果:通过ZUCI-PDAS蛋白芯片数据分析系统软件包运算,用6个质荷比峰(3951.15、4364.49、5926.63、8103.64、8964.43、11709.02 m/z)建立了大肠癌蛋白质组图谱诊断模型,经过交叉验证其准确度96.00%,灵敏度94.59%,特异度97.37%,阳性预测值97.22%,经盲法验证该方法的检出率为81.25%,排除率为100%,均高于同期CEA检测灵敏度及特异度(50.94%,91.83%)。结论:用SELDI-TOF-MS技术分析大肠癌血清蛋白质表达谱,发现由6个差异表达蛋白及其特定组合构成的诊断模型可以有效区分大肠癌和非癌正常人群,为大肠癌的诊断与筛查提供了一条崭新途径。SELDI-TOF-MS技术和生物信息学分析软件的联合应用是一种寻找肿瘤生物标志物的有效方法。更多还原

【Abstract】 Objective: To analyze the alterations of serum protein patterns in colorectal cancer patients by SELDI-TOF-MS (surface-enhanced laser desorption/ionization time of flight mass spectrometry), screen and build diagnosis model of colorectal cancer and investigate its clinical value. Materials and methods: One hundred and two serum samples (53 CRC. Patients,49 healthy individuals) randomly divided into training set (n=75, 37 CRC. patients and 38 healthy individuals) and test set (n=27). SELDI-TOF-MS and CM10 protein chip were used to detect the serum protein patterns of training set. The diagnostic model was evaluated and validated by leave one cross validation. The classifier was then challenged with the test set. Meanwhile, using Electrochemiluminescence immunoassay to analysis the CEA level of one hundred and two serum samples. Results:A diagnostic model consisting of six protein peaks (3951.15、4364.49、5926.63、8103.64、8964.43、11709.02m/z) could do the best in the diagnosis between colorectal cancer and controls. Its accuracy was 96.00%, sensitivity was 94.59%, specificity was 97.37% and positive value was 97.22%。By blind examination in test set, the corresponding accuracy was 81.25%,the corresponding sensitivity was 100%, which was significantly better than use of CEA (sensitivity 50.94%,specificity 91.83%) for early detection of colorectal cancer. Conclusion:Using SELDI-TOF-MS technology, we have discovered a candidate CRC pattern consisting of six peaks and build the diagnostic model. The model can do the best in discriminating colorectal cancer from controls. It provides a new approach for diagnosing and screening colorectal cancer. Combined use of bioinformatics tools and proteomic profiling provides an effective approach for screening potential serum tumor markers.更多还原