应用液体蛋白芯片飞行时间质谱系统研究胃癌的血清蛋白差异表达谱*

目的:应用液体蛋白芯片飞行时间质谱系统分析胃癌患者血清蛋白质表达谱，寻找具有潜在诊断意义的血清标志物。方法:收集血清样本62 例，其中正常对照组（N 组）16 例，胃癌组（T 组）28 例，验证组18 例。经WCX磁珠纯化、MALDI-TOF-MS 及ClinproTools生物信息学方法研究其血清蛋白表达谱，并筛选出差异蛋白质峰，运用数据挖掘算法，构建胃癌的血清蛋白诊断模型，并在验证组中验证其准确性。结果:1）通过对比胃癌组和正常组的血清蛋白质谱图，分析得到 25 个具有显著差异的蛋白质峰，其中差异最显著的前两位质核比分别为 5 248.49 m/z和5 754.25 m/z，其灵敏度分别为 84 .61 %和73 .07 %，特异性分别为 100%和93 .75 %，能很好地区分胃癌组和正常组。2）通过 ANN 的数据挖掘的方法，在具有显著差别的 25 个蛋白质峰中，筛选了组合能力最强的6 个蛋白峰（分别为4 268.05 m/z、5 636.53 m/z、5 248.49 m/z、2 933.15 m/z、1 450.13 m/z和1 349.4m/z），建立了胃恶性肿瘤的诊断模型，其识别率为100%，预测能力为 90 .59 %，准确性为 100%。将已知信息的验证组 18 例分别代入已建立的模型，特异性和灵敏性分别为75 %和100%。结论:液体蛋白芯片飞行时间质谱系统作为研究蛋白表达谱的工具，能够用于筛选潜在的胃恶性肿瘤的血清标志物，利用其优点并结合统计学的方法，建立血清学胃癌的诊断模型，能为胃恶性肿瘤的筛查提供帮助。 

Proteomic Profiling of Gastric Cancer in Serum Using Magnetic Bead Based Sample Fractionation and MALDI-TOF-MS

Objective: To search for potential serum biomarkers for gastric cancer screening using serum proteomic based technologies. Methods:Serum samples from 16healthy volunteers (N group),28gastric cancer patients (T group), and 18controls were collected. The differentially expressed peaks of serum proteins were found and selected by the WCX magnetic bead assisted MALDI-TOF-MS technique and ClinProTool software. These data were then used to build a diag-nostic model for gastric cancer. Results:A total of 25peaks of serum proteins were found to be differentially expressed be -tween the tumor group and the control group ( P<0.01). Two of them (m/z: 5248.49and 5754.25) were selected as the po -tential cancer biomarkers by ClinProTool software (sensitivity 84.61% and73.07% , respectively; specificity100 % and 93.75% , respectively). According to the ANN, we selected6 peaks of protein (4268.05m/z, 5636.53m/z, 5248.49m/z, 2933.15m/z, 1450.13m/z and 1349.4 m/z) to build a diagnostic model for gastric cancer. The sensitivity and specificity of this model were 100 % and 75%, respectively. Conclusion:Specific protein markers of gastric cancer can be detected in se-rum by magnetic bead assisted MALDI-TOF-MS. A classification model of serum proteomics could be created for diagnosis of gastric cancer.