血清蛋白质谱结合人工神经网络在胃癌诊断中的研究

目的 建立筛选胃癌血清蛋白质谱的人工神经网络(ANN)诊断模型.方法 将84例胃癌患者和75例对照者的血清样本按照随机数字表法随机分为训练集(106例)和测试集(53例).首先应用表面加强激光解吸电离-飞行时间质谱(SELDI-TOF-MS)技术及弱阳离子交换表面芯片(CMl0)检测练集样本,结合反向传播ANN的方法建立诊断模型,进一步检测测试集样本并评价该模型的诊断价值.结果 胃癌患者与对照者血清蛋白质谱图有5个明显表达差异的蛋白质峰(P<0.05),质荷比分别为7567、6742、5262、4869、4256 m/z,5个蛋白质峰作为标志蛋白建立ANN诊断模型.利用该模型对胃癌患者进行盲法预测,结果表明其对胃癌的诊断灵敏度和特异度分别为90.0%和91.3%.结论 胃癌血清蛋白质谱结合ANN建立的诊断模型对胃癌诊断具有较高的灵敏度和特异度,可用于胃癌早期诊断与肿瘤标志物筛选研究.

Serum diagnosis of gastric cancer using surface-enhanced desorption ionization mass spectrometry and artificial neural network analyses

Objective To develop an artificial neural networks tool and use it to identify proteomic patterns in serum so as to distinguish gastric cancer from controls. Methods Serum samples from 84 gastric cancer patients and 75 controls were randomized into training set (106 samples) and test set (53 samples). At first, samples of the training set were detected using SELDI mass spectrometry and CMIO protein chips. Using a multi-layer ANN with a back propagation algorithm, a proteomic pattern that could distinguish cancer from control samples was identified in the training set. The discovered pattern was then used to determine the accuracy of the classification system in the test set. Results Totally 5 differentially expressed proteins between patients and controls were identified. The five proteins (P < 0.05, m/z at 7567,6742,5262,4869, 4256) were chosen to develop ANN based diagnostic model. The model was blindly tested in the test set for diagnosing gastric cancer. The sensitivity and specificity was 90.0% and 91.3% respectively. Conclusions Combination of SELDI with the artificial neural networks can get a high sensitivity and specificity approach to identify the gastric cancer from the controls. The method shows great potential for early diagnosis of gastric cancer and screening of new tumor biomarkers.