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目的:采用蛋白质芯片表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术检测宫颈癌病人血清蛋白指纹图谱,通过差异蛋白组学筛选特有的蛋白标记物。方法:应用SELDI-TOF-MS技术和WCX2(弱阳离子)芯片采集58例宫颈癌患者和57例健康人血清蛋白质指纹图谱,采用Biomarker Wizard软件筛选差异蛋白质组。将115例血清随机分为两组:以训练组30例宫颈癌患者和30例健康人建立人工神经网络(ANN)模型,以验证组28例宫颈癌患者和27例健康人血清标本用于模型的双盲法验证。结果:宫颈癌患者与对照组血清蛋白质指纹图谱有145个差异表达的蛋白质峰(P<0.05),筛选出质荷比(M/Z)分别为5912、5642、8702、4320、6432的标志蛋白(P<10-6),建立人工神经网络模型,其对宫颈癌的诊断敏感性为92.86%,特异性为88.89%,阳性预测值为89.66%,阴性预测值为92.31%。结论:特征蛋白在宫颈癌患者较正常人血清明显的高表达或低表达,可能对宫颈癌的早期诊断和治疗后随访具有重要的指导意义。
OBJECTIVE: To detect serum protein fingerprints of cervical cancer patients by SELDI-TOF-MS and screen specific protein markers by differential proteomics. Methods: Serum protein fingerprints of 58 patients with cervical cancer and 57 healthy controls were collected by SELDI-TOF-MS and WCX2 (weak cation) chip. The differential proteome was screened by Biomarker Wizard software. 115 cases of serum were randomly divided into two groups: Artificial neural network (ANN) model was established in 30 cases of cervical cancer patients and 30 healthy people in training group to verify the serum samples of 28 cases of cervical cancer and 27 healthy people in model group Double-blind method of verification. Results: There were 145 differentially expressed protein peaks (P <0.05) in the serum protein fingerprints of patients with cervical cancer and controls. The M / Z markers were 5912, 5664, 8702, 4320 and 6432 (P <10 -6). The artificial neural network model was established. The sensitivity and specificity of the method for diagnosing cervical cancer were 92.86%, 88.89%, 89.66%, and 92.31%, respectively. Conclusion: The characteristic proteins in cervical cancer patients significantly higher than normal human serum expression or low expression may be early diagnosis of cervical cancer and follow-up after treatment has an important guiding significance.