摘要
目的：应用基质辅助激光解析电离飞行时间质谱(Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry,MALDI-TOF-MS)联合磁珠技术建立乳腺癌诊断模型,发现乳腺癌特异性肿瘤标志物并鉴定蛋白和验证。
方法：收集32例乳腺癌患者和59例正常人的血清,高速离心后-80℃冰箱保存。样本在一个冻融周期内同批进行MALDI-TOF-MS联合磁珠分析,应用Biomarker Wizard软件识别蛋白峰信息,用SPSS13.0软件对数据进行统计学分析,然后应用Biomarker Patterns软件建立乳腺癌诊断模型并确定目标蛋白。应用磁珠纯化蛋白后用Tricine-SDS-PAGE电泳分离蛋白,然后用肽质量指纹图谱对目标蛋白进行鉴定,最后用免疫沉淀法对已鉴定蛋白进行验证。
结果：通过软件分析找到136个符合条件的差异蛋白峰,并用其中的三个,即M11692.4,M15111.4和M15909.8建立了乳腺癌的诊断模型,敏感性达到96.875%,特异性达到98.305%。磁珠纯化后的蛋白浓度达到8.33ug/μl,达到电泳上样量的标准。应用Tricine-SDS-PAGE电泳成功分离目标蛋白,并成功鉴定三个目标蛋白,分别为人血红蛋白α和β链以及人血清淀粉样蛋白A(SAA),最后用免疫沉淀方法成功验证了人血红蛋白α链和SAA。
结论：本研究建立的树形分类模型能够以高敏感性和特异性鉴别乳腺癌患者·和正常对照者,可以做为乳腺癌早期诊断方法。成功分离鉴定出了用于建模的三个蛋白质,并得到验证,可以将这三个蛋白用于乳腺癌的诊断和病情监测。
Objective To establish the serum proteins diagnosis model and identify serum protein biomarkers specific for breast cancer with Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry(MALDI-TOF-MS) combined with magnetic beads technology.
Methods A total of59samples from healthy controls and32samples from Breast Cancer patients before operation were collected, high speed centrifugated and frozen at-80℃until thawed specifically for MALDI-TOF-MS combined with magnetic beads analysis. Proteomic fingerprinting of serum were identified and analyzed by Biomarker Wizard Software and Biomarker Patterns Software.Software,SPSS13.0will be used for statistical analysis.Then the diagnosis model will be established with BPS5.0software. A decision tree model that differentiate Breast Cancer patients from healthy controls was selected, and the biomarkers specific for Breast Cancer was determined also. After purification by magnetic beads technology and seperation by the Tricine-SDS-PAGE1D Electrophoresis, the serum protein biomarkers specific for breast cancer will be identified by PMF, at last the biomarkers will be proved by immunoprecipitation.
Results.136protein peaks of significant difference were found and3discriminative peaks were generated by Biomarker Patterns Software to establish the diagnosis model for Breast Cancer. The modle was with96.875%of the sensitivity and98.305%of the specificity. The following three peaks were highlighted:m/z values of11692.4Da15111.4Da and15909.8Da. By the concentration of8.33ug/ul, which was enough for the electrophoresis, the three biomarkers for Breast Cancer was finally identified, and they are human hemoglobin α、β chain and human serum amyloid A protein (SAA), the hemoglobin a chain and SAA were proved successfully by immunoprecipitation.
Conclusions Our discriminative model could recognize Breast Cancer patients from healthy controls with satisfying sensitivity and specificity. Three specific protein biomarkers for breast cancer have been identified and proved successfully, and the three biomarkers could be used for early diagnosis and desease monitoring for Breast Cancer.
引文
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