唾液蛋白指纹图谱在乳腺癌诊断中的应用

Application of Salivary Protein Fingerprint Model in Diagnosis of Breast Cancer

目的:检测乳腺癌患者唾液蛋白质,筛选特异的蛋白质标记物,构建用于乳腺癌早期诊断的唾液蛋白质指纹图谱模型。方法:应用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术测定91例唾液标本(其中乳腺癌47例,健康人44例)的蛋白质质谱,建立乳腺癌诊断模型。结果:乳腺癌患者与健康对照样本检测到311个蛋白质峰,其中32个蛋白质峰在两组间差异有统计学意义。获得分子量为4849.31,5224.96,3439.02和3559.89Da4个蛋白质组成的模板,可将乳腺癌与正常人正确分组,47例乳腺癌有37例被准确诊断,34例健康人被准确排除,灵敏度78.7%(37/47);特异度为77.2%(34/44)。结论:表面增强激光解吸电离飞行时间质谱技术建立乳腺癌癌唾液蛋白质指纹图谱模型为早期筛查及诊断乳腺癌提供了一种特异性强、敏感性高的新方法,值得进一步研究和应用。

Objetive To find new biomarkers and to establish salivary protein fingerprint models for early detection and diagnosis of breast cancer. Method 91 salivary samples （including 47 cases of breast cancer and 44 healthy inviduals） were tested by SELDI-TOF-MS, the data of spectra were analyzed by bioinformatics tools and discriminant analysis to establish diagnosis model. Results It have detected 311 protein peaks including 32 protein peaks which have significant differences between breast cancer and healthy control. The detective model combined with 4 biomakers could differentiate breast cancer from healthy individuals with specificity of 78.7% and the sensitivity of 77.2%. Conclusion Salivary protein fingerprint model is a novel effective method for breast cancer dectection and diagnosis by SELDI-TOF-MS.