血清蛋白质谱模型的建立及在乳腺癌诊断中的应用

Establishment of serum proteomic mass spectra model and its application in diagnosis of breast cancer

目的建立健康女性人群、乳腺癌患者血清蛋白质谱模型,并探讨对乳腺癌诊断的临床意义。方法应用表面加强激光解析电离化飞行时间质谱(SELDI-TOF-MS)技术,以弱阳离子交换芯片(CM10)结合血清蛋白质,分析60例乳腺癌患者、60例健康对照女性的血清样本,得到其蛋白质谱,用BioMarkerPattern软件分析蛋白质谱图,建立分类树模型并进行盲筛验证。结果乳腺癌患者与健康对照女性比较,筛选出12个差异蛋白质峰(P<0.001),其中6个标志蛋白呈高表达,6个标志蛋白呈低表达。BioMarkerPattern软件在设定条件下自动选取3个标志蛋白用于建立乳腺癌诊断的分类树模型,此模型灵敏度为91.90%,特异度为81.25%。结论利用SELDI-TOF-MS技术可筛选出乳腺癌患者和健康对照女性血清蛋白质谱存在的差异蛋白质峰,可作为乳腺癌检测、随访监测的指标。

Objective To establish the serum proteins mass spectra model in healthy controls and breast cancer patients, and evaluate its clinical significance in diagnosis of breast cancer. Methods SELDI-TOF-MS technology and protein chip （CM10） were used to detect mass spectra of serum samples including 60 cases of breast cancer and 60 cases of healthy controls. BioMarker Pattern Software （BPS） was used to analyze the protein peaks significantly different between them and establish a diagnostic pattern which was further valuated by a blind test. Results Twelve significantly different protein peaks were found in serum samples between breast cancer patients and healthy controls （P〈0. 001）. Among the total, the protein expression was high in six and low in the other six. The softwares BioMarker Pattern automatically, under given conditions, selected three biomarker proteins peaks to build up a taxonomic tree model with a specificity of 91.90% and a sensitivity of 81.25%. Conclusion Significantly different protein peaks can be screened out between breast cancer patients and healthy controls using SELDI-TOF-MS technology, and these protein peaks may he useful in detecting and monitoring recurrence of breast cancer.