蛋白质芯片技术在前列腺癌诊断中的应用

Proteomic analysis of prostate cancer using surface enhanced laser desorption/ionization mass spectrometry

目的通过应用蛋白质芯片和生物信息学技术筛选前列腺癌患者的血清标志蛋白,诊断早期前列腺癌。方法以集团普查诊断的83例前列腺癌患者血清和95例正常人血清为研究对象。采用SELDI(surfaced enhanced laser desorption/ionization)蛋白质芯片技术检测血清的蛋白质谱。以蛋白质芯片阅读机读取谱图数据,后者应用Biomarker Wizard和Biomarker Pattern软件进行分析比较。结果与正常人血清蛋白质谱比较发现,前列腺癌患者血清有18个标志蛋白,其中4个标志蛋白呈高表达(15265,15868,16003,16068),14个标志蛋白呈低表达。BiomarkerWizard和BiomarkerPattern软件在设定条件下自动选取8个标志蛋白用于建立前列腺癌诊断的分类树模型。此模型可正确划分96.386%的前列腺癌患者和92.632%的正常人。结论SELDI蛋白质芯片技术可筛选出前列腺癌标志蛋白并建立前列腺癌诊断分类树模型,可能成为前列腺癌诊断的有效方法。

Objective To identify the serum biomarkers of prostate cancer by using protein chip and bioinformatics. Methods Eighty three prostate cancer （PCA） patients and ninety five healthy people from mass screen in Changchun were detected by surface-enhanced laser desorption/ionization mass spectrometry （SELDI-MS）. The data of spectra were analyzed by bioinformatics tools-Biomarker Wizard and Biomarker Pattern. Results Compared with the spectra of healthy people, there were 18 potential markers detected in the spectra of the PCA patients, the protein expression was high in 4 of which and low in the 10 of which. The softwares Biomarkerwizard and Biomarker Pattern automatically, under given conditions, selected 8 biomarker proteins to be used to establish a five layer decision tree differentiate to diagnose PCA and differentiate PCA from healthy people with a specificity of 92. 632% and a sensitivity of 96. 386%. Conclusion New serum biomarkers of PCA have been identified, and this SELDI mass spectrometry coupled with decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCA.