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采用蛋白质芯片和生物信息学方法从喉癌患者血清中筛选标志蛋白.采用SELDI(surfaced enhanced laser desorption/ionization)蛋白质芯片技术对33例喉癌(12例声门癌,18例声门上癌,3例声门下癌)病人血清和31例正常人血清蛋白质谱图进行了检测.PBSⅡ-C型蛋白质芯片阅读机读取数据,获得的结果采用Ciphergen公司的Biomarker Wizard和BiomarkerPattern软件分析.结果显示与正常人血清蛋白质谱相比时,喉癌病人血清中有16个差异蛋白,其中8个标志分子在病人血清中高表达,8个标志分子在病人血清中低表达.Biomarker Pattern软件在设定条件下自动选取上述标志分子中的两个蛋白质8153和2035 Da用于建立喉癌诊断的分类树模型.此分类树具有两层3个叶结点,可将96.9％的喉癌病人和96.7％正常人正确划分出来.实验证明利用蛋白质组学和生物信息学方法可从血清中筛选出喉癌相关的标志蛋白,而且蛋白质芯片技术对于发现和筛选血清中的喉癌标志蛋白是一种有效、快速的工具.
Screen proteins from laryngeal cancer patients using protein chip and bioinformatics methods.Methods 33 cases of laryngeal carcinoma (12 cases of glottis carcinoma, 18 cases of supraglottic carcinoma, 3 cases of subglottic cancer) serum samples and 31 cases of normal serum protein profiles were detected.PBS Ⅱ-C protein chip reader reads the data, the results obtained using Ciphergen’s Biomarker Wizard and BiomarkerPattern software analysis results show that There are 16 differential proteins in the serum of laryngeal cancer patients, including 8 markers in the serum of patients and 8 markers in the serum of patients, Two proteins, 8153 and 2035 Da, were selected automatically for establishing a taxonomic tree model for the diagnosis of laryngeal cancer.This tree has three leaf nodes in two layers, which can separate 96.9% of laryngeal cancer patients and 96.7% of normal subjects Correctly demarcated.Experiments show that using proteomics and bioinformatics methods can be selected from the serum of laryngeal cancer-related marker protein, and protein Sheet is an effective technique for screening and discovery laryngeal serum marker protein, quick tool.