摘要目的 应用表面增强激光解析电离飞行时间质谱技术(SELDI-TOF-MS)筛选蕈样肉芽肿患者血清特异性蛋白.方法 用CM10(弱阳离子交换表面)蛋白芯片结合SELDI-TOF-MS技术,检测15例蕈样肉芽肿和15例对照组慢性湿疹、神经性皮炎患者血清中的蛋白质谱,用浙江大学肿瘤研究所蛋白芯片数据分析系统筛选与慢性湿疹、神经性皮炎有差异的蛋白.结果 从15例蕈样肉芽肿与15例慢性湿疹、神经性皮炎患者的血清蛋白质谱中共检测出329个蛋白峰,从中筛选出30个表达差异有统计学意义(P＜0.01)的蛋白标志物.用支持向量机算法结合其中两个蛋白峰(3939,5909)可得到最佳诊断模型,模型训练时的敏感性和特异性均为100%,留一法交叉验证和测试的敏感性和特异性也为100%.结论 血清蛋白质谱联合生物信息学分析方法对蕈样肉芽肿的诊断具有较高的敏感性和特异性.

abstractsObjective To characterize mycosis fungoides (MF)-associated serum proteins by surface enhanced laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS) technique. Methods Serum samples were collected following informed consent from 15 patients with MF and 15 patients with chronic eczema or neurodermatitis. Serum protein profiles were detected by CM10 protein chip combined with SELDI-TOF-MS. The proteins differentially expressed between MF and chronic eczema or neuroder matitis were assessed by the Zhejiang University Cancer Institute - ProteinChip Data Analysis System (ZUCIPDAS). Results A total of 329 protein peaks were detected from these patients, significant difference was observed in only 30 protein peaks. The optimal diagnostic model was developed by support vector machine algorithm with two protein peaks at Mass/Charge (m/z) 3939 and 5909 respectively. The expression of protein peak at m/z 3939 was higher in chronic eczema and neurodermatitis than in MF, while that at m/z 5909 appeared to express in an opposite way. In simulation training, this model proved to be able to distinguish MF from chronic eczema and neurodermatitis with both the specifity and sensitivity being 100 percent. The leave-one-out cross-validation also revealed a specificity and sensitivity of 100 percent for this model in the comparison of MF with chronic eczema and neurodermatitis. Conclusion These results suggest that SELDITOF-MS technique combined with bioinformatics is highly specific and sensitive in the diagnosis of MF.