摘要
目的对河南省林州地区食管癌高发区食管鳞癌患者(食管癌组)和健康人血清(对照组)蛋白质组进行比较研究,确立食管癌的蛋白质指纹诊断模型,并筛选与食管癌淋巴结转移相关的蛋白质。方法采用IMAC3芯片及表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)检测61例食管癌患者和50例健康人血清,用Bio-MarkerWizard软件对芯片检测得到的蛋白质相对含量进行处理及方差分析。并对25例发生淋巴结转移的食管癌患者(转移组),36例无淋巴结转移患者(未转移组)与对照组进行了对比分析。结果以相对分子质量(Mr)为9439.58、6627.21、2867.65、4494.08、7762.68、6835.32、4095.947种蛋白质组建立的决策树模型,对食管癌测试的准确率、敏感度和特异度分别为85.6%、88.5%和82.0%。转移组、未转移组与对照组3组相比,有15种蛋白差异有统计学意义。其中转移组和未转移组相比有2种蛋白差异有统计学意义(Mr分别为11742.48、9294.44)。结论以上述7种蛋白质建立的诊断模型用于食管癌的诊断,具有较高灵敏度和特异度;筛选出2种与食管癌转移密切相关蛋白质,后者为进一步建立食管癌转移相关肿瘤标志物提供了重要线索。
Aim : To establish the proteomic model for esophageal cancer (EC) diagnosis and to identify the biomarker associated with lymph node metastasis through proteomic analysis on serum from esophageal squamous cell carcinoma (SCC) patients and healthy control. Methods:IMAC3 chips and SELDI-TOF-MS methods were applied to serum samples from SCC patients (n = 61 ) and healthy control (n = 50). The proteomic data were analyzed by BioMarker Wizard Software. The proteomic analysis was also performed on serum from SCC patients with lymph node metastasis (n = 25 ) and those without lymph node metastasis (n = 36). Results: Seven tumor markers (Mr = 9 439.58, 6 627.21, 2 867.65, 4 494.08, 7 762.68, 6 835.32 and 4 095.94) were identified to discriminate the patients with EC and healthy control with veracity, sensitivity and specificity of 85.6% , 88.5% and 82.0% , respectively. There were 15 different proteins in the patients with and without lymph nodes metastasis and healthy control. Two different proteins (Mr = 11 742.48 and 9 294.44) were verified to discriminate SCC patients with and without lymph node metastasis. Conclusion : Diagnosis model with seven protein markers has higher sensitivity and specificity for SCC. The proteins (Mr = 11 742.48 and 9 294.44) may be related with EC metastasis, which provides important clues for establishing EC metastasis associated biomarkers.
出处
《郑州大学学报(医学版)》
CAS
北大核心
2007年第3期397-399,共3页
Journal of Zhengzhou University(Medical Sciences)
基金
河南省医学创新人才工程基金资助项目200084
国家自然科学基金资助项目30025016
国家科技部"863"重大基金资助项目2007