利用质谱技术对肝癌组织中自然呈递的MAGE表位进行鉴定

来源 :世界华人消化杂志 | 被引量 : 0次 | 上传用户:yu_threestone
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目的:利用质谱检测技术和差异比较方法,对肝癌组织中自然呈递的黑色素瘤抗原基因(melanomaantigengene,MAGE)表位进行鉴定分析.方法:肝癌细胞和癌旁无瘤肝细胞取自同1例肝癌患者,弱酸洗涤法分离肝癌细胞和肝细胞表面所有肽段;利用表位预测法挑选出HLA—A2限制的MAGE-1,MAGE-3理论侯选肽作为质谱筛选目标;利用弱酸洗涤法从肝癌细胞和肝细胞表面分离肽段,这些肝癌细胞和肝细胞都来自同1例肝癌患者.然后用HPLC分离纯化这些肽段,并将二种细胞的各峰段(fractions)进行差异比较,挑选出肿瘤特异性的峰段进行质谱分析.结果:经表位预测,共选出80条目标肽.从肝癌细胞样品中检测出2条自然呈递的MAGE抗原肽:FLWGPRALV(MAGE-3271-279)和FPSLREAAL(MAGE-1294-302),他们分别来自HCC的峰45.246和峰34.801,m/z分别为1058.49和1003.62.结论:这是首次从肿瘤组织中分离、鉴定出自然呈递的MAGE抗原表位.本实验证实质谱检测法可以对组织中自然呈递的肿瘤相关抗原表位进行快速准确检测,而检测的准确性和高效性对于表位的鉴定和肿瘤疫苗的设计部是非常重要的. OBJECTIVE: To identify and identify naturally expressed melanoma antigengenes (MAGE) epitopes in hepatocellular carcinoma using mass spectrometry and differential methods.Methods: Hepatocellular carcinoma cells and non-cancerous adjacent hepatocytes were collected from the same liver cancer Patients, weak acid washing method to separate all the peptides on the surface of liver cancer cells and hepatocytes; HLA-A2-restricted MAGE-1, MAGE-3 candidate peptides were selected by epitope prediction method as screening targets; using weak acid washing method from liver cancer Peptides were isolated from the surface of cells and hepatocytes, and these hepatoma cells and hepatocytes were all from the same patient with liver cancer. The peptides were then separated and purified by HPLC, and the fractions of the two cells were compared for differences Tumor-specific peaks were analyzed by mass spectrometry.Results: According to the epitope prediction, a total of 80 target peptides were selected and two naturally presented MAGE antigen peptides: FLWGPRALV (MAGE-3271-279) and FPSLREAAL (MAGE-1294-302), respectively, from peak 45.246 and peak 34.801 of HCC with m / z of 1058.49 and 1003.62, respectively.Conclusion: This is the first time that a MAGE epitope has been isolated from tumor tissue and identified. Experiments confirmed MS detection method can quickly and accurately detect the natural tissue of tumor-associated antigen presenting epitopes and the detection accuracy and efficiency for epitope identification and design of the tumor vaccine is very important.
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