胰腺癌多药耐药性是影响其化疗疗效的主要原因，如何精确有效地预测胰腺癌多药耐药的发生和发展并指导临床是目前研究的难点和热点。胰腺癌多药耐药与ABC转运蛋白家族中ABCB1/MDR1、ABCC/MRP和ABCG2/BCRP的异常表达密切相关，相应启动子甲基化程度可作为预测胰腺癌多药耐药的独立指标，但缺乏精准定量检测方法和同步的动态研究。本课题拟运用甲基化定量检测新技术-甲基化特异性高分辨熔解分析法（MS-HRM）对多种胰腺癌细胞株ABC转运蛋白家族启动子甲基化进行同步定量检测，评估其预测胰腺癌原发性耐药的敏感性和特异性；动态监测吉西他滨低浓度梯度诱导胰腺癌细胞株产生获得性耐药过程中相应启动子甲基化程度的变化，研究其动态预测胰腺癌获得性耐药的科学性；通过移植瘤动物化疗模型，验证其在体内对药物疗效评估的准确性，探讨其评测肿瘤多药耐药的应用价值，为将来临床研究胰腺癌多药耐药分子病理检测提供实验依据。

Multidrug resistance (MDR) is one of the main causes of overall efficacy reduction during pancreatic cancer chemotherapy. The main focus is to find effective indicators and methods which could predict and evaluate MDR in pancreatic cancer. The abnormal expressions of ABC transporters family (ABCB1/MDR1, ABCC/MRP and ABCG2/BCRP) have confirmed that they are closely related to MDR in pancreatic cancer. Recent studies have found that low level of ABC transporters family promoter methylation in pancreatic cancer and could be an independent indicator for the evaluation. However, there is lack of further researches with methods to do accurate quantitative detection and dynamic simultaneous analysis. We intend to use a new technology, methylation-sensitive high resolution melting (MS-HRM), to detect promoter methylations of ABC transporters family quantitatively in different pancreatic cancer cell lines and evaluate the sensitivity and specificity of original multidrug resistance prediction. Using low concentration of gemcitabine, cell lines get acquired drug resistance, we then monitor the dynamic changes of ABC transporters family promoter methylation in the process. In order to test the possibility of acquired multidrug resistance prediction, we plan to built animal models with planted tumor which would be treated by chemotherapy and had the assession of drug efficacy. We will also do the tumors' biopsy several times before chemotherapy to detect ABC transporters family promoter methylation and predict the MDR in vivo environment. This study would be meaningful to molecular pathology diagnosis of pancreatic cancer, helpful in finding important parameters for MDR prediction and evaluation in future clinical trails.