【作者】 符方方；

【导师】 冯毅凡；

【作者基本信息】 广东药学院 ， 药物分析， 2011， 硕士

【摘要】 实验目的:初步建立基于1HNMR技术和的UPLC-Q/TOF(液质联用)技术代谢组学方法,并将该方法应用于乳腺癌患者血清中内源性代谢物的变化,寻找乳腺癌相关诊断标志物。在此基础上初步比较了化疗方案AC(阿霉素+环磷酰胺)干预前后乳腺癌患者血清中代谢物的变化,观察化疗方案干预后患者体内血清中内源性代谢物的变化趋势。研究方法:1.研究对象的纳入:选取体检结果正常的11例健康人作为正常对照组;选取符合纳入标准的16例初诊乳腺癌患者作为实验组。2.标本采集:采集11例正常女性静脉血,分离并收集血清;分别采集实验组16例乳腺癌患者在化疗前及化疗一个疗程后的静脉血,分离并收集3.建立基于1HNMR技术的血清代谢组学分析方法,包括实验方法和模式识别方法的选择。并将建立的方法正常女性和实验组化疗前血清的代谢组学分析,找出血清中代谢物的差异;同时将方法应用于AC方案干预前后乳腺癌患者血清中的代谢物变化。通过文献检索和数据查询找出血清中差异。4.建立基于液质联用技术(UPLC-Q/TOF)技术的血清代谢组学分析平台,并将建立的方法正常女性和实验组化疗前血清的代谢组学分析,找出差异;同时将方法应用于AC方案干预前后乳腺癌患者血清中的代谢物变化。和1HNMR技术研究结果进行比较。5.数据分析:通过多种分析统计分析方法对产生的海量数据进行分析,分析过程包括:归一化、修正80%规则、数据集分割和数据缩放等方法对数据集进行预处理;通过数据分析:采用主成分分析(principal componentanalysis,PCA)、正交偏最小二乘判别分析(OrthogonalPartial Least-Squares Discriminant Analysis,OPLS-DA)进行模式识别分析;根据模型的变量重要性因子(VIP值)、非参数检验结果和P值筛选潜在标记物。实验结果:1.建立了1H-NMR的血清代谢组学方法,健康女性和乳腺癌患者化疗前、化疗方案干预前后两个组经代谢组学方法得到很好的区分,并找到了相对应的16个乳腺癌肿瘤标志物。乳酸(lactate,1.33、4.12),缬氨酸(valine,0.99/1.04),N-乙酰糖蛋白(N-acetylgly-coproteins,2.04),谷氨酰胺(glutamine,2.41),磷酸胆碱/胆碱(phosphocholine/cholie,3.22),葡萄糖(a-glucose和B-glucose,3.40-3.90,4.66,5.22),酪氨酸(tyrosine,6.87,7.17),组氨酸(histidine), LDL/VLDL,不饱和脂肪酸等峰有增大或减小的表现。2.建立了UPLC-Q/TOF-MS的血清代谢组学研究方法,发现胆碱类和苯丙氨酸、异亮氨酸在正常人和乳腺癌患者化疗前有不同,其中化疗后的乳腺癌患者血清中的胆碱类有向正常变化单位趋势。1HNMR技术所得到的标记物和UPLC-Q/TOF得到的标记物大部分不同,但是分组聚类结果类似。结论:~1HNMR技术和UPLC-Q/TOF技术作为常用的代谢组学分析工具,在标记物发现中有一定的互补性。通过对正常人和乳腺癌患者化疗前后的代谢组学研究,得到了三者的代谢谱,发现本文所建立的NMR技术和UPLC-Q/TOF技术为基础的代谢组学方法应用于乳腺癌患者血清中具有良好的聚类效果。更多还原

【Abstract】 Objective:~1HNMR and UPLC-Q/TOF based metabolic profiling was used to investigate the differences of serum metabolic patterns between patients with Breast cancer and healthycontrols to find tentative diagnostic biomarkers.Based on the findings above,we tried to describe the metabolite changes related to the continuous phases ofradiotherapy.It will provide a novel technological platform for the mechanism study of Breast cancer.Methods:1.Patients:16 newly diagnosed patients with Breast cancer who match the selection conditions served as Breast cancer group.11 healthy persons served as control group.2.Sample collection:Serum samples were collected from 11 persons in the control group through venous blood.Serum samples of 16 patients in the Breast cancer group were collected before chemotherapy and two weeks after chemotherapy.3.NMR analysis of serum samples:The NMR technique based metabonomic method was established,and the serum was introduced to NMR.Applying the NMR metheod,the serum samples from the health and Breast cancer were collected and detected by the machine. The serum endogenous metabolites were identified.4. Eatabilished the approach with Ultra-performance-Liquid Chromatography- ElectroSpray Ionization-quatrupole-Time Of Flight mass spectrometry (UPLC-ESI-Q-TOF)for metabonomic study,and introduced the method to the collecded serum to judge the state of disease.5. Data analysis:Then the data was imported SIMCA-P+ software for denoising. Through a variety of analysis of statistical analysis method of analysis produced massive data,analyzing process including: through the normalization, correction 80% rules, data set and data zoom in a set of data pretreatment method; Through theanalysis of data: principal by principal component analysis (PCA), slant component analysis, the least squares discriminant analysis (discriminant squares - order extra transportation charges nothing, PLS - DA) two methods of the samples classification; According to the model of variable importance factor (VIP value) and non-parameter test results and z value screening potential markers.Results:1. Eatabilished the approach with NMR for metabonomic study, some specific serum endogenous metabolites changes in the metabolic composition of serum samples from Breast cancer,there are lactate,valine, N-acetylgly-coproteins, glutamine,phosphocholine/choline, a-glucose/B-glucose,tyrosine,histidine,LDL/VLDL.2. Eatabilished the approach with for metabonomic study.Examination of the metabonomic data obtained by Ultra-performance-Liquid Chromatography -ElectroSpray Ionization-quatrupole-Time Of Flight mass spectrometry (UPLC-ESI-Q-TOF) ,the acquired metabonomic data was analyzed by multivariate analysis.Significant difference in endogenous metabolite profilea was observed in the control and patients with chemtheropy before and after.Conclusions:The relusts demonstrated that metabonomics is a potential and powerful tool for characterization of the metabolic perturbation under different physiopathologic status,and also indicated that the metabonomic analysis is promising to provide an integrative criterion to evaluate the severity of the Breast cancer and the prognosis.更多还原