Li Xianna, Zhang Aihua, Sun Hui, Liu Zhidong, Zhang Tianlei, Qiu Shi, Liu Liang, Wang Xijun
Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Chinmedomics Research Center of TCM State Administration, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China.
State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau.
Oncotarget. 2017 Apr 28;8(39):65022-65041. doi: 10.18632/oncotarget.17531. eCollection 2017 Sep 12.
Recent explosion of biological data brings a great challenge for the traditional methods. With increasing scale of large data sets, much advanced tools are required for the depth interpretation problems. As a rapid-developing technology, metabolomics can provide a useful method to discover the pathogenesis of diseases. This study was explored the dynamic changes of metabolic profiling in cells model and Balb/C nude-mouse model of prostate cancer, to clarify the therapeutic mechanism of berberine, as a case study. Here, we report the findings of comprehensive metabolomic investigation of berberine on prostate cancer by high-throughput ultra performance liquid chromatography-mass spectrometry coupled with pattern recognition methods and network pathway analysis. A total of 30 metabolite biomarkers in blood and 14 metabolites in prostate cancer cell were found from large-scale biological data sets (serum and cell metabolome), respectively. We have constructed a comprehensive metabolic characterization network of berberine to protect against prostate cancer. Furthermore, the results showed that berberine could provide satisfactory effects on prostate cancer via regulating the perturbed pathway. Overall, these findings illustrated the power of the ultra performance liquid chromatography-mass spectrometry with the pattern recognition analysis for large-scale biological data sets may be promising to yield a valuable tool that insight into the drug action mechanisms and drug discovery as well as help guide testable predictions.
近期生物数据的爆炸式增长给传统方法带来了巨大挑战。随着大数据集规模的不断扩大，深度解读问题需要更先进的工具。作为一项快速发展的技术，代谢组学能够为发现疾病发病机制提供有用的方法。本研究以黄连素为例，探讨前列腺癌细胞模型和Balb/C裸鼠模型中代谢谱的动态变化，以阐明黄连素的治疗机制。在此，我们报告了通过高通量超高效液相色谱-质谱联用模式识别方法和网络通路分析对黄连素抗前列腺癌进行全面代谢组学研究的结果。分别从大规模生物数据集（血清和细胞代谢组）中发现了血液中的30种代谢物生物标志物和前列腺癌细胞中的14种代谢物。我们构建了黄连素预防前列腺癌的综合代谢特征网络。此外，结果表明黄连素可通过调节紊乱的通路对前列腺癌产生满意的效果。总体而言，这些发现表明超高效液相色谱-质谱联用模式识别分析对大规模生物数据集的作用有望产生一个有价值的工具，用于深入了解药物作用机制和药物发现，并有助于指导可验证的预测。
