Metabolic characterization and pathway analysis of berberine protects against prostate cancer
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Xianna Li1,*, Aihua Zhang1,*, Hui Sun1, Zhidong Liu1, Tianlei Zhang1, Shi Qiu1, Liang Liu2 and Xijun Wang1,2
1Sino-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
2State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau
*These authors contributed equally to this work
Xijun Wang, email: email@example.com
Keywords: prostate cancer, metabolomics, pathway analysis, metabolome, UPLC-Q/TOF-MS
Received: March 27, 2017 Accepted: April 17, 2017 Published: April 28, 2017
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.
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