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代谢组学的创新以改善个性化医疗。

Innovation in metabolomics to improve personalized healthcare.

作者信息

Cacciatore Stefano, Loda Massimo

机构信息

Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.

Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

出版信息

Ann N Y Acad Sci. 2015 Jun;1346(1):57-62. doi: 10.1111/nyas.12775. Epub 2015 May 26.

Abstract

Metabolomics is the systemic study of all small molecules (metabolites) and their concentration as affected by pathological and physiological alterations or environmental or other factors. Metabolic alterations represent a "window" on the complex interactions between genetic expression, enzyme activity, and metabolic reactions. Techniques, including nuclear magnetic resonance spectroscopy, mass spectrometry, Fourier-transform infrared, and Raman spectroscopy, have led to significant advances in metabolomics. The field is shifting from feasibility studies to biological and clinical applications. Fields of application range from cancer biology to stem cell research and assessment of xenobiotics and drugs in tissues and single cells. Cross-validation across high-throughput platforms has allowed findings from expression profiling to be confirmed with metabolomics. Specific genetic alterations appear to drive unique metabolic programs. These, in turn, can be used as biomarkers of genetic subtypes of prostate cancer or as discovery tools for therapeutic targeting of metabolic enzymes. Thus, metabolites in blood may serve as biomarkers of tumor state, including inferring driving oncogenes. Novel applications such as these suggest that metabolic profiling may be utilized in refining personalized medicine.

摘要

代谢组学是对所有小分子(代谢物)及其浓度受病理和生理改变或环境及其他因素影响的系统研究。代谢改变代表了基因表达、酶活性和代谢反应之间复杂相互作用的一个“窗口”。包括核磁共振光谱、质谱、傅里叶变换红外光谱和拉曼光谱在内的技术,已使代谢组学取得了重大进展。该领域正从可行性研究转向生物和临床应用。应用领域涵盖从癌症生物学到干细胞研究以及对组织和单细胞中外源生物和药物的评估。高通量平台之间的交叉验证使得表达谱分析的结果能够通过代谢组学得到证实。特定的基因改变似乎驱动着独特的代谢程序。这些反过来又可作为前列腺癌基因亚型的生物标志物,或作为代谢酶治疗靶点的发现工具。因此,血液中的代谢物可能作为肿瘤状态的生物标志物,包括推断驱动癌基因。诸如此类的新应用表明,代谢谱分析可用于完善个性化医疗。

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