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Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery

机译:人类乳腺癌的代谢组学:肿瘤分型和生物标志物发现的新方法

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摘要

Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development.
机译:乳腺癌是全世界女性中最常见的癌症,因此开发新技术以更好地了解与乳腺癌进展有关的分子变化至关重要。代谢变化先于明显的表型变化,因为细胞调节最终会影响小分子底物用于细胞分裂,生长或环境变化(例如缺氧)的使用。已经发现正常细胞和癌细胞之间的代谢差异。由于酶浓度或活性的微小变化会导致整体代谢物水平发生较大变化,因此代谢组可被视为生物系统的放大输出。通过结合不同的代谢谱图技术,可以最大限度地提高人乳腺癌组织中的代谢组覆盖率。研究人员正在研究代谢物稳态浓度的变化,这些变化反映了代谢遗传控制中的放大变化。代谢组学结果可用于基于肿瘤生物学对乳腺癌进行分类,识别新的预后和预测指标以及发现新的靶标以用于未来的治疗干预。在这里,我们检查了最近的结果,包括来自欧洲FP7项目METAcancer财团的结果,这些结果表明,集成的代谢组学分析可以提供有关乳腺肿瘤的阶段,亚型和等级的信息,并提供机械学见解。我们预测在临床和临床前研究中集中使用代谢组学筛查,重点是肿瘤发展的发生和发展。

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