Biomedica Molecular Medicine SL, C/ Faraday, 7, 28049, Madrid, Spain.
Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain.
BMC Cancer. 2020 Apr 15;20(1):307. doi: 10.1186/s12885-020-06764-x.
Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances.
In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways.
On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient's clinical outcome.
Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.
代谢组学在癌症新生物标志物的开发中有很大的潜力，并经历了最近的技术进步。
本研究分析了 67 个局部（I 期至 IIIB 期）乳腺癌肿瘤样本的代谢组学和基因表达数据，使用 (1) 概率图模型来定义使用定量数据的关联，而无需其他先验信息；和 (2) 通量平衡分析和通量活性来描述代谢途径的差异。
一方面，这两种分析都强调了谷氨酰胺在乳腺癌中的重要性。此外，细胞实验表明，用靶向谷氨酰胺代谢的药物治疗乳腺癌细胞会显著影响细胞活力。另一方面，这些计算方法提出了一些假设，并证明了它们在代谢组学数据分析和将代谢组学与患者临床结果相关联方面的实用性。
应用于代谢组学数据的计算分析表明，谷氨酰胺代谢是乳腺癌的一个重要过程。细胞实验证实了这一假设。此外，这些计算分析允许将代谢组学数据与患者预后相关联。
