Molecular genetic approaches are now mandatory for cancer diagnostics, especially for brain tumors. Genotype-based diagnosis has predominated over the phenotype-based approach, with its prognostic and predictive powers. However, comprehensive genetic testing would be difficult to perform in the clinical setting, and translational research is required to histologically decipher the peculiar biology of cancer. Of interest, recent studies have demonstrated discrete links between oncogenotypes and the resultant metabolic phenotypes, revealing cancer metabolism as a promising histologic surrogate to reveal specific characteristics of each cancer type and indicate the best way to manage cancer patients. Here, we provide an overview of our research progress to work on cancer metabolism, with a particular focus on the genomically well-characterized malignant tumor glioblastoma. With the use of clinically relevant animal models and human tissue, we found that metabolic reprogramming plays a major role in the aggressive cancer biology by conferring therapeutic resistance to cancer cells and rewiring their epigenomic landscapes. We further discuss our future endeavor to establish “metabolism-based pathology” on how the basic knowledge of cancer metabolism could be leveraged to improve the management of patients by linking cancer cell genotype, epigenotype, and phenotype through metabolic reprogramming.

中文翻译：

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胶质母细胞瘤发病机制中的代谢和表观遗传重编程：建立“基于代谢的病理学”

分子遗传学方法现在是癌症诊断的必备方法，尤其是脑肿瘤。基于基因型的诊断因其预后和预测能力而优于基于表型的方法。然而，在临床环境中很难进行全面的基因检测，并且需要转化研究来从组织学上破译癌症的特殊生物学。有趣的是，最近的研究证明了癌基因型和由此产生的代谢表型之间的离散联系，揭示了癌症代谢作为一种有前途的组织学替代物，可以揭示每种癌症类型的具体特征，并指出治疗癌症患者的最佳方法。在这里，我们概述了我们在癌症代谢方面的研究进展，特别关注基因组学特征明确的恶性肿瘤胶质母细胞瘤。通过使用临床相关的动物模型和人体组织，我们发现代谢重编程通过赋予癌细胞治疗抗性并重新连接其表观基因组景观，在侵袭性癌症生物学中发挥着重要作用。我们进一步讨论了未来建立“基于代谢的病理学”的努力，即如何利用癌症代谢的基础知识，通过代谢重编程将癌细胞基因型、表观基因型和表型联系起来，从而改善患者的管理。