Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition (i.e. multiomics data) and analysis (i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network (MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.

遗传、表观遗传和代谢改变都是癌症的标志。然而，表观基因组和代谢组都是体内高度复杂且动态的生物网络。表观基因组和代谢组之间的相互作用有助于形成一个对肿瘤微环境做出响应并拥有大量未知生物标志物和癌症治疗靶点的生物系统。从这个角度来看，我们首先回顾了高通量生物数据采集（即多组学数据）和分析（即计算工具）的现状，然后提出了基于这些的计算机模拟代谢和表观遗传调控网络（MER-Net）的概念目前的高通量方法。概念性 MER-Net 旨在通过生物过程观察、组学数据采集、网络信息分析以及与经过验证的数据库知识的集成来连接代谢组学和表观基因组网络。因此，MER-Net 可用于使用深度学习模型来整合和分析大型多组学网络来揭示新的潜在生物标志物和治疗靶点。我们建议 MER-Net 可以作为指导综合代谢组学和表观基因组学研究的工具，或者可以进行修改以使用多组学数据回答其他复杂的生物学和临床问题。