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Volume 48 Issue 7
Jul.  2021
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Article Navigation >  Journal of Genetics and Genomics  > 2021  >  48(7): 520-530

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

doi: 10.1016/j.jgg.2021.05.008
  •  

    a The State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China;

  •  

    b Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

Funds:

This work was supported by the National Natural Science Foundation of China (81890994, 31871343), National Key Research and Development Program of China (2017YFA0505503, 2018YFB0704304, 2018YFA0801402), the WBE Liver Fibrosis Foundation (CFHPC 2020021), the Beijing Dongcheng District outstanding talent funding project and the Beijing Undergraduate Training Programs for Innovation and Entrepreneurship (202010023046).

  • Received Date: 2021-01-18
  • Accepted Date: 2021-05-11
  • Rev Recd Date: 2021-05-07
  • Publish Date: 2021-07-20
    Abstract

  • 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.

    • These authors contributed equally to this work.
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