期刊
METHODS
卷 203, 期 -, 页码 125-138出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2022.04.002
关键词
M(6)A methylation; Driver gene; Heterogeneous network; Random walk with restart; Heat diffusion
资金
- China Postdoctoral Science Foundation [2020M683568]
- National Natural Science Foundation of China [61873202, 62173271]
Researchers propose a novel network-based approach (m(6)Acancer-Net) to identify m(6)A-mediated driver genes and their associated networks in specific cancers. By integrating multiple data sources, this method can reliably identify functionally significant m(6)A-mediated driver genes in specific cancers, facilitating a deeper understanding of the regulatory and therapeutic mechanisms of cancer driver genes at the epitranscriptome level.
N-6-methyladenosine (m(6)A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m(6)A-mediated functional circuit and key genes targeted by m(6)A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m(6)Acancer-Net) to identify m(6)A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m(6)A-mediated cancer driver genes are defined as genes mediated by m(6)A methylation, significantly mutated, and functionally interacted in cancer. m(6)Acancer-Net identified the m(6)A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m(6)A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m(6)A-mediated genes were identified by selecting the m(6)A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m(6)A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m(6)A-mediated genes in protein-protein interaction networks. The experimental results of m(6)AcancerNet on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m(6)A-mediated caner driver genes identified by m(6)Acancer-Net are targeted by m(6)A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m(6)A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m(6)A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m(6)Acancer-Net is reliable to identify the functionally significant m(6)A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer.
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