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Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer

1 School of Life Science, Sun Yat-sen University, Guangzhou 510275, China
2 Key Laboratory of Tropical Disease Control, Chinese Ministry of Education, Zhong-Shan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China

Special Issues: Systems biology: Modeling of dynamical diseases and cancer

Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.
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Keywords clinical transcriptomic data; colorectal cancer; dynamic gene regulatory network; reconstruction

Citation: Ancheng Deng, Xiaoqiang Sun. Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer. Mathematical Biosciences and Engineering, 2020, 17(4): 3224-3239. doi: 10.3934/mbe.2020183


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