Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers.
Identifying the pre-transition state just before a critical transition during a complex biological process is a challenging task, because the state of the system may show neither apparent changes nor clear phenomena before this critical transition during the biological process. By exploring rich correlation information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-transition state. In this work, we apply DNB to detect an early-warning signal of breast cancer on the basis of gene expression data of MCF-7 cell differentiation. We find a number of the related modules and pathways in the samples, which can be used not only as the biomarkers of cancer cells but also as the drug targets. Both functional and pathway enrichment analyses validate the results.
School of Computer Science, South China University of Technology Guangzhou, China.
Pei
P
Chen
School of Mathematics, South China University of Technology Guangzhou, China.
Rui
R
Liu
Collaborative Research Center for Innovative Mathematical Modelling, University of Tokyo Tokyo, Japan ; Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences Shanghai, China.
Luonan
L
Chen
Collaborative Research Center for Innovative Mathematical Modelling, University of Tokyo Tokyo, Japan.
Kazuyuki
K
Aihara
breast cancer; cell differentiation; critical transition; dynamical network biomarker (DNB); early-warning signal; pre-transition state