A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Alexis Hernández-Magaña, Antonio Bensussen, Juan Carlos Martínez-García, Elena R Álvarez-Buylla
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Abstract

In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition.

Abstract Image

布尔模型解释了肝癌干细胞出现的表型可塑性变化。
在包括肝细胞癌在内的多种癌症中,癌症干细胞(CSCs)与分化癌细胞相比,具有更强的侵袭性和耐药性。数学计算工具对于整合实验结果和理解 CSCs 出现的表型可塑性机制很有价值。在文献综述的基础上,我们构建了一个布尔模型,恢复了肝细胞和间充质细胞在衰老、静止、增殖和干样状态下基因表达谱的八个稳定状态(吸引子)。我们分析了与调控网络相关的表观遗传景观。我们观察到,p53、p16、RB 的缺失或β-catenin 和 YAP1 的组成性激活会增加增殖干样表型的稳健性。此外,我们还发现,p53 失活促进了增殖性肝细胞向干样间质表型的转变。因此,表型的可塑性可能会发生改变,突变后可能更容易获得与CSCs相关的干样表型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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