乳腺癌干细胞的生长动力学:自我反馈和EMT机制的影响。

IF 1.3 4区 生物学 Q3 BIOLOGY
Theory in Biosciences Pub Date : 2022-09-01 Epub Date: 2022-08-03 DOI:10.1007/s12064-022-00374-w
Liuyong Pang, Sanhong Liu, Zhong Zhao, Tianhai Tian, Xinan Zhang, Qiuying Li
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引用次数: 1

摘要

乳腺癌干细胞(BCSCs)具有自我更新和分化的能力,已经在原发性乳腺癌组织和细胞系中被发现。骨髓间充质干细胞通常对传统的放疗和/或化疗具有耐药性。先前的研究也表明,成功的治疗必须根除癌症干细胞。本文的目的是建立一个具有自我反馈机制的数学模型,以说明有关绝对消除乳腺癌的困难的问题。此外,我们引入上皮-间质转化(epithelial-mesenchymal transition, EMT)的机制,探讨EMT对乳腺癌生长和治疗效果的影响。结果表明,EMT机制促进了乳腺癌的生长,使乳腺癌更难治愈。因此,靶向EMT参与的信号可以阻止乳腺癌的肿瘤进展。最后,利用实验数据进行了数值模拟,验证了理论结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms.

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms.

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

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来源期刊
Theory in Biosciences
Theory in Biosciences 生物-生物学
CiteScore
2.70
自引率
9.10%
发文量
21
审稿时长
3 months
期刊介绍: Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.
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