Deep learning of the role of interleukin IL-17 and its action in promoting cancer

IF 1.2 Q3 Computer Science Bio-Algorithms and Med-Systems Pub Date : 2020-10-17 DOI:10.1515/bams-2020-0052
Alessandro Nutini, A. Sohail
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引用次数: 4

Abstract

Abstract In breast cancer patients, metastasis remains a major cause of death. The metastasis formation process is given by an interaction between the cancer cells and the microenvironment that surrounds them. In this article, we develop a mathematical model that analyzes the role of interleukin IL-17 and its action in promoting cancer and in facilitating tissue metastasis in breast cancer, using a dynamic analysis based on a stochastic process that accounts for the local and global action of this molecule. The model uses the Ornstein–Uhlembeck and Markov process in continuous time. It focuses on the oncological expansion and the interaction between the interleukin IL-17 and cell populations This analysis tends to clarify the processes underlying the metastasis expansion mechanism both for a better understanding of the pathological event and for a possible better control of therapeutic strategies. IL-17 is a proinflammatory interleukin that acts when there is tissue damage or when there is a pathological situation caused by an external pathogen or by a pathological condition such as cancer. This research is focused on the role of interleukin IL-17 which, especially in the case of breast cancer, turns out to be a dominant “communication pin” since it interconnects with the activity of different cell populations affected by the oncological phenomenon. Stochastic modeling strategies, specially the Ornstein-Uhlenbeck process, with the aid of numerical algorithms are elaborated in this review. The role of IL-17 is discussed in this manuscript at all the stages of cancer. It is discussed that IL-17 also acts as “metastasis promoter” as a result of its proinflammatory nature. The stochastic nature of IL-17 is discussed based on the evidence provided by recent literature. The resulting dynamical analysis can help to select the most appropriate therapeutic strategy. Cancer cells, in the case of breast cancer, have high level of IL-17 receptors (IL-17R); therefore the interleukin itself has direct effects on these cells. Immunotherapy research, focused on this cytokine and interlinked with the stochastic modeling, seems to be a promising avenue.
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深入学习白细胞介素IL-17的作用及其在促癌中的作用
在乳腺癌患者中,转移仍然是死亡的主要原因。转移的形成过程是由癌细胞和周围微环境的相互作用决定的。在本文中,我们建立了一个数学模型来分析白细胞介素IL-17的作用及其在乳腺癌中促进癌症和促进组织转移的作用,使用基于随机过程的动态分析来解释该分子的局部和全局作用。该模型采用连续时间的Ornstein-Uhlembeck和Markov过程。它侧重于肿瘤扩展和白细胞介素IL-17与细胞群之间的相互作用,这种分析倾向于阐明转移扩展机制的过程,以便更好地理解病理事件,并可能更好地控制治疗策略。IL-17是一种促炎白介素,在组织损伤或由外部病原体或癌症等病理状况引起的病理情况下起作用。这项研究的重点是白细胞介素IL-17的作用,特别是在乳腺癌的情况下,它被证明是一个主要的“通讯针”,因为它与受肿瘤现象影响的不同细胞群的活动相互联系。本文阐述了基于数值算法的随机建模策略,特别是Ornstein-Uhlenbeck过程。本文讨论了IL-17在癌症各个阶段的作用。本文还讨论了IL-17由于其促炎性质也可作为“转移促进子”。根据近年来的文献资料,讨论了IL-17的随机性。结果的动力学分析可以帮助选择最合适的治疗策略。以乳腺癌为例,癌细胞具有高水平的IL-17受体(IL-17R);因此,白细胞介素本身对这些细胞有直接作用。以该细胞因子为研究重点,结合随机建模的免疫治疗研究,似乎是一条很有前景的研究途径。
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
Bio-Algorithms and Med-Systems
Bio-Algorithms and Med-Systems MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
3.80
自引率
0.00%
发文量
3
期刊介绍: The journal Bio-Algorithms and Med-Systems (BAMS), edited by the Jagiellonian University Medical College, provides a forum for the exchange of information in the interdisciplinary fields of computational methods applied in medicine, presenting new algorithms and databases that allows the progress in collaborations between medicine, informatics, physics, and biochemistry. Projects linking specialists representing these disciplines are welcome to be published in this Journal. Articles in BAMS are published in English. Topics Bioinformatics Systems biology Telemedicine E-Learning in Medicine Patient''s electronic record Image processing Medical databases.
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