缺氧驱动的尤文肉瘤混合表型：计算上皮-间充质转化模型的启示

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-10-29 DOI:10.1016/j.jocs.2024.102464

Daner A. Silveira , Shantanu Gupta , André T. Brunetto , José Carlos Merino Mombach , Marialva Sinigaglia

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引用次数: 0

摘要

尤文肉瘤（ES）是一种侵袭性极强的儿科肿瘤，主要由 EWS::FLI1 融合蛋白推动。这种融合蛋白在 ES 的各种生物过程中起着关键作用，包括缺氧和上皮-间质转化（EMT）。缺氧已被证实可引发 EMT，这一过程可稳定混合细胞状态，增强转移潜力和抗药性。然而，ES缺氧时维持这种混合表型的确切机制仍然是个谜。我们的研究引入了ES中EMT的逻辑模型，强调了EWS::FLI1/miR-145回路在缺氧时诱导混合状态的潜在意义。此外，我们的研究结果还强调，在缺氧条件下，必须下调 EWS::FLI1 才能完全激活 EMT。这一模型与现有文献的结果非常吻合。这些发现强调了 EWS::FLI1 在缺氧条件下诱导 ES 中的混合状态的关键作用。

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Hypoxia-driven hybrid phenotypes in Ewing sarcoma: Insights from computational epithelial-mesenchymal transition modeling

Ewing sarcoma (ES) is an extremely aggressive pediatric tumor primarily propelled by the EWS::FLI1 fusion protein. This fusion protein plays a pivotal role in various biological processes within ES, including hypoxia and epithelial-mesenchymal transition (EMT). Hypoxia has been documented to trigger EMT, a process that can stabilize a hybrid cell state, enhancing metastatic potential and resistance to drugs. However, the precise mechanisms that sustain this hybrid phenotype during hypoxia in ES have remained enigmatic. Our study introduces a logical model for EMT in ES, underscoring the potential significance of the EWS::FLI1/miR-145 circuit in inducing hybrid states during hypoxia. Furthermore, our findings underscore the necessity for downregulating EWS::FLI1 to fully activate EMT under hypoxic conditions. This model aligns well with results derived from existing literature. These insights underscore the crucial role of EWS::FLI1 in inducing the hybrid state in ES during hypoxia.

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).