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基因表达与基于主体的建模改善乳腺癌的精准预后。

Gene expression and agent-based modeling improve precision prognosis in breast cancer.

作者信息

Sridharan Padmasri, Ghosh Mini

机构信息

Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, 600127, India.

出版信息

Sci Rep. 2025 May 16;15(1):17059. doi: 10.1038/s41598-025-01275-w.

DOI:10.1038/s41598-025-01275-w
PMID:40379718
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12084417/
Abstract

Breast cancer survival is hard to predict because of the complex ways genes and cells interact. This study offers a new method to improve these predictions by combining gene expression profiling (GEP) with agent-based modeling (ABM). First, GEP will pinpoint genes that are important in breast cancer development. Then, a mathematical model will be built to show how these genes influence cell behavior. This data will be used in ABM to simulate tumor growth and treatment response. The ABM allows us to virtually test different treatments and see how they might affect patient survival. Finally, the model's accuracy will be checked against real patient data and compared to other models. By combining the strengths of GEP and ABM, this research could significantly improve breast cancer survival prediction. ABM's ability to analyze interactions mathematically could pave the way for more personalized and effective treatments.

摘要

由于基因和细胞相互作用的方式复杂，乳腺癌的生存率很难预测。这项研究提供了一种新方法，通过将基因表达谱分析（GEP）与基于主体的建模（ABM）相结合来改进这些预测。首先，基因表达谱分析将找出在乳腺癌发展中起重要作用的基因。然后，将建立一个数学模型来展示这些基因如何影响细胞行为。这些数据将用于基于主体的建模，以模拟肿瘤生长和治疗反应。基于主体的建模使我们能够虚拟测试不同的治疗方法，并观察它们可能如何影响患者的生存率。最后，将根据真实患者数据检查模型的准确性，并与其他模型进行比较。通过结合基因表达谱分析和基于主体的建模的优势，这项研究可以显著提高乳腺癌生存率预测。基于主体的建模在数学上分析相互作用的能力可以为更个性化、更有效的治疗铺平道路。

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