McDermott MeiLu, Mehta Riddhee, Roussos Torres Evanthia T, MacLean Adam L
Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA.
Department of Medicine, Division of Medical Oncology, Keck School of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
NPJ Syst Biol Appl. 2025 Apr 10;11(1):31. doi: 10.1038/s41540-025-00512-2.
Epithelial-mesenchymal transition (EMT) is a cell state transition co-opted by cancer that drives metastasis via stable intermediate states. Here we study EMT dynamics to identify marker genes of highly metastatic intermediate cells via mathematical modeling with single-cell RNA sequencing (scRNA-seq) data. Across multiple tumor types and stimuli, we identified genes consistently upregulated in EMT intermediate states, many previously unrecognized as EMT markers. Bayesian parameter inference of a simple EMT mathematical model revealed tumor-specific transition rates, providing a framework to quantify EMT progression. Consensus analysis of differential expression, RNA velocity, and model-derived dynamics highlighted SFN and NRG1 as key regulators of intermediate EMT. Independent validation confirmed SFN as an intermediate state marker. Our approach integrates modeling and inference to identify genes associated with EMT dynamics, offering biomarkers and therapeutic targets to modulate tumor-promoting cell state transitions driven by EMT.
上皮-间质转化（EMT）是一种被癌症利用的细胞状态转变，它通过稳定的中间状态驱动转移。在这里，我们研究EMT动力学，通过对单细胞RNA测序（scRNA-seq）数据进行数学建模，以识别高转移性中间细胞的标记基因。在多种肿瘤类型和刺激条件下，我们鉴定出在EMT中间状态中持续上调的基因，其中许多基因以前未被识别为EMT标记物。一个简单的EMT数学模型的贝叶斯参数推断揭示了肿瘤特异性的转变率，为量化EMT进展提供了一个框架。对差异表达、RNA速度和模型衍生动力学的共识分析突出了SFN和NRG1作为EMT中间状态的关键调节因子。独立验证证实SFN是一种中间状态标记物。我们的方法整合了建模和推断，以识别与EMT动力学相关的基因，为调节由EMT驱动的肿瘤促进细胞状态转变提供生物标志物和治疗靶点。
