ArticleNPJ systems biology and applications2025
Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- The Origin of Cancer-Associated Fibroblasts (CAFs) in Brain Metastases: Seven Hypotheses and Current Evidence.Cancers · 2026Review
- Tumor heterogeneity: development, mechanisms, and therapeutic implications.Signal transduction and targeted therapy · 2026Review
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma.Diagnostics (Basel, Switzerland) · 2026Article
- Targeting tumor transition windows.Exploration of targeted anti-tumor therapy · 2026Review
- HOXA10-TWIST2 antagonism drives partial epithelial-to-mesenchymal transition for embryo implantation.Cell death discovery · 2025Article
- Data-driven model discovery and model selection for noisy biological systems.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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