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Updated: Jun 13, 2025

Author Spotlight: Advanced Ex Vivo Model for Investigating Cancer-Adipose Microenvironment Interaction
Published on: January 26, 2024
Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models
Zihao Chen1,2, Jia Lu3, Xing-Ming Zhao2
1Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, 200433, China.
Researchers revealed how cancer cells can transform into fat cells using mathematical modeling and experiments. This study identified key intermediate cell states and new drug combinations to promote cancer adipogenesis, offering novel therapeutic strategies.
Area of Science:
- Systems biology
- Cancer research
- Molecular network analysis
Background:
- Cancer is a complex, heterogeneous disease driven by molecular networks.
- Epithelial-mesenchymal transition (EMT) is crucial for cancer metastasis and plasticity.
- Existing research suggests drug-induced transformation of cancer cells into adipocytes, but mechanisms are unclear.
Purpose of the Study:
- To elucidate the molecular mechanisms underlying cancer-adipose conversion (CAC).
- To identify novel therapeutic strategies for inducing CAC using a systems biology approach.
Main Methods:
- Integrated mathematical modeling with molecular experiments to analyze regulatory networks.
- Identified cell state attractors (epithelial, mesenchymal, adipose, intermediate EMT) on the CAC landscape.
- Employed a landscape control approach to discover new therapeutic strategies.
Main Results:
- Four distinct cell state attractors were identified, including crucial intermediate EMT states.
- Intermediate states were found to be critical for the cancer to adipose transition.
- Two novel drug combinations promoting CAC were predicted and experimentally validated in cell lines.
Conclusions:
- The study reveals the underlying mechanisms of intermediate cell states governing CAC.
- Identified potential new drug combinations for inducing cancer adipogenesis.
- Demonstrates the power of combined computational and experimental approaches for exploring cell fate transitions.
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