Energy landscape reveals the underlying mechanism of cancer-adipose conversion with gene network models
Zihao Chen, Jia Lu, Xing-Ming Zhao, Haiyang Yu, Chunhe Li

TL;DR
This study uses a systems biology approach combining mathematical modeling and experiments to uncover the molecular mechanisms of cancer-to-adipose cell conversion, revealing intermediate states and proposing new therapeutic strategies.
Contribution
It introduces a landscape-based model identifying key cell states and transition pathways in cancer-adipose conversion, and suggests novel drug combinations for therapy.
Findings
Identified four cell states: epithelial, mesenchymal, adipose, and intermediate.
Intermediate states are crucial in cancer to adipose transition.
Proposed and validated new drug strategies to promote cancer adipogenesis.
Abstract
Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments proposed that cancer cells can be transformed into adipocytes with combination drugs. However, the underlying mechanisms for how these drugs work from molecular network perspective remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), we adopt a systems biology approach by combing mathematical modeling and molecular experiments based on the underlying molecular regulatory network. We identified four types of attractors which correspond to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that the intermediate states play critical…
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Taxonomy
TopicsMathematical Biology Tumor Growth · Bioinformatics and Genomic Networks · Gene Regulatory Network Analysis
