Abstract
We propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex multi-organ relationships more comprehensively than traditional graph-based methods. By integrating mutual information analysis and Markov models, we identify key markers driving tumour heterogeneity and metastasis, offering detailed insights into their interdependencies. This approach establishes hypergraphs as a computationally powerful tool to model cancer progression and metastatic dynamics, contributing to the understanding of complex biological systems and supporting the development of targeted therapeutic strategies.