Unveiling Cancer Stem Cell Marker Networks: A Hypergraph Approach

TL;DR

This paper introduces a hypergraph-based computational framework to analyze cancer stem cell marker networks across multiple organs, revealing complex interdependencies and key markers involved in tumor heterogeneity and metastasis.

Contribution

It presents a novel hypergraph approach combined with mutual information and Markov models to better understand CSCM networks and cancer progression.

Findings

Hypergraph models capture multi-organ CSCM relationships more effectively.

Identification of key markers driving tumor heterogeneity.

Insights into CSCM interdependencies and metastatic dynamics.

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.