Network Topology Similarities Across Cancer Types: Identifying Central Protein Hubs for Drug Discovery

A molecular-level understanding of cancer is essential for the development of effective therapies. Constructing protein-protein interaction (PPI) networks offers a valuable approach to identifying dysregulated driver genes and potential therapeutic targets. In this study, we modeled cancer PPI networks as metric spaces and applied mathematical and computational algorithms to analyze their structural and functional properties. Our findings reveal that these networks share a conserved architecture across different cancer types, with central zones enriched in essential proteins and critical regulatory pathways. Notably, zones 1 and 2 of the cancer PPI networks are uniquely enriched in specific pathways, underscoring their importance in the progression of cancer. These results highlight the potential of metric-based analysis of PPI networks to uncover key molecular targets and accelerate drug discovery in oncology.