Mapping breast cancer protein interaction networks as metric spaces: insights into central zones and drug discovery targets

Abstract

Introduction: Graph theory was employed in recent advances of cancer research for gain deeper insights into the complex structure and function of protein-protein interaction (PPI) networks. Objective: By representing proteins as nodes and their interactions as edges, graph theory offers a comprehensive framework for analyzing the topological properties of these networks and identifying key nodes that regulate critical biological processes. This approach has been widely applied to study various cancers, including breast cancer. Methods: To investigate the molecular organization and critical pathways in breast cancer, we constructed a breast cancer protein-protein interaction network (BCPIN) and analyzed its hierarchical structure. The network was modeled as a metric space to delineate its central zones, facilitating the identification of essential hubs enriched with signaling pathways critical for cancer progression. Results: Our study demonstrates the potential of hierarchical modeling of the BCPIN in unraveling its molecular organization and identifying therapeutic opportunities. By analyzing PPI network as a metric space, we highlight central zones 1 – 3 as critical hubs enriched with key signaling pathways, such as DNA repair, Notch signaling, and p53 signaling, which are essential to cancer progression. The identification of MAPK14 as a central node emphasizes its significant role in cancer biology and its value as a therapeutic target. The predominance of signaling proteins within these zones underscores their functional relevance, offering a strong rationale for prioritizing them in drug development. Conclusion: By modeling the PPI network as a metric space, we uncovered important insights into its architecture and the central zone’s critical role in facilitating key cellular processes. Our results indicate that zones 1 – 3, particularly the central zone, may serve as promising targets for drug discovery in cancer biology.