AccScience Publishing / EJMO / Online First / DOI: 10.36922/ejmo.8208
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ORIGINAL RESEARCH ARTICLE

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

Emad Fadhal1,2*
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1 Department of Mathematics and Statistics, College of Science, King Faisal University, Al-Ahsa, Saudi Arabia
2 Department of Mathematics and Applied Mathematics, University of the Western Cape, Bellville, South Africa
EJMO, 8208 https://doi.org/10.36922/ejmo.8208
Received: 25 December 2024 | Accepted: 6 March 2025 | Published online: 20 March 2025
© 2025 by the Author(s).. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

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. 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. 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. 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. 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.

Keywords
Protein interaction networks
Metric spaces
Signaling pathways
Breast cancer
Central zones
Drug discovery
Funding
None
Conflict of interest
The authors declare no conflicts of interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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