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Analyzing the network properties of cancer biomarkers within protein-protein interaction (PPI) networks is valuable for discovering novel biomarker candidates. Therefore, we constructed PPI networks using breast cancer (BC)-associated gene sets and performed 12 distinct centrality analyses to characterize the topological features of clinically validated biomarkers. Our reference set of biomarkers comprised genes from five clinical genetic testing panels-MammaPrint, Oncotype DX, PAM50, EndoPredict, and the BC Index-that were also present in the STRING database. The PPI networks were constructed from the top 2,000 BC-associated genes, ranked by disease score from the DISEASES database. These networks were then subjected to centrality analysis using five local and seven global measures. The top 5% centrality rankings were evaluated, demonstrating that maximum clique centrality (MCC) identified the highest proportion of known biomarkers, with an inclusion rate of approximately 36%. Furthermore, MCC generated a unique biomarker-ranking pattern, exhibiting a Spearman's rank correlation coefficient below 0.8 when compared with all other metrics. Consequently, a high MCC score is a key topological feature of many validated biomarkers. Genes with the highest MCC scores (top 5%) were significantly enriched for gene-ontology terms related to the cell cycle and fibroblast growth factor receptor signaling pathway. Additionally, biomarkers with high MCC scores exhibited significantly greater evolutionary conservation and potential for protein complex formation. Collectively, our findings indicate that many effective BC biomarkers are components of large, evolutionarily conserved cliques within cell-cycle-associated regions of the PPI network. Finally, based on this MCC-centric approach, we identified 11 novel candidate biomarkers.