Computational prioritization of breast cancer biomarker candidates through automated protein-protein interaction...

Breast cancer is a highly heterogeneous malignancy, creating a need for reproducible computational strategies for candidate-biomarker prioritization. We developed an automated, multi-database framework integrating expression evidence from GEO and GEPIA2, protein annotation from UniProt, interaction information from STRING, PINA, and OncoPPI, and functional interpretation through KEGG and Gene Ontology. The workflow incorporates data retrieval, preprocessing, identifier harmonization, PPI-network construction, hub-node prioritization, enrichment analysis, and computational-performance evaluation. A Python-based implementation supports structured data processing and NetworkX-based network analysis. The visualized PPI network comprised 73 protein nodes, including 10 centrality-prioritized hub-like candidates. Scalability was evaluated across input sizes from 100 to 10,000 proteins; analyzed PPIs increased from 231 to 22,157, execution time ranged from 37.620 to 453.357 s, and throughput increased from 6.140 to 48.873 PPIs/s. The framework provides a reusable strategy for integrating heterogeneous molecular evidence and prioritizing breast cancer-associated candidates. Its modular structure may also support application to other disease-specific molecular datasets. The resulting network and enrichment signals represent computationally prioritized candidates requiring independent biological, clinical, and experimental validation.