Decoding the ceRNA-Network Blueprint of Breast Cancer Metastasis via Molecular Cross-Talk in Motion from Silence to Signal: A Systematic Review and Bioinformatics Analysis

Background Breast cancer metastasis (BCM) remains the primary cause of breast cancer–related mortality. Despite advances in understanding breast cancer pathogenesis, the mechanisms driving metastasis are complex and therapeutically challenging. Recent progress in transcriptomics and bioinformatics has enabled deeper insights into the genomic and regulatory alterations underlying BCM. Aim This study aimed to construct and analyze a comprehensive competing endogenous RNA (ceRNA) network involving non-coding RNAs (lncRNAs, miRNAs, and circRNAs) associated with BCM to elucidate their molecular cross-talk and regulatory roles. Methods Interaction data were obtained through systematic literature review and bioinformatic predictions using the multiMiR R package, LncBase, and Circular RNA Interactome databases. A ceRNA network integrating mRNAs, miRNAs, lncRNAs, and circRNAs was visualized in Cytoscape, along with a protein–protein interaction (PPI) network. Network topology was analyzed with cytoHubba and MCODE, while functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Survival analysis was conducted to evaluate the prognostic relevance of hub ncRNAs. Results Network analysis identified 11 key hub nodes, including hsa-miR-1, hsa-miR-9, hsa-miR-27b, and MALAT1, which were significantly associated with poor prognosis. KEGG pathways were enriched in proteoglycans in cancer, microRNAs in cancer, and signaling pathways regulating stem cell pluripotency. GO terms highlighted regulation of transcription, cell differentiation, epithelial-to-mesenchymal transition (EMT), and cyclin-dependent kinase complexes. Conclusion This integrative ceRNA network analysis provides new insights into the molecular mechanisms driving BCM, offering potential biomarkers for improved diagnosis, prognosis, and therapeutic targeting.