Unraveling Genetic Markers in Breast Cancer: Bioinformatics Perspectives
Abstract
Breast cancer, the most prevalent malignancy in women globally, is a formidable challenge due to its molecular heterogeneity and the disparate clinical outcomes of luminal A, luminal B, HER2-enriched and triple-negative subtypes. The current review integrates bioinformatics approaches to decipher genetic markers in breast cancer, focusing on transcriptomics-based pipelines for discovering differentially expressed genes (DEGs) and key hub genes. Leveraging datasets from databases like TCGA and GEO, these pipelines cover data acquisition, preprocessing with packages such as DESeq2 and Scanpy, and advanced analyses including functional enrichment, protein–protein interaction network and machine learning algorithms for biomarker prioritization. Clinically relevant markers, validated through survival analysis and experimental validation, are used for informing diagnosis, prognosis and treatment strategies, with special reference to precision therapies such as PARP inhibitors and trastuzumab. Emerging technologies, such as liquid biopsy and single-cell multi-omics, enhance diagnostic precision by alleviating tumor heterogeneity and enabling noninvasive monitoring. However, unresolved issues persist, such as disparities in healthcare, dataset biases and the high cost of genomic profiling. Future directions for development include developing cost-effective tools, enhancing diverse datasets and integrating multi-omics with artificial intelligence for advancing equitable precision oncology. This comprehensive bioinformatics pipeline bridges molecular findings and clinical relevance, offering a promising strategy to alleviate the global breast cancer burden.