Emerging Biomarkers in Breast Cancer: Translational and Multi-Omics Perspectives in Precision Oncology

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, emphasizing the urgent need for improved diagnostic and therapeutic strategies. This review comprehensively explores the emerging landscape of breast cancer biomarkers, integrating insights from molecular mechanisms, clinical validation, and future translational applications. It highlights the evolution from classical receptor-based classification (ER, PR, HER2) to next-generation multi omics and AI-assisted biomarker discovery. Particular emphasis is placed on genetic, epigenetic, proteomic, and metabolomic markers, as well as liquid biopsy derived components such as ctDNA methylation, exosomal RNA, and extracellular vesicle biomarkers. The review critically analyses the reliability, reproducibility, and regulatory challenges of biomarker validation in clinical trials, including assay standardization and patient heterogeneity. Additionally, the discussion underscores the growing role of artificial intelligence in computational pathology and data harmonization across omics platforms. Limitations of current approaches and future research directions such as integrative modelling, personalized diagnostics, and real-world clinical translation are outlined to guide ongoing advancements in precision oncology. Overall, this article provides a mechanistic, evidence-based, and forward-looking overview of how emerging biomarkers are reshaping breast cancer diagnosis, prognosis, and therapeutic decision-making.