Emerging biomarkers in breast cancer

Breast cancer (BC) remains the most prevalent malignancy among women and a major contributor to global cancer mortality. Unfortunately, early detection, therapeutic targeting, and prognosis remains challenging due to the profound molecular and clinical heterogeneity in BC. However, recent advances in multi-omics technologies have transformed biomarker discovery by enabling comprehensive interrogation of the genomics, transcriptomics, proteomics, and metabolic networks that drive BC pathogenesis. This chapter reviews key developments in biomarker research and the technological platforms that support their discovery and validation. Innovations in mass spectrometry, single-cell and spatial proteomics, and nanoproteomics have expanded the detectable proteome and markedly improved analytical sensitivity, particularly for low-abundance and heterogeneous tumor signals. Complementary array- and aptamer-based assays further enhance high-throughput, quantitative, and minimally invasive profiling, providing scalable routes toward clinical implementation across diverse BC subtypes. Beyond experimental technologies, the chapter examines emerging computational frameworks that integrate multi-omics data using artificial intelligence and machine learning. These approaches enable multidimensional data interpretation and facilitate the identification of dynamic molecular signatures associated with disease progression, therapeutic response, and resistance. By outlining current methodologies, translational challenges, and future directions, this chapter highlights how converging technological and analytical advances are reshaping the BC biomarker landscape and paving the way toward more precise, adaptive, and clinically actionable diagnostic and therapeutic strategies.