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Breast cancer (BC) remains the most frequently diagnosed malignancy among women worldwide, while limitations of imaging-based screening, particularly in dense breast tissue and early-lesions, continue to challenge timely diagnosis. Liquid biopsy offers a complementary, minimally invasive approach for accessing circulating tumor-derived analytes and supporting early detection, molecular classification, and longitudinal monitoring. This review provides a critical overview of innovative analytical methods used for BC biomarker detection in liquid biopsy, situating them alongside established diagnostic assays. Established tissue- and serum-based biomarkers, including hormone receptors, HER2, Ki-67, and CA15-3, are discussed together with emerging analytes such as circulating tumor DNA, circulating tumor cells, microRNAs, extracellular vesicles, and multimodal signatures. Particular attention is given to how BC molecular heterogeneity, biomarker abundance and stability, sample matrix, and pre-analytical processing influence biomarker selection and analytical performance. Established methods, including immunohistochemistry and immunoassays, are benchmarked against digital PCR, next-generation sequencing, mass spectrometry, CRISPR-Cas diagnostics, surface-enhanced Raman spectroscopy, microfluidics, and electrochemical biosensors. These platforms are compared with emphasis on clinically relevant detection limits, dynamic range, matrix tolerance, throughput, reproducibility, and translational maturity rather than instrumental sensitivity alone. The electrochemical literature on lipocalin-2 is used as a focused case study to compare recognition elements, electrode materials, signal-amplification strategies, and matrix validation across platforms targeting the same biomarker. The review concludes that further progress in BC liquid biopsy will depend less on incremental reductions in detection limits and more on harmonized pre-analytical workflows, standardized reference materials, robust multiplexed strategies, and prospective validation against clinically relevant diagnostic and therapeutic decisions.
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Puchiu et al. (2026) studied this question.
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