Surface-enhanced Raman spectroscopy for breast cancer personalized management:From liquid biopsy and intraoperative navigation to therapy monitoring.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical platform for addressing the critical challenges in breast cancer precision diagnostics. This review systematically examines the recent methodological advances that position SERS as a transformative tool across the entire clinical continuum of breast cancer management-spanning non-invasive liquid biopsy screening, intraoperative surgical margin navigation, and personalized therapeutic guidance-with an emphasis on analytical innovation rather than clinical outcomes alone. We critically assess the core analytical strategies that underpin these applications, including the rational design of high-performance plasmonic nanostructures for enhanced signal reproducibility and amplification; the development of targeted and label-free SERS probes for ultrasensitive detection of circulating biomarkers such as microRNAs, exosomal proteins, and circulating tumor cells in complex biofluids; the integration of SERS with microfluidic platforms to enable sample-in-answer-out automation with reduced sample volumes and accelerated turnaround times; and the application of artificial intelligence and deep learning algorithms for deciphering complex spectral "fingerprints" enabling automated disease classification and molecular subtyping. Furthermore, we discuss the persistent analytical bottlenecks hindering clinical translation, notably the lack of standardized substrate fabrication protocols, signal variability due to heterogeneous biological matrices, and the biosafety evaluation of nanoprobes. By highlighting these methodological advancements and unresolved analytical challenges, this review aims to provide a comprehensive resource for analytical chemists and bioengineers seeking to develop next-generation SERS-based diagnostic platforms, ultimately contributing to the evolution of breast cancer management toward more precise, data-driven, and personalized paradigms.

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Authors

Fang Y; Huang G; Liu N; Zhu S; Cheng Y; Wang H

Published

Jan 1, 2026

Journal or venue

Talanta

DOI

10.1016/j.talanta.2026.130404

Paper type

journal article

Source

Pubmed