Surface-enhanced Raman spectroscopy with Ag@Ni-NiO core-shell nanowires for breast cancer detection

Breast cancer subtypes, luminal A/B, HER2-enriched, and triple-negative, have distinct prognoses and treatment responses, making accurate classification essential. Current methods are often time-consuming and rely on labeling. We introduce a label-free, non-destructive approach using Surface-Enhanced Raman Spectroscopy (SERS) with Ag@Ni-NiO core–shell nanowires to classify subtypes based on intrinsic biochemical differences. A total of 837 spectra were collected from tissues of 31 patients, including normal and tumor samples. Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) enabled differentiation based on spectral features related to nucleic acids, proteins, lipids, and collagen. The method revealed unique molecular signatures across subtypes, particularly in terms of lipid and collagen content, reflecting variations in the tumor microenvironment. The classification model achieved 90.32% accuracy, with sensitivity and specificity above 90%, highlighting its potential as a rapid, accurate, and minimally invasive diagnostic tool for clinical applications.