SERS/Fluorescence Dual-Modal Imaging Bioprobe for Accurate Diagnosis of Breast Cancer

Early diagnosis and precise identification of breast cancer subtypes are vital. However, current detection methods are often hindered by high costs and complexity. This study aims to develop an efficient and noninvasive method to realize efficient breast cancer detection. First, hexoctahedral gold nanoparticles (Au HNPs) are constructed, which detect molecules with concentrations as low as 10⁻¹² M, and the EF value is ∼3.8 × 10⁸. Then, two optical bioprobes with a surface-enhanced Raman scattering (SERS)-fluorescence (FL) dual-modal function for breast cancer cell detection and subtype identification are designed. These bioprobes exhibit excellent SERS stability since the spectral relative standard deviation (RSD) of the SERS-FL bioprobe achieves a good level of ∼10.4%. Additionally, the clear distinction between breast cancer cells and white blood cells (WBCs) under a fluorescence microscope showed that bioprobes have a good fluorescence imaging ability. More importantly, by creatively stitching the SERS spectra of the two bioprobes, a "symphonic SERS spectra" is constructed, and a linear discriminant analysis (LDA) machine learning algorithm is employed, enabling high-precision classification of breast cancer subtypes with an accuracy of 94%. This study proposes an innovative strategy combined with SERS and FL technology, which provides the possibility for rapid and accurate detection of breast cancer subtypes.