SERS analysis of serum for detection of early and locally advanced breast cancer.

Abstract

In this contribution, we investigated whether surface-enhanced Raman scattering (SERS) of serum can be a candidate method for detecting "luminal A" breast cancer (BC) at different stages. We selected three groups of participants aged over 50years: 20 healthy women, 20 women with early localized small BC, and 20 women affected by BC with lymph node involvement. SERS revealed clear spectral differences between these three groups. A predictive model using principal component analysis (PCA) and linear discriminant analysis (LDA) was developed based on spectral data, and its performance was estimated with cross-validation. PCA-LDA of SERS spectra could distinguish healthy from BC subjects (sensitivity, 92%; specificity, 85%), as well as subjects with BC at different stages, with a promising diagnostic performance (sensitivity and specificity, ≥80%; overall accuracy, 84%). Our data suggest that SERS spectroscopy of serum, combined with multivariate data analysis, represents a minimally invasive, easy to use, and fast approach to discriminate healthy from BC subjects and even to distinguish BC at different clinical stages.