Surface-enhanced Raman scattering (SERS) spectra were obtained from urine samples from subjects diagnosed with prostate cancer as well as from healthy controls, using Au nanoparticles as substrates. Principal component analysis (PCA) of the spectral data, followed by linear discriminant analysis (LDA), leads to a classification model with a sensitivity of 100 %, a specificity of 89 %, and an overall diagnostic accuracy of 95 %. Even considering the very limited number of samples involved in this report, preliminary results from this approach are extremely promising, encouraging further investigation.

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 50 years: 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.