Label-free serum detection based on surface-enhanced Raman scattering for noninvasive screening of lung cancer

Lung cancer is one of the most prevalent cancers in China and has the highest mortality rate. Early-stage lung cancer often presents as lung nodules, which lack specific clinical features and can be easily overlooked, resulting in late-stage diagnoses. Unfortunately, there is a lack of effective techniques for early detection of lung cancer. Surface-enhanced Raman spectroscopy (SERS) offers a fast, simple and non-invasive qualitative or quantitative analysis. Additionally, the weak Raman scattering signal of water makes SERS an ideal tool for studying biological samples in aqueous solutions. The objective of this study was to evaluate the feasibility of a SERS-based label-free nano-biosensor for distinguishing lung cancer patients from healthy volunteers. Herein, silver nanoparticles were directly mixed with human serum as SERSactive nanostructures to enhance the Raman scattering signal, enabling high quality SERS spectra to be obtained from 50 lung cancer patients and 50 healthy volunteers. The results showed that the SERS spectral properties (spectral intensity) in the serum of lung cancer patients were significantly different from those of normal subjects due to biomolecular changes. High classification accuracy can be achieved using PCA-LDA diagnostic algorithms and machine learning techniques. This exploratory study demonstrates the great potential of the serum SERS method as a fast and convenient tool for lung cancer diagnosis and screening.