A cubic Cu₂O@Ag bioprobe for label-free SERS classification of hepatic fibrosis and hepatocellular carcinoma

Early diagnosis and treatment of liver fibrosis can effectively prevent chronic liver disease from developing into hepatocellular carcinoma (HCC). Conventional techniques to detect liver fibrosis are complex and expensive. The development of non-invasive and sensitive surface-enhanced Raman scattering (SERS) can significantly reduce the time and cost, which is important for improving the efficiency of diagnosis and detection of liver disease. In this study, we developed a cubic core-shell Cu₂O@Ag SERS bioprobe for label-free identification of HCC and hepatic fibrosis. The constructed composite substrate has shown impressive SERS sensitivity and good stability. Trace molecules (alizarin red and rhodamine 6G) with concentrations as low as 10⁻¹⁰ mol L⁻¹ could be detected. Cubic Cu₂O@Ag also exhibited good SERS stability, since the smallest relative standard deviation (RSD) of Cu₂O@Ag-MB (methylene blue) was only 8.80%. Then, the spectral analysis of these three molecules (AR, MB, and R6G) was carried out by applying a machine learning-assisted LDA model, and the classification accuracy reached 100%. Subsequently, four different types of hepatocytes were identified and classified by using the established model and label-free SERS detection with a desirable accuracy of 91.38%. This innovative technology will further facilitate the early diagnosis of HCC and liver disease and assist in the rationalization of clinical treatment.