Discrimination of gastric cancer from normal by serum RNA based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis

Purpose: Here, the authors explore the feasibility of discriminating cancer patients from healthy controls by serum RNA detection based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis. Methods: MgSO4-aggregated silver nanoparticles (Ag NP) as the SERS-active substrate presented strong SERS signals to RNA. SERS measurements were performed on two groups of serum RNA samples: one group from patients (n = 31) with gastric cancer and the other group from healthy volunteers (n = 34). Results: Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of circulating RNA between the gastric cancer group and the control group. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to differentiate gastric cancer from normal and achieved sensitivity of 100% and specificity of 94.1%. Conclusions: This exploratory study demonstrated potential for developing serum RNA SERS analysis into a useful clinical tool for noninvasive screening and detection of cancer.