Detecting Esophageal Cancer Using Surface-Enhanced Raman Spectroscopy (SERS) of Serum Coupled with Hierarchical Cluster Analysis and Principal Component Analysis

Abstract

Serum samples taken from healthy individuals and pre- and post-operative esophageal cancer patients were analyzed using surface-enhanced Raman spectroscopy (SERS) to explore the feasibility of diagnosing esophageal cancer using the technique. The serum spectrum data were collected using a He–Ne laser of wavelength 632.8 nm. Differences in peaks assigned to nucleic acids, lipids, and proteins were found to be statistically significant between groups, which implies that corresponding serum alterations occur with the development of esophageal diseases. For quantitative analysis, the chemometric methods of hierarchical clustering analysis and principal component analysis were utilized on the obtained SERS spectra for classification with good results.