Raman spectroscopy is a vibrational spectroscopic technique that can be used to optically detect the molecular changes associated with various body liquids. The objective of our study is to explore Surface-Enhanced (SERS) Raman spectroscopy for distinguishing the saliva sample of lung cancer patients from those of the normal candidates. Saliva specimens(18 normal, 7 squamous cell carcinoma (SCC), and 14 adenocarcinoma)were obtained from 39 patients with known or suspected malignancies of the lung. A rapid acquisition dispersive type Surface-Enhanced Raman spectroscopy system was used for studies on saliva sample at 785 nm excitation. Raman spectra of saliva differed significantly between normal and malignant tumor. The spectroscopy of lung cancer patients show higher percentage signals for nucleic acid, tryptophan and phenylalanine and lower percentage signals for phospholipids, proline and valine, compared to normal candidates. Two algorithms were used to classify the samples respectively, including the Support Vector Machine (SVM) and Random Forest algorithm. R software (a language and software platform for statistical computing and graphics) was used to implement these two algorithms. Both algorithms have high sensitivities and specificities. The results of this exploratory study indicate that Surface-Enhanced Raman spectroscopy has significant potential for the noninvasive diagnosis of lung cancers in vivo based on the optic evaluation of biomolecules.
['Yan Wang', 'Zijian Cui', 'Wenxin Zheng', 'Anyu Chen', 'Hong Wang', 'Xun Guo', 'Chunwei Liu']
A feasibility study of early detection of lung cancer by saliva test using Surface Enhanced Raman scattering