New method of lung cancer detection by saliva test using surface‐enhanced Raman spectroscopy

Abstract

Surface‐enhanced Raman spectroscopy (SERS) is a surface‐sensitive technique that enhances Raman scattering by molecules adsorbed on nanostructures. The advantages of using SERS include high detection sensibility and fast analysis, thus it is a potentially promising tool for sensing metabolic cancer molecules in trace amounts. To explore this new method of lung cancer detection, we analyzed saliva samples from 61 lung cancer patients and 66 healthy controls. An SERS system and a nano‐modified chip were used in this study. Statistics were analyzed using support vector machine (SVM) and random forest algorithms. The leave‐one‐out algorithm was used based on SVM results to analyze differences in saliva between lung cancer patients and controls. There was a significant difference between the saliva of patients with lung cancer and healthy controls using the Raman spectrum; the intensity of the spectral line in lung cancer patients was weaker than in controls and 12 characteristic peaks were detected. Saliva SERS peaks have been characterized to refer to tissues, body fluids, and biological standard Raman peaks, but it is difficult to identify molecules with current information. The sensitivity and specificity of Raman spectroscopy data and SVM classification results of lung cancer patients and normal saliva samples were both 100%. Using the leave‐one‐out algorithm, the sensitivity was 95.08% and the specificity was 100%. The sensitivity of the random forest method was 96.72% and specificity was 100%. Our results show that SERS has the potential to detect lung cancer.

Vibrational Spectroscopy Saliva Profiling as Biometric Tool for Disease Diagnostics: A Systematic Literature Review

“…Qian et al carried out in 2018, a study using SERS to discriminate 61 lung cancer saliva samples from 66 non-cancer controls [ 20 ]. Twelve peaks that varied significantly from one group to another were identified and attributed mainly to change in protein residues and the content of nucleic acid molecules ( Table 2 ).…”

Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

“…The peak assignments are listed in Table 3 according to the reported literature. [ 11,14,21–28 ] The HGG and LGG group peak intensity changes compared with the normal group are shown in Table 2. The HGG group had lower intensities than the LGG group at all specific peaks except 749 cm −1 .…”

Raman spectroscopic analysis of saliva for the diagnosis of oral cancer: A systematic review

“…Associated with this, literature reviews of any nature were also excluded from the final systematic analysis (n = 230). Finally, only eight studies were deemed to be fully consistent with the proposed theme [29][30][31][32][33][34][35][36], including those that did not specifically diagnose oral cancer (n = 3) but used salivary samples for Raman spectroscopy [32,34,35]. Important aspects of each study were also analysed, including the year of the study, sample size, the nature of saliva collection, the physical state of the sample at the time of analysis, the laser wavelength used as source, the use of nanoparticles as enhancers for Raman analysis (SERS), the type of statistical analysis adopted, whether principal components analysis associated with linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLSDA) or support vector machines (SVM) were used, and the size of the spectral range analysed (Table 1).…”