Oral cancer has a poor prognosis of only 50% even in the light of current technological advances. This poor prognosis may be attributed to the still unmet clinical need to diagnose oral pre-cancer and dysplasia. Raman spectroscopy, which can detect subtle biochemical changes, has been explored for the diagnosis of cancer. This study aims to address the clinical need by exploiting the high amplification factor of Surface Enhanced Raman Spectroscopy (SERS) to analyse the saliva samples of 10 healthy controls and 10 patients with oral dysplasia. Furthermore, this technique was compared to conventional Raman spectroscopy. The saliva samples were centrifuged at 14000g for 15 minutes and the supernatant was applied directly on the SERS substrate and dried. Simultaneously, the saliva samples were prepared in the same way on slides for conventional Raman analysis. A peak at 2108 cm-1, attributed to salivary thiocyanate was present in all samples from dysplasia subjects but absent in samples from healthy non-smoking subjects. Partial least squares – discriminant analysis models for classification of oral pre-cancer were developed for both Raman spectroscopy and SERS to discriminate between healthy, mild and moderate dysplasia cohorts.
Oral cancer is one of the most common cancers worldwide. One-fifth of the world’s oral cancer subjects are from India and other South Asian countries. The present Raman mapping study was carried out to understand biochemical variations in normal and malignant oral buccal mucosa. Data were acquired using WITec alpha 300R instrument from 10 normal and 10 tumors unstained tissue sections. Raman maps of normal sections could resolve the layers of epithelium, i.e. basal, intermediate, and superficial. Inflammatory, tumor, and stromal regions are distinctly depicted on Raman maps of tumor sections. Mean and difference spectra of basal and inflammatory cells suggest abundance of DNA and carotenoids features. Strong cytochrome bands are observed in intermediate layers of normal and stromal regions of tumor. Epithelium and stromal regions of normal cells are classified by principal component analysis. Classification among cellular components of normal and tumor sections is also observed. Thus, the findings of the study further support the applicability of Raman mapping for providing molecular level insights in normal and malignant conditions.
Oral cancer is the most common cancer among Indian males, with 5-year- survival-rates of less than 50%. Efficacy of Raman spectroscopic methods in non-invasive and objective diagnosis of oral cancers and confounding factors has already been demonstrated. The present Raman microspectroscopic study was undertaken for in-depth and site-specific analysis of normal and tumor tissues. 10 normal and 10 tumors unstained sections from 20 tissues were accrued. Raman data of 160 x 60 μm and 140 x 140 μm in normal and tumor sections, respectively, were acquired using WITec alpha 300R equipped with 532 nm laser, 50X objective and 600 gr/mm grating. Spectral data were corrected for CCDresponse, background. First-derivitized and vector-normalized data were then subjected to K-mean cluster analysis to generate Raman maps and correlated with their respective histopathology. In normal sections, stratification among epithelial layers i.e. basal, intermediate, superficial was observed. Tumor, stromal and inflammatory regions were identified in case of tumor section. Extracted spectra of the pathologically annotated regions were subjected to Principal component analysis. Findings suggest that all three layers of normal epithelium can be differentiated against tumor cells. In epithelium, basal and superficial layers can be separated while intermediate layer show misclassifications. In tumors, discrimination of inflammatory regions from tumor cells and tumor-stroma regions were observed. Finding of the study indicate Raman mapping can lead to molecular level insights of normal and pathological states.