Head and neck cancer (HNC) is the sixth most common malignancy worldwide. Squamous cell carcinoma, the primary cause of HNC, evolves from normal epithelium through dysplasia before invading the connective tissue to form a carcinoma. Only 5% of suspicious lesions progress to cancer and diagnosis currently relies on histopathological evaluation, which is invasive and time consuming. A non-invasive, real-time point-of-care method could overcome these problems and facilitate regular screening. Infrared (IR) and Raman spectroscopy (RS) can non-invasively provide information regarding biochemical differences between normal and abnormal tissues. In this study, RS was employed to distinguish between different tissues-engineered models. 3D tissue engineered models of normal, dysplastic and head and neck squamous cell carcinoma (HNSCC) using normal oral keratinocytes, dysplastic (D19, D20 and DOK) and HNSCC cell lines (Cal27 , SCC4 and FaDu) were constructed and their biochemical content predicted by interpretation of their spectral characteristics. Spectral features of normal tissue samples were mainly attributed to lipids, whereas, malignant tissue samples were observed to be protein dominant. Visible differences were found between the spectra of normal, dysplastic and cancerous models, specifically in the bands of amide I and III. The spectra of HNSCC models showed a broad and strong peak of amide I instead of the sharp and weak lipid peak in normal models at band centred at 1667 cm-1. A shift at 2937 cm-1 was only observed in DOK, differentiating them from the other tissue types. Principal Component Analysis (PCA) and Cluster Analysis (CA) distinguished noticeable differences between tissues.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)