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Tongue cancer, a subtype of oral squamous cell cancer (OSCC), is regarded as one of the most widespread as well as aggressive lesions of the oral cavity, comprising approximately 25%–40% of oral cancers worldwide. Conventional diagnostic methods include histopathology, biopsy, computed tomography (CT), and magnetic resonance imaging (MRI), which provide meaningful diagnostic data but are often invasive, time-consuming, and lack molecular specificity. Surface-enhanced Raman Spectroscopy (SERS) employed gold nanoparticles (Au–NPs) that provide a very sensitive and noninvasive method in order to detect cancer through molecular fingerprinting of blood serum. The gold nanoparticles are further explored for the detailed study of different stages of tongue cancer because gold nanoparticles (Au–NPs) provide different SERS features than silver nanoparticles (Ag–NPs). The major differentiating SERS features include biocompatibility, signal enhancement, chemical stability, and surface chemistry. This study aimed to characterize serum samples using SERS along with the chemometric tools like principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). PCA reduced the dimensionality of complex spectral datasets. PLS-DA was effectively distinguished between the SERS spectral groups of cancerous and non-cancerous samples by achieving an area under the curve value of 0.88, specificity (98%), sensitivity (99%), and accuracy (100%). Unlike previous research that focused on tissue-based Raman analysis, this work optimized gold nanoparticle-based SERS for serum characterization, enhancing early detection capabilities.
Murtaza et al. (Fri,) studied this question.