Vibrational spectroscopy and saliva- a rapid and non-invasive diagnostic tools for diabetes and oral cancer
Creators: name: Khan, Rabia
Publisher: name: Lancaster University
Year: 2024

description: Oral cancer is a complex malignant disease of the head and neck that
 results from uncontrolled cell multiplication. Diabetes, alternatively, is
 a chronic metabolic condition characterized by elevated glucose
 concentration. Studies suggest that diabetes is a risk factor for oral
 cancer, possibly due to the overproduction of reactive oxygen species and
 high levels of insulin-like growth factors. Current diagnostic methods for
 oral cancer are time-consuming, invasive, and subject to inter-observer
 variability, making novel approaches necessary to identify the disease and
 malignancies. Fourier-transform Infrared (FTIR) spectroscopy is a
 sensitive and reproducible analytical technique that detects vibrational
 modes of molecular bonds. This study aimed to use FTIR spectroscopy to
 identify early biomarkers of oral cancer in diabetic patients. The study
 used 10 control (healthy) samples and 10 oral cancerous (diabetic)
 samples, with 10 independent spectra acquired from each sample to detct
 intra-sample heterogeneity. Spectral biomarkers that contributed to
 spectral variation were identified in the regions of amide I and amide
 III. Four machine learning models were generated, with model 1 covering
 the range of 600-4000cm-1, model 2 covering 1000-1800cm-1, model 3
 covering 1000-1500cm-1, and 
 
 model 4 covering 1800-3000cm-1, respectively. The data was analyzed by
 Principal component analysis (PCA), Linear discriminant analysis (LDA),
 k-nearest neighbors algorithm (k-NN), and support vector machine (SVM).
 The highest classifier, with a prediction accuracy of over 80%, was
 produced by applying SVM. The results support the use of FTIR spectroscopy
 with machine learning as an adjunct method to the gold standard in the
 diagnosis of oral cancer. We continued the project with saliva samples as
 a case-control study, where saliva samples (80) were collected from type 1
 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), and healthy
 controls (CON) were analyzed to identify specific molecular spectral
 signatures. Spectral analysis revealed unique vibrational modes in
 diabetic saliva samples compared to non-diabetic samples. Spectral
 biomarkers in the regions of Amide I and carbohydrates were discovered,
 along with subtle differences in spectra during spectral and multivariate
 analysis that could provide a robust and novel non-invasive 
 
 monitoring tool for diabetes. FTIR imaging has advantages over
 conventional 
 
 methods, as it is a label-free, non-disruptive technique requiring only a
 small amount of sample. It provides a biochemical fingerprinting that
 includes information about the structure, content, and chemical
 modification of any major biomolecules in the tested sample. In saliva
 samples, FTIR imaging provides information about modifications in
 proteins, DNA/RNA, and carbohydrates induced by external interventions.
 The spectral salivary biomarkers discovered using univariate and
 multivariate analysis may provide a novel and robust alternative for
 diabetes monitoring using non-invasive technology.