Abstract:
This dissertation comprises of seven chapters. First chapter summarizes the brief introduction of matrix assisted laser desorption/ ionization mass spectrometry (MALDI-MS), its theory, applications, and significance in small molecule analyses. The second chapter summarizes the brief introduction of the development of metabolomics in omics sciences with relevance to oncology. While third chapter includes the oral cancer pathogenesis, classification, and its associated biomarkers. Fourth chapter is based on the possible increment in MALDI-MS sensitivity by increasing the ion generation via use of dopants in MALDI-MS matrices. These dopants constitute commonly used dyes, along with conventional MALDI-MS matrices which can significantly increase the ion intensity of standard proteins. Moreover, such effect remains consistent when applied to real samples (RSC Adv. 2017, 7, 6598–6604). Fifth chapter describes the successful application of 31 compounds of acylhydrazide, and isatin Schiff bases as alternate UV-LDI matrices for the analyses of peptides (2000 Da) with significantly low background signals and showed comparable results with the conventional matrix (AJAC, 2012, 3, 779-789). Chapter six focused on the identification of distinguished metabolites of oral cancer tissue samples in comparison with precancerous and control tissue samples using gas chromatography-mass spectrometry (GC-MS) and chemometric analyses. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) library. Mass Profiler Professional (MPP) software was used for the alignment and for all the statistical analyses. 31 compounds out of 735 found distinguishing among oral cancer, precancerous and control group samples using p-value ≤ 0.05. The sensitivity and specificity of built model was found to be 85.7% and 93.3%, respectively, using statistically significant metabolites (Scientific Reports, 2016, 6, 38985-38993). Last chapter of the dissertation based on the distinctive identification of metabolites of oral cancer, snuff dippers and healthy plasma samples using GC-MS and chemometric analyses. Metabolites obtained were identified through NIST mass spectral library. MPP software was used for the data pretreatment and statistical analyses. 29 compounds were found distinguishing among oral cancer, snuff dippers and healthy group samples using p-value ≤ 0.001 and fold change ≥3. PLSDA model was generated using statistically significant metabolites which gave an overall accuracy of 89.2% (Submitted in Head & Neck).
