Rahul Yadav (Banaras Hindu University, India)
LinkedIn: Rahul Yadav; X: @ryadav0089
Abstract: Oral cancer, a major global health concern due to its frequent late-stage diagnosis and poor prognosis. In India, around 77,000 new cases and 52,000 deaths are reported annually, which is approximately one-fourth of global incidences. Oral Submucous Fibrosis (OSMF), is a precancerous condition that elevates the risk of Oral Squamous Cell Carcinoma (OSCC) development due to tobacco, areca nut, alcohol, HPV, and poor oral hygiene. Traditional diagnostic methods, including biopsies and advanced imaging, and not easily accessible. Therefore, saliva-based biomarkers offer a non-invasive, affordable alternative for early disease detection and monitoring.
Recent advancements in Nuclear Magnetic Resonance (NMR)-based metabolomics combined with deep learning show promising potential for identifying metabolic alterations associated with oral cancer. NMR-based metabolomic profiling can detect distinct metabolome changes in saliva, enabling differentiation between OSCC patients, OSMF patients, and healthy individuals. Through Statistical analysis along with Machine learning and deep learning identifying metabolic patterns and potential biomarkers for Oral cancer and survivor. Our findings through NMR-based metabolomics uncovered set of metabolic signatures in saliva linked to oral cancer progression, from OSMF to OSCC and also explore Tissue metabolite in OSCC patients compare these metabolites from saliva which preferred non-invasive strategies to Early Detection of OSCC. Salivary biomarkers could revolutionize early diagnosis, facilitate personalized therapeutic interventions, and enhance prognostic evaluation in oral cancer management. Future research involving larger patient with multiple cohorts and integrated multi-omics and deep learning strategies will be crucial to validate these results and drive advancements in precision oncology.
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