Graphene nanomaterial-based electrochemical biosensors for salivary biomarker detection: A translational approach to oral cancer diagnostics

Graphene-based electrochemical biosensors have emerged as promising tools for the early detection and monitoring of oral cancer through salivary biomarker analysis. Graphene's exceptional properties, including high surface area, superior electrical conductivity, and excellent mechanical strength, enable the development of highly sensitive and specific biosensors. This review provides a comprehensive overview of the current state-of-the-art in graphene-based electrochemical biosensors for salivary biomarker detection in oral cancer. We discuss the unique advantages of saliva as a diagnostic medium and highlight the key salivary biomarkers associated with oral cancer, including proteins, DNA, and RNA. Various electrochemical detection techniques, such as cyclic voltammetry, differential pulse voltammetry, electrochemical impedance spectroscopy, field-effect transistors, amperometry, chronoamperometry, and photoelectrochemical methods, are explored in the context of graphene-based biosensors. The challenges associated with the development and clinical translation of these biosensors are also addressed, emphasizing the need for improved functionalization strategies, enhanced stability, and standardized validation protocols. Finally, we present a futuristic outlook on the integration of graphene-based biosensors with artificial intelligence, microfluidics, and telemedicine platforms to enable personalized diagnostics and treatment monitoring. With continued advancements in sensor technology and computational tools, graphene-based electrochemical biosensors have the potential to revolutionize oral cancer management, improving patient outcomes and quality of life. • Offer sensitive detection of salivary biomarkers. • Saliva is a non-invasive diagnostic medium. • Employ various electrochemical techniques. • Challenges include complex cancer biology, low biomarker concentrations, and individual saliva composition variability. • Future advancements include integration with AI and microfluidics.