Quantum-Enhanced Graphene-Plasmonic Metasurface for Ultra-Sensitive Multi-Cancer Biomarker Detection

Early and ultra-sensitive detection of cancer biomarkers is essential for effective diagnosis and timely clinical intervention. In this study, we propose a quantum-enhanced graphene-plasmonic metasurface biosensor designed for multi-cancer biomarker detection with femtomolar-level sensitivity. The hybrid architecture integrates a tunable graphene monolayer with a metasurface composed of square-ring and nanowire resonators, enabling strong electromagnetic field confinement and quantum-corrected plasmonic enhancement in nanoscale gaps. Graphene functionalized with specific antibodies and aptamer probes facilitates selective detection of clinically relevant biomarkers, including prostate specific antigen (PSA), carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), and circulating tumor DNA (ctDNA). Finite-difference time-domain simulations and experimental validation using microfluidic delivery establish the device's high sensitivity, narrow resonance linewidths, and dynamic spectral tunability. Real-time resonance shifts were analyzed using an AI-assisted classification framework incorporating machine learning models and ROC-AUC validation to achieve robust detection accuracy. Experimental characterization using SEM, AFM, and Raman spectroscopy confirms fabrication quality and graphene integrity. Comparative analysis demonstrates that the quantum-corrected metasurface outperforms classical plasmonic designs in sensitivity and figure-of-merit. These results position the proposed platform as a promising foundation for universal multi-cancer screening systems with high precision and clinical translational potential. Even though the investigated biomarkers are neither adrenal-cancer-specific, they are commonly utilized in clinical differential diagnosis of adrenal malignancies, where biomarker expression is usually low and heterogeneous. The innovative platform is thus placed as a multi-purpose ultra-sensitive detecting architecture that can equally be used in the screening of adrenal cancer and a larger multi-cancer diagnostic.