Quantum Dot Electrochemical Biosensors for Cancer Biomarker Detection: Materials, Approaches, and Clinical Applications

Abstract

(1) Background: Early and accurate cancer biomarker detection is essential for improving cancer diagnosis and monitoring. Quantum dot (QD)-based electrochemical biosensors have attracted considerable       attention because their tunable physicochemical properties, high surface area, and favorable electron-transfer characteristics enable sensitive and selective detection. However, the literature remains fragmented across QD materials, synthesis strategies, electrode architectures, sensing mechanisms, and biomarker       applications.

(2) Review Approach: This critical narrative review examines recent advances in QD-based electrochemical biosensors for cancer biomarker detection, focusing on QD materials, synthesis strategies, electrode       engineering, surface functionalization, electrochemical sensing mechanisms, complementary nanomaterials, multiplexed detection, and point-of-care (POC) applications. The review also highlights the relationship between preparation conditions, material properties, sensing mechanisms, analytical performance, and applicability, and provides directions for future development and clinical translation.

(3) Results: Reported platforms demonstrate high analytical sensitivity, with detection limits reaching 2 fg/mL for CEA, 0.029 pg/mL for CEA using ZnSe@ZnS QDs, 0.3 pg/mL for AFP, 38 pg/mL for PSA, and 6.5 pg/mL for MMP-2. Reported linear ranges extended from 0.0001 ng/mL to 100 ng/mL, demonstrating broad analytical applicability. Integration with graphene, gold nanoparticles, carbon nanotubes, and other       nanomaterials further enhanced electron transfer, biomolecule loading, signal amplification, and stability.

(4) Conclusions: This review establishes a material-to-device-to-application framework connecting QD composition and synthesis with surface engineering, electrochemical mechanisms, analytical performance, and clinical applicability. Despite promising performance, toxicity, reproducibility, stability, scalability, standardization, and clinical validation remain major barriers. Future research should prioritize safer QDs, scalable fabrication, standardized evaluation, clinical validation, and AI/ML-assisted cancer diagnosis.