Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review
Clinical Snapshot
PICO Framework
| P — Population | Cancer patients and populations at risk for cancer requiring glycan-based diagnostic assessment |
| I — Intervention | Nanobiosensor-enabled glycan profiling technologies with AI/ML integration |
| C — Comparator | Conventional glycan detection methods and traditional cancer diagnostic approaches |
| O — Outcomes | Diagnostic accuracy, sensitivity, specificity, clinical utility for cancer detection and progression monitoring |
Bottom Line
This review examines nanobiosensor technologies combined with artificial intelligence for detecting cancer-associated glycans. While conventional glycan detection methods struggle with structural complexity and low abundance, nanobiosensors offer enhanced sensitivity through signal amplification and selective recognition. The review covers optical sensors (surface plasmon resonance, SERS, fluorescence) and electrochemical systems (impedimetric, voltammetric, amperometric) enhanced with nanomaterials. AI and machine learning approaches are highlighted for analyzing complex biosensor outputs through pattern recognition and nonlinear modeling. However, significant challenges remain in standardization and clinical scalability. The technology shows promise for point-of-care cancer diagnostics but requires substantial development before clinical implementation. Australian institutions are well-positioned in this field, though regulatory pathways and clinical validation studies are needed.
Key Findings
P Value: Not applicable - review study
Effect Size: Not applicable - review study
Primary Outcome: Evaluation of nanobiosensor technologies for glycan profiling in cancer
Nnt Or Sensitivity: Sensitivity and specificity improvements claimed but specific values not provided in abstract
Confidence Interval: Not applicable - review study
Clinical Application
Currently limited to research settings; clinical translation requires standardization and regulatory approval Strong Australian research base in nanotechnology; potential for TGA regulatory pathway development; alignment with precision medicine initiatives Cancer patients requiring improved diagnostic and monitoring tools, particularly for early detection
Abstract
Glycans are complex carbohydrates that are integral to cancer progression and tumor biology. Conventional methods face challenges due to the structural diversity and low abundance of glycans, limiting the detection of cancer-associated glycan signatures. This bottleneck hampers the accurate monitoring of cancer progression and the development of effective diagnostic tools. Nanobiosensor-enabled glycan profiling overcomes conventional limitations by providing highly sensitive detection of low-abundance glycans and precise discrimination of similar forms through nanoscale electrochemical or optical signal amplification and selective biorecognition. This review evaluates emerging nanobiosensors, including optical modalities such as surface plasmon resonance, localized surface plasmon resonance, surface-enhanced Raman scattering, fluorescence, and colorimetric nanobiosensors. It also covers electrochemical systems, including impedimetric, voltammetric, and amperometric sensors that use nanomaterials to enhance the sensitivity, specificity, and reliability of glycan detection. After a critical evaluation of nanobiosensors, this review highlights their applications for glycan analysis in cancer progression and potential of miniaturized point-of-care platforms. In parallel, this review highlights emerging artificial intelligence (AI) and machine learning (ML) approaches that are being explored to analyse complex biosensor outputs. By enabling multivariate pattern recognition, dimensionality reduction, and nonlinear modeling of biosensor responses, these computational frameworks may facilitate the extraction of diagnostically relevant signatures from high-dimensional optical and electrochemical datasets. The review identifies challenges in standardization and clinical scalability of nanobiosensors and explores future directions, including AI integration to advance early cancer detection and personalized patient management.
References
- 1.Khamis, R., Wu, Y., Sina, A. A. I., Trau, M., & Wuethrich, A. (2026). Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review. ACS Sensors. https://doi.org/10.1021/acssensors.5c04911
Related Research
The American journal of pathology
An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
3 Aug 2026
Current opinion in chemical biology
Prediction of protein-protein interactions and co-complex models with deep learning
3 Aug 2026
European journal of radiology
A CT-based deep learning model to differentiate between benign and malignant adrenal lesions
3 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service