Related Experiment Video
Updated: Apr 16, 2026
Profiling Anti-Neu5Gc IgG in Human Sera with a Sialoglycan Microarray Assay
Published on: July 13, 2017
Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review
Ramlah Khamis1, Yuao Wu1, Abu Ali Ibn Sina2
1Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia.
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.
Insights
Nanobiosensors offer sensitive detection of cancer-associated glycans, overcoming limitations of traditional methods. Integrating artificial intelligence with these sensors promises improved early cancer detection and personalized patient management.
Area of Science:
- Biotechnology
- Nanotechnology
- Cancer Biology
Background:
- Glycans play a crucial role in cancer progression, but their structural diversity and low abundance pose challenges for conventional detection methods.
- Limitations in current glycan detection hinder accurate cancer monitoring and the development of effective diagnostic tools.
Purpose of the Study:
- To review emerging nanobiosensors for sensitive and specific glycan profiling in cancer research.
- To evaluate the potential of nanobiosensors, including optical and electrochemical modalities, for cancer-associated glycan analysis.
- To explore the integration of artificial intelligence (AI) and machine learning (ML) with nanobiosensors for enhanced cancer diagnostics.
Main Methods:
- Evaluation of various optical nanobiosensors (SPR, LSPR, SERS, fluorescence, colorimetric) and electrochemical nanobiosensors (impedimetric, voltammetric, amperometric).
- Assessment of nanomaterials for enhanced sensitivity, specificity, and reliability in glycan detection.
- Review of AI and ML approaches for analyzing complex biosensor data, including pattern recognition and dimensionality reduction.
Main Results:
- Nanobiosensors provide highly sensitive detection and precise discrimination of low-abundance glycans, overcoming conventional method limitations.
- Optical and electrochemical nanobiosensors show significant potential for glycan analysis in cancer progression.
- AI and ML can extract diagnostically relevant signatures from complex biosensor datasets, aiding early cancer detection.
Conclusions:
- Nanobiosensor-enabled glycan profiling offers a powerful approach for cancer diagnostics and monitoring.
- Integration of AI and ML with nanobiosensors is crucial for advancing early cancer detection and personalized medicine.
- Challenges in standardization and clinical scalability need to be addressed for widespread adoption of nanobiosensor platforms.
