Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review

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.

Authors

Institutions

Publication Details

Journal
ACS Sensors
Published
2026-04-14
DOI
https://doi.org/10.1021/acssensors.5c04911
Primary Topic
Glycosylation and Glycoproteins Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review

Alain Wuethrich, Matt Trau, Abu Ali Ibn Sina, Yuao Wu et al.
ACS Sensors
Glycosylation and Glycoproteins Research
article

Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review

Alain Wuethrich, Matt Trau, Abu Ali Ibn Sina, Yuao Wu, Ramlah Khamis
article en

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.

ACS SensorsVol. 11(4)
The University of Queensland (AU), UNSW Sydney (AU), Institute of Bioengineering and Nanotechnology (SG), AgriBio (AU)
Cancer Australia, Australian Research Council, National Health and Medical Research Council
Zero hunger
Openalex Percentile: Top 15%
Glycosylation and Glycoproteins Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.