Recently, a team led by Professor Jiazhu Sun from the National Center for Nanoscience and Technology collaborated with Professor Liqin Zhang from Peking University School of Pharmaceutical Sciences, Professor Shaohua Zhang from the 5th Medical Center of PLA General Hospital, and Professor Yuguang Wang from Peking University School of Stomatology to develop a novel thermophoretic biosensing method based on lectin-carbohydrate molecular recognition. This new method enables rapid, highly sensitive, and selective analysis of extracellular vesicle (EV) glycan profiles in plasma, and has been applied to the precise diagnosis, efficacy monitoring, and prognosis prediction of triple-negative breast cancer (TNBC). The findings of this study have been published in Nature Communications.
EV Glycan Thermophoresis for Triple-Negative Breast Cancer Diagnostics and Prognostics
Triple-negative breast cancer (TNBC) is more malignant and has a worse prognosis compared to other breast cancer subtypes. Clinical diagnosis and treatment of TNBC are limited due to the lack of specific biomarkers.
"EV-associated glycans play an important role in tumor progression and have great potential as novel tumor biomarkers," Sun Jia姝 told China Science Daily. "However, the complex nature of body fluids, heterogeneity of EVs, and the complexity and low sensitivity of traditional glycan analysis methods pose challenges for EV glycan profiling in blood samples."
In this study, the research team developed a new EV glycan thermophoresis (EVLET) method based on lectin glycan molecular recognition. They used fluorescently labeled lectins to specifically label surface glycans of EVs in plasma, removed unbound lectins, soluble glycoproteins, and lipoproteins by vibration membrane filtration, and further enriched EVs using microfluidic thermophoresis and amplified the coupled lectin fluorescence signal. This enabled highly sensitive and quantitative detection of EV glycans, with a sensitivity two orders of magnitude higher than traditional methods.
By combining bioinformatics and experimental approaches, the researchers screened a set of specific lectin probes to obtain clinical plasma EV glycan profiles and constructed EV glycan features based on machine learning algorithms. This enabled accurate diagnosis of TNBC in experiments, with an accuracy rate of 91%. In terms of TNBC efficacy monitoring, EV glycan features achieved a 96% accuracy rate in determining disease progression or remission and could serve as an independent prognostic factor for progression-free survival of TNBC patients.
The researchers said that the study provides a new method for non-invasive cancer management based on EV glycan profiling.
Relevant paper information: https://doi.org/10.1038/s41467-024-46557-5
