ArticleJournal of extracellular vesicles2023
Correlation between membrane proteins and sizes of extracellular vesicles and particles: A potential signature for cancer diagnosis.
Article in Journal of extracellular vesicles, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Article
- Overcoming tumour resistance in cancer immunotherapy through predictive and pharmacodynamic biomarkers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Steric hindrance-mediated extracellular vesicle size fractionation for rapid prehospital diagnosis of intracerebral hemorrhage.Nature communications · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Review
- A 96-Well Ultrafiltration Approach for the High-Throughput Proteome Analysis of Extracellular Vesicles Isolated From Conditioned Medium.Journal of extracellular vesicles · 2025Article
- Advances in the application of extracellular vesicles in precise diagnosis of pancreatic cancer.European journal of medical research · 2025Review
- Nanophotonic sensing and label-free imaging of extracellular vesicles.Light, science & applications · 2025Review
- Unveiling circulating targets in pancreatic cancer: Insights from proteogenomic evidence and clinical cohorts.iScience · 2025Article
- Biomembrane structure at the molecular level and its application in precision medicine.Biophysics reviews · 2025Review
- Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Size distribution of extracellular vesicles in pretreatment ascites and plasma is correlated with primary treatment outcome in advanced high-grade serous carcinoma.Scientific reports · 2025Article
- Extracellular vesicles as cancer biomarkers and drug delivery strategies in clinical settings: Advances, perspectives, and challenges.Clinics (Sao Paulo, Brazil) · 2025Review
- Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis.Computational and structural biotechnology journal · 2025Review
- Size matters: Biomolecular compositions of small and large extracellular vesicles in the urine of glioblastoma patients.Journal of extracellular biology · 2024Article
- Extracellular Vesicle Characteristics in Local Fluid and Plasma Measured by Nanoparticle Tracking Analysis Can Help Differentiate High-Grade Serous Carcinoma from Benign Ovarian Pathology.Diagnostics (Basel, Switzerland) · 2024Article
- Surface Plasmon Resonance Biosensors: A Review of Molecular Imaging with High Spatial Resolution.Biosensors · 2024Review
- Correlation between membrane proteins and sizes of extracellular vesicles and particles: A potential signature for cancer diagnosis.Journal of extracellular vesicles · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Extracellular vesicles and particles (EVPs) are recognized as ideal liquid biopsy tools for cancer detection, and membrane proteins are commonly used EVP biomarkers. However, bulk analysis of EVP membrane protein biomarkers typically fails to meet the clinical requirement for diagnostic accuracy. We investigated the correlation between the membrane protein expression level, the binding kinetics to aptamers and the sizes of EVPs with interferometric plasmonic microscopy (iPM), and demonstrated the implementation of the correlative signature to determine cancer types. Using EVPs collected from both cell model and clinical plasma samples with liver, lung, breast, or prostate cancer, we found that the selective set of membrane protein expression levels of five protein markers and their binding kinetics were highly heterogeneous across various sizes of EVPs, resulting in the low overall accuracy (<50%) in cancer classification with bulk analysis of all populations. By grouping the EVPs into three subpopulations according to their sizes, the overall accuracy could be increased to about 70%. We further grouped the EVPs into subpopulations with a 10 nm interval in sizes and analysed the correlation between the membrane proteins and sizes with a machine learning algorithm. The results show that the overall accuracy to discriminate cancer types could be improved to 85%. Therefore, this work highlights the significance of size-dependent subtyping of EVPs and suggests that the correlation between the selective set of membrane proteins and sizes of EVP can serve as a signature for clinical cancer diagnosis.
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