ArticleeLife2024
A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 36 citations in OpenAlex.
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- Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.BMC cancer · 2026Article
- Advancements in extracellular vesicle research.Extracellular vesicle · 2026Article
- Exosomes in bone health and disease: cellular crosstalk, systemic signaling, and AI-driven advances in regenerative therapy.Stem cell research & therapy · 2026Review
- Environmental Exosomes/Small Extracellular Vesicles: Evidence of Extracellular RNA Release by Aquatic Organisms.Marine biotechnology (New York, N.Y.) · 2026Article
- Exosome-enabled bone defect repair: mechanistic foundations, bioengineered delivery, and artificial intelligence-driven translation.Journal of nanobiotechnology · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Past, present, and future of exosomes research in cancer: A bibliometric and visualization analysis.Human vaccines & immunotherapeutics · 2025Article
- Clinical relevance of extracellular vesicles in cancer - therapeutic and diagnostic potential.Nature reviews. Clinical oncology · 2025Review
- Human extracellular vesicle blueprints.Nature cell biology · 2025Article
- Exploring Exosome-Based Approaches for Early Diagnosis and Treatment of Neurodegenerative Diseases.Molecular neurobiology · 2025Review
- What we missed then, AI sees now: Revisiting legacy large extracellular vesicle data to reveal synergistic biomarkers for liver cancer screening.JHEP reports : innovation in hepatology · 2025Article
- Microfluidic Biochip-Based Multiplexed Profiling of Small Extracellular Vesicles Proteins Integrated with Machine Learning for Early Disease Diagnosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Engineering exosomes for liver disease: a new insight in regenerative medicine and drug delivery.Applied biochemistry and biotechnology · 2025Review
- Exosomes as Biomarkers and Therapeutic Agents in Neurodegenerative Diseases: Current Insights and Future Directions.Molecular neurobiology · 2025Review
- Mesenchymal stem cell-derived exosomes and the Wnt/β-catenin pathway: Unifying mechanisms of multi-organ regeneration and the path to precision clinical translation.World journal of stem cells · 2025Article
- Zika Virus Infection Modulates Extracellular Vesicle Biogenesis and Morphology in Human Umbilical Cord Endothelial Cells: A Proteomic and Microscopic Analysis.Microorganisms · 2025Article
- Enhancing detection and monitoring of circulating tumor cells: Integrative approaches in liquid biopsy advances.The journal of liquid biopsy · 2025Review
- Early-Stage Pancreatic Cancer Diagnosis: Serum Biomarkers and the Potential for Aptamer-Based Biosensors.Molecules (Basel, Switzerland) · 2025Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.