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关于在液体活检中使用单一细胞外囊泡分析的困境:血液中肿瘤细胞外囊泡丰度低的挑战。

On the dilemma of using single EV analysis for liquid biopsy: the challenge of low abundance of tumor EVs in blood.

作者信息

Imanbekova Meruyert, Sharma Mohul, Wachsmann-Hogiu Sebastian

机构信息

Department of Bioengineering, McGill University, Montreal, QC, H3A 0E9, Canada.

出版信息

Theranostics. 2025 Jul 24;15(16):8031-8048. doi: 10.7150/thno.115131. eCollection 2025.


DOI:10.7150/thno.115131
PMID:40860141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12374542/
Abstract

Single extracellular vesicle (EV) analysis holds great promise for non-invasive cancer diagnostics, offering insights into tumor-specific biomarkers and enabling personalized treatment strategies. However, a significant challenge in the path towards clinical applications is the low abundance of tumor-derived EVs (s) in biofluids, which reduces the sensitivity, specificity, and accuracy of detection. This review emphasizes the importance of analyzing a large number of single EVs to overcome this limitation. We estimate that less than 0.1% of total EVs could be from cancer cells in a mixed sample. Additionally, the development of more efficient s isolation methods and targeted enrichment strategies, as well as high-throughput analysis techniques are crucial for improving diagnostic accuracy and advancing liquid biopsy applications in cancer care.

摘要

单个细胞外囊泡(EV)分析在非侵入性癌症诊断方面具有巨大潜力,可为肿瘤特异性生物标志物提供见解,并实现个性化治疗策略。然而,在迈向临床应用的道路上,一个重大挑战是生物流体中肿瘤来源的EV数量稀少,这降低了检测的灵敏度、特异性和准确性。本综述强调了分析大量单个EV以克服这一局限性的重要性。我们估计,在混合样本中,癌细胞来源的EV占总EV的比例不到0.1%。此外,开发更有效的分离方法和靶向富集策略以及高通量分析技术对于提高诊断准确性和推进癌症护理中的液体活检应用至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/41b034eac5bd/thnov15p8031g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7a562da3df58/thnov15p8031g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7b18ff4cd779/thnov15p8031g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7297bc0defc4/thnov15p8031g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/41b034eac5bd/thnov15p8031g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7a562da3df58/thnov15p8031g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7b18ff4cd779/thnov15p8031g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/7297bc0defc4/thnov15p8031g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4552/12374542/41b034eac5bd/thnov15p8031g004.jpg

相似文献

[1]
On the dilemma of using single EV analysis for liquid biopsy: the challenge of low abundance of tumor EVs in blood.

Theranostics. 2025-7-24

[2]
The extracellular vesicle biomolecular corona: current insights and diagnostic potential.

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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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本文引用的文献

[1]
Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.

Gigascience. 2025-1-6

[2]
An All-in-One Nanohole Array for Size-Exclusive Trapping and High-Throughput Digital Counting of Single Extracellular Vesicles for Non-Invasive Cancer Screening.

Angew Chem Int Ed Engl. 2025-7

[3]
Partition-Less Digital Immunoassay Using Configurable Topographic Nanoarrays for Extracellular Vesicle Diagnosis of Ewing Sarcoma.

ACS Nano. 2025-4-1

[4]
Toward Identification of Markers for Brain-Derived Extracellular Vesicles in Cerebrospinal Fluid: A Large-Scale, Unbiased Analysis Using Proximity Extension Assays.

J Extracell Vesicles. 2025-3

[5]
Extracellular vesicles in tumor immunity: mechanisms and novel insights.

Mol Cancer. 2025-2-14

[6]
A scoping review of factors influencing the implementation of liquid biopsy for cancer care.

J Exp Clin Cancer Res. 2025-2-12

[7]
Dynamic Multilevel Regulation of EGFR, KRAS, and MYC Oncogenes: Driving Cancer Cell Proliferation Through (Epi)Genetic and Post-Transcriptional/Translational Pathways.

Cancers (Basel). 2025-1-14

[8]
Multiparametric profiling of HER2-enriched extracellular vesicles in breast cancer using Single Extracellular VEsicle Nanoscopy.

J Nanobiotechnology. 2024-9-28

[9]
A critical systematic review of extracellular vesicle clinical trials.

J Extracell Vesicles. 2024-10

[10]
Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.

Cell Rep Med. 2024-8-20

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