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Recent Progress in Detection and Profiling of Cancer Cell‐Derived Exosomes
Author(s) -
Xiong Huiwen,
Huang Zhipeng,
Yang Zhejun,
Lin Qiuyuan,
Yang Bin,
Fang Xueen,
Liu Baohong,
Chen Hui,
Kong Jilie
Publication year - 2021
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202007971
Subject(s) - microvesicles , exosome , profiling (computer programming) , nanotechnology , nucleic acid , computational biology , liquid biopsy , cancer , microrna , biology , medicine , computer science , materials science , biochemistry , gene , operating system
Exosomes, known as nanometer‐sized vesicles (30–200 nm), are secreted by many types of cells. Cancer‐derived exosomes have great potential to be biomarkers for early clinical diagnosis and evaluation of cancer therapeutic efficacy. Conventional detection methods are limited to low sensitivity and reproducibility. There are hundreds of papers published with different detection methods in recent years to address these challenges. Therefore, in this review, pioneering researches about various detection strategies are comprehensively summarized and the analytical performance of these tests is evaluated. Furthermore, the exosome molecular composition (protein and nucleic acid) profiling, a single exosome profiling, and their application in clinical cancer diagnosis are reviewed. Finally, the principles and applications of machine learning method in exosomes researches are presented.