Recent Progress in Detection and Profiling of Cancer Cell-Derived Exosomes.
Huiwen XiongZhipeng HuangZhejun YangQiuyuan LinBin YangXueen FangBaohong LiuHui ChenJilie KongPublished in: Small (Weinheim an der Bergstrasse, Germany) (2021)
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.
Keyphrases
- papillary thyroid
- mesenchymal stem cells
- machine learning
- squamous cell
- stem cells
- loop mediated isothermal amplification
- single cell
- induced apoptosis
- lymph node metastasis
- squamous cell carcinoma
- real time pcr
- randomized controlled trial
- photodynamic therapy
- systematic review
- oxidative stress
- risk assessment
- deep learning
- signaling pathway
- mass spectrometry
- cell proliferation
- big data
- bone marrow
- binding protein
- liquid chromatography