Mining Extracellular Vesicles for Clinically Relevant Noninvasive Diagnostic Biomarkers in Cancer ( Novel Implications of Exosomes in Diagnosis and Treatment of Cancer and Infectious Diseases )

Publication series : Novel Implications of Exosomes in Diagnosis and Treatment of Cancer and Infectious Diseases

Author: Muhammad Nawaz Farah Fatima and Jeremy A. Squire  

Publisher: IntechOpen‎

Publication year: 2017

E-ISBN: INT6550469406

P-ISBN(Paperback): 9789535133353

P-ISBN(Hardback):  9789535133360

Subject: R473.73 Oncology Nursing

Keyword: 肿瘤科护理学

Language: ENG

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Mining Extracellular Vesicles for Clinically Relevant Noninvasive Diagnostic Biomarkers in Cancer

Description

Extracellular vesicles (EVs) are nanosized vesicles secreted by virtually all cell types into the extracellular milieu. EVs transport bioactive molecules between cells and play multifaceted roles in cell-to-cell communications and in the pathogenesis of various human diseases including cancer. EVs are currently a focus of intensive interest, mainly because they hold a wealth of biological information in the form of differentially expressed nucleic acids and proteins, including DNA and cancer-related mutated genes, microRNAs, and a variety of transcriptional factors. Both the mutational content and any differentially expressed RNA are highly stable in patient blood or urine because they are encapsulated in EVs. This protects them against nuclease activity, pH change, temperature fluctuations, and multiple free-thaw cycles. Therefore, EVs isolated from patient fluids may serve as an ideal source of liquid biopsy for mining cancer signatures through mutation screening and genetic profiling. However, the methods for obtaining pure and intact EVs from patient samples, as well as the optimized characterization of tumor-derived EVs are still not rigorously defined for routine clinical use. High-throughput genomic or proteomic platforms may aid in the identification of novel diagnostic and prognostic biomarkers that collectively could lead to cancer monitoring and improved patient outcome.

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