The DOI matches its Crossref record; 315 of the 315 references it cites were found in a registry or an index.
Emerging Prospects of Exosomes for Cancer Treatment: From Conventional Therapy to Immunotherapy
Open accessAbstract
Exosomes are a class of extracellular vesicles of around 100 nm in diameter that are secreted by most cells and contain various bioactive molecules reflecting their cellular origin and mediate intercellular communication. Studies of these exosomal features in tumor pathogenesis have led to the development of therapeutic and diagnostic approaches using exosomes for cancer therapy. Exosomes have many advantages for conveying therapeutic agents such as small interfering RNAs, microRNAs, membrane‐associated proteins, and chemotherapeutic compounds; thus, they are considered a prime candidate as a delivery tool for cancer treatment. Since exosomes also provide an optimal microenvironment for the effective function of immunomodulatory factors, exosomes harboring bioactive molecules have been bioengineered as cancer immunotherapies that can effectively activate each stage of the cancer immunity cycle to successfully elicit cancer‐specific immunity. This review discusses the advantages of exosomes for treating cancer and the challenges that must be overcome for their successful clinical development.
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Cites
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Cited by
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Engineering nano-drug biointerface to overcome biological barriers toward precision drug delivery
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Metadata
- Authors
- Nam, G., Choi, Y., Kim, G. B., Kim, S., Kim, S. A., & Kim, I.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2020-10-05
- Volume
- 32
- Issue
- 51
- Pages
- e2002440-e2002440
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3091590487
- Cited by
- 267
- References
- 315
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Nam, G., Choi, Y., Kim, G. B., Kim, S., Kim, S. A., & Kim, I. (2020). Emerging Prospects of Exosomes for Cancer Treatment: From Conventional Therapy to Immunotherapy. Advanced Materials. https://doi.org/10.1002/adma.202002440
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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