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Cited by 475

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Autophagy in cancer metastasis

Open access
Mowers, E E, Sharifi, M N, Macleod, K F·2017·Oncogene

Abstract

Autophagy is a highly conserved self-degradative process that has a key role in cellular stress responses and survival. Recent work has begun to explore the function of autophagy in cancer metastasis, which is of particular interest given the dearth of effective therapeutic options for metastatic disease. Autophagy is induced upon progression of various human cancers to metastasis and together with data from genetically engineered mice and experimental metastasis models, a role for autophagy at nearly every phase of the metastatic cascade has been identified. Specifically, autophagy has been shown to be involved in modulating tumor cell motility and invasion, cancer stem cell viability and differentiation, resistance to anoikis, epithelial-to-mesenchymal transition, tumor cell dormancy and escape from immune surveillance, with emerging functions in establishing the pre-metastatic niche and other aspects of metastasis. In this review, we provide a general overview of how autophagy modulates cancer metastasis and discuss the significance of new findings for disease management.

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Crossref · 10.1038/onc.2016.333

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Metadata

Authors
Mowers, E. E., Sharifi, M. N., & Macleod, K. F.
Venue
Oncogene
Published
2016-09-05
Volume
36
Issue
12
Pages
1619-1630
ISSN
  • 0950-9232 (ISSN-L)
  • 1476-5594
OpenAlex
W2513393093
Cited by
475
References
41
FWCI
43.34
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Autophagy in cancer metastasis. https://app.cochat.ai/papers/10.1038/onc.2016.333
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Mowers, E. E., Sharifi, M. N., & Macleod, K. F. (2017). Autophagy in cancer metastasis. Oncogene. https://doi.org/10.1038/onc.2016.333

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.