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Cited by 373

The DOI matches its Crossref record; 306 of the 306 references it cites were found in a registry or an index.

Similarities and Distinctions of Cancer and Immune Metabolism in Inflammation and Tumors

Open access
Andrejeva, Gabriela, Rathmell, Jeffrey C.·2017·Cell Metabolism

Abstract

Summary It has been appreciated for nearly 100 years that cancer cells are metabolically distinct from resting tissues. More recently understood is that this metabolic phenotype is not unique to cancer cells but instead reflects characteristics of proliferating cells. Similar metabolic transitions also occur in the immune system as cells transition from resting state to stimulated effectors. A key finding in immune metabolism is that the metabolic programs of different cell subsets are distinctly associated with immunological function. Further, interruption of those metabolic pathways can shift immune cell fate to modulate immunity. These studies have identified numerous metabolic similarities between cancer and immune cells, but also critical differences that may be exploited and affect treatment of cancer and immunological diseases.

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Crossref · 10.1016/j.cmet.2017.06.004

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Metadata

Authors
Andrejeva, G., & Rathmell, J. C.
Venue
Cell Metabolism
Publisher
Elsevier BV
Published
2017-07
Volume
26
Issue
1
Pages
49-70
ISSN
1550-4131 (ISSN-L)
OpenAlex
W2725383737
Cited by
373
References
306
FWCI
9.63
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Similarities and Distinctions of Cancer and Immune Metabolism in Inflammation and Tumors. https://app.cochat.ai/papers/10.1016/j.cmet.2017.06.004
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Andrejeva, G., & Rathmell, J. C. (2017). Similarities and Distinctions of Cancer and Immune Metabolism in Inflammation and Tumors. Cell Metabolism. https://doi.org/10.1016/j.cmet.2017.06.004

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.