The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Targeting Glutamine Metabolism for Cancer Treatment
Open accessAbstract
Rapidly proliferating cancer cells require energy and cellular building blocks for their growth and ability to maintain redox balance. Many studies have focused on understanding how cancer cells adapt their nutrient metabolism to meet the high demand of anabolism required for proliferation and maintaining redox balance. Glutamine, the most abundant amino acid in plasma, is a well-known nutrient used by cancer cells to increase proliferation as well as survival under metabolic stress conditions. In this review, we provide an overview of the role of glutamine metabolism in cancer cell survival and growth and highlight the mechanisms by which glutamine metabolism affects cancer cell signaling. Furthermore, we summarize the potential therapeutic approaches of targeting glutamine metabolism for the treatment of numerous types of cancer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Repertoires of Autophagy in the Pathogenesis of Ocular Diseases
Li, Y.-J., Jiang, Q., Cao, G.-F., Yao, J., & Yan, B. (2015) · Cellular Physiology and Biochemistry · doi:10.1159/000373980 · Cited by 16,304
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
The Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.10.002 · Cited by 4,088
mTOR: from growth signal integration to cancer, diabetes and ageing
Zoncu, R., Efeyan, A., & Sabatini, D. M. (2011) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3025 · Cited by 3,941
Regulation mechanisms and signaling pathways of autophagy
He, C., & Klionsky, D. J. (2009) · Annual Review of Genetics · doi:10.1146/annurev-genet-102808-114910 · Cited by 3,609
Modulation of oxidative stress as an anticancer strategy
Gorrini, C., Harris, I. S., & Mak, T. W. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4002 · Cited by 3,579
Antimicrobial Autophagy: A Conserved Innate Immune Response in Drosophila
Moy, R. H., & Cherry, S. (2013) · Journal of Innate Immunity · doi:10.1159/000350326 · Cited by 3,539
The Rag GTPases Bind Raptor and Mediate Amino Acid Signaling to mTORC1
Sancak, Y., Peterson, T. R., Shaul, Y. D., Lindquist, R. A., Thoreen, C. C., Bar-Peled, L., & Sabatini, D. M. (2008) · Science · doi:10.1126/science.1157535 · Cited by 2,773
DeBerardinis, R. J., Mancuso, A., Daikhin, E., Nissim, I., Yudkoff, M., Wehrli, S., & Thompson, C. B. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709747104 · Cited by 2,622
Cited by
Altered metabolism in cancer: insights into energy pathways and therapeutic targets
Tufail, M., Jiang, C.-H., & Li, N. (2024) · Molecular Cancer · doi:10.1186/s12943-024-02119-3 · Cited by 534
The role of ASCT2 in cancer: A review
Liu, Y., Zhao, T., Li, Z., Wang, L., Yuan, S., & Sun, L. (2018) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2018.07.007 · Cited by 162
Hoerner, C. R., Chen, V. J., & Fan, A. C. (2019) · Kidney Cancer · doi:10.3233/kca-180043 · Cited by 68
Lewis, J. E., Forshaw, T. E., Boothman, D. A., Furdui, C. M., & Kemp, M. L. (2021) · Cell Systems · doi:10.1016/j.cels.2020.12.001 · Cited by 61
Fan, Y., Xue, H., Li, Z., Huo, M., Gao, H., & Guan, X. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1345522 · Cited by 40
Metadata
- Authors
- Choi, Y.-K., & Park, K.-G.
- Venue
- Biomolecules & Therapeutics
- Published
- 2018-01-01
- Volume
- 26
- Issue
- 1
- Pages
- 19-28
- ISSN
- 1976-9148 (ISSN-L)
- 2005-4483
- OpenAlex
- W2771345740
- Cited by
- 317
- References
- 88
- FWCI
- 7.86
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Targeting Glutamine Metabolism for Cancer Treatment. https://app.cochat.ai/papers/10.4062/biomolther.2017.178
- The paper
- Choi, Y.-K., & Park, K.-G. (2018). Targeting Glutamine Metabolism for Cancer Treatment. Biomolecules & Therapeutics. https://doi.org/10.4062/biomolther.2017.178
Data sources
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.
