The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Metabolic reprogramming induced by ketone bodies diminishes pancreatic cancer cachexia
Open accessAbstract
BACKGROUND: Aberrant energy metabolism is a hallmark of cancer. To fulfill the increased energy requirements, tumor cells secrete cytokines/factors inducing muscle and fat degradation in cancer patients, a condition known as cancer cachexia. It accounts for nearly 20% of all cancer-related deaths. However, the mechanistic basis of cancer cachexia and therapies targeting cancer cachexia thus far remain elusive. A ketogenic diet, a high-fat and low-carbohydrate diet that elevates circulating levels of ketone bodies (i.e., acetoacetate, β-hydroxybutyrate, and acetone), serves as an alternative energy source. It has also been proposed that a ketogenic diet leads to systemic metabolic changes. Keeping in view the significant role of metabolic alterations in cancer, we hypothesized that a ketogenic diet may diminish glycolytic flux in tumor cells to alleviate cachexia syndrome and, hence, may provide an efficient therapeutic strategy. RESULTS: We observed reduced glycolytic flux in tumor cells upon treatment with ketone bodies. Ketone bodies also diminished glutamine uptake, overall ATP content, and survival in multiple pancreatic cancer cell lines, while inducing apoptosis. A decrease in levels of c-Myc, a metabolic master regulator, and its recruitment on glycolytic gene promoters, was in part responsible for the metabolic phenotype in tumor cells. Ketone body-induced intracellular metabolomic reprogramming in pancreatic cancer cells also leads to a significantly diminished cachexia in cell line models. Our mouse orthotopic xenograft models further confirmed the effect of a ketogenic diet in diminishing tumor growth and cachexia. CONCLUSIONS: Thus, our studies demonstrate that the cachectic phenotype is in part due to metabolic alterations in tumor cells, which can be reverted by a ketogenic diet, causing reduced tumor growth and inhibition of muscle and body weight loss.
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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
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Analyzing real-time PCR data by the comparative CT method
Schmittgen, T. D., & Livak, K. J. (2008) · Nature Protocols · doi:10.1038/nprot.2008.73 · Cited by 26,190
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
NMRPipe: A multidimensional spectral processing system based on UNIX pipes
Delaglio, F., Grzesiek, S., Vuister, G., Zhu, G., Pfeifer, J., & Bax, A. (1995) · Journal of Biomolecular NMR · doi:10.1007/bf00197809 · Cited by 15,965
Siegel, R., Naishadham, D., & Jemal, A. (2013) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21166 · Cited by 11,825
Spencer, P. M., Cameron, J. L., & Lin, L. (2000) · Cited by 7,202
Identification of c- MYC as a Target of the APC Pathway
He, T.-C., Sparks, A. B., Rago, C., Hermeking, H., Zawel, L., da Costa, L. T., Morin, P. J., Vogelstein, B., & Kinzler, K. W. (1998) · Science · doi:10.1126/science.281.5382.1509 · Cited by 4,566
Dang, C. V. (2012) · Cell · doi:10.1016/j.cell.2012.03.003 · Cited by 3,390
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0 · Cited by 3,289
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Puchalska, P., & Crawford, P. A. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2016.12.022 · Cited by 1,610
Cancer-associated adipocytes: key players in breast cancer progression
Wu, Q., Li, B., Li, Z., Li, J., Sun, S., & Sun, S. (2019) · Journal of Hematology & Oncology · doi:10.1186/s13045-019-0778-6 · Cited by 462
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Paul, S., Ghosh, S., & Kumar, S. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.09.007 · Cited by 449
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Wang, T., Gnanaprakasam, J. N. R., Chen, X., Kang, S., Xu, X., Sun, H., Liu, L., Rodgers, H., Miller, E., Cassel, T. A., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0219-4 · Cited by 331
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Puchalska, P., & Crawford, P. A. (2021) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-111120-111518 · Cited by 282
Metadata
- Authors
- Shukla, S. K., Gebregiworgis, T., Purohit, V., Chaika, N. V., Gunda, V., Radhakrishnan, P., Mehla, K., Pipinos, I. I., Powers, R., Yu, F., & Singh, P. K.
- Venue
- Cancer & Metabolism
- Published
- 2014-09-01
- Volume
- 2
- Issue
- 1
- Pages
- 18-18
- ISSN
- 2049-3002 (ISSN-L)
- OpenAlex
- W2155631120
- Cited by
- 255
- References
- 61
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- gold
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- Shukla, S. K., Gebregiworgis, T., Purohit, V., Chaika, N. V., Gunda, V., Radhakrishnan, P., Mehla, K., Pipinos, I. I., Powers, R., Yu, F., & Singh, P. K. (2014). Metabolic reprogramming induced by ketone bodies diminishes pancreatic cancer cachexia. Cancer & Metabolism. https://doi.org/10.1186/2049-3002-2-18
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