The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Metabolic Characterization of Hepatocellular Carcinoma Using Nontargeted Tissue Metabolomics
Abstract
Hepatocellular carcinoma has a poor prognosis due to its rapid development and early metastasis. In this report, we characterized the metabolic features of hepatocellular carcinoma using a nontargeted metabolic profiling strategy based on liquid chromatography-mass spectrometry. Fifty pairs of liver cancer samples and matched normal tissues were collected from patients having hepatocellular carcinoma, including tumor tissues, adjacent noncancerous tissues, and distal noncancerous tissues, and 105 metabolites were filtered and identified from the tissue metabolome. The principal metabolic alternations in HCC tumors included elevated glycolysis, gluconeogenesis, and β-oxidation with reduced tricarboxylic acid cycle and Δ-12 desaturase. Furthermore, increased levels of glutathione and other antioxidative molecules, together with decreased levels of inflammatory-related polyunsaturated fatty acids and phospholipase A2, were observed. Differential metabolite levels in tissues were tested in 298 serum specimens from patients with chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Betaine and propionylcarnitine were confirmed to confer good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum specimens further showed that this combination biomarker is useful for diagnosis of hepatocellular carcinoma with a supplementary role to α-fetoprotein. Cancer Res; 73(16); 4992–5002. ©2013 AACR.
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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
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999) · Xenobiotica · doi:10.1080/004982599238047 · Cited by 3,895
Glutathione Metabolism and Its Implications for Health
Wu, G., Lupton, J. R., Turner, N. D., Fang, Y.-Z., & Yang, S. (2004) · The Journal of Nutrition · doi:10.1093/jn/134.3.489 · Cited by 3,842
On Respiratory Impairment in Cancer Cells
Warburg, O. (1956) · Science · doi:10.1126/science.124.3215.269 · Cited by 3,468
Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., Haqq, A. M., Shah, S. H., Arlotto, M., Slentz, C. A., et al. (2009) · Cell Metabolism · doi:10.1016/j.cmet.2009.02.002 · Cited by 3,165
The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth
Christofk, H. R., Vander Heiden, M. G., Harris, M. H., Ramanathan, A., Gerszten, R. E., Wei, R., Fleming, M. D., Schreiber, S. L., & Cantley, L. C. (2008) · Nature · doi:10.1038/nature06734 · Cited by 2,820
Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression
Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., et al. (2009) · Nature · doi:10.1038/nature07762 · Cited by 2,234
Gao, P., Tchernyshyov, I., Chang, T.-C., Lee, Y.-S., Kita, K., Ochi, T., Zeller, K. I., De Marzo, A. M., Van Eyk, J. E., Mendell, J. T., et al. (2009) · Nature · doi:10.1038/nature07823 · Cited by 2,124
Self-citationQ's next: the diverse functions of glutamine in metabolism, cell biology and cancer
DeBerardinis, R. J., & Cheng, T. (2010) · Oncogene · doi:10.1038/onc.2009.358 · Cited by 1,365
AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress
Jeon, S.-M., Chandel, N. S., & Hay, N. (2012) · Nature · doi:10.1038/nature11066 · Cited by 1,132
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Lipid alterations in chronic liver disease and liver cancer
Paul, B., Lewinska, M., & Andersen, J. B. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100479 · Cited by 308
Effects of pre-analytical processes on blood samples used in metabolomics studies
Yin, P., Lehmann, R., & Xu, G. (2015) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-015-8565-x · Cited by 278
Self-citationHuang, L., Sun, H., Sun, L., Shi, K., Chen, Y., Ren, X., Ge, Y., Jiang, D., Liu, X., Knoll, W., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35696-2 · Cited by 266
Metabolic reprogramming in the tumor microenvironment of liver cancer
Lin, J., Rao, D., Zhang, M., & Gao, Q. (2024) · Journal of Hematology & Oncology · doi:10.1186/s13045-024-01527-8 · Cited by 251
“Moreover, numerous studies indicate enhanced amino acid metabolism in liver tumors compared to non-tumor tissues [1, 31, 37, 60, 61].”
BackgroundHarnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
Chen, B., Garmire, L., Calvisi, D. F., Chua, M.-S., Kelley, R. K., & Chen, X. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0240-9 · Cited by 154
Metadata
- Authors
- Huang, Q., Tan, Y., Yin, P., Ye, G., Gao, P., Lu, X., Wang, H., & Xu, G.
- Venue
- Cancer Research
- Published
- 2013-08-13
- Volume
- 73
- Issue
- 16
- Pages
- 4992-5002
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- OpenAlex
- W2142319787
- Cited by
- 414
- References
- 38
- FWCI
- 10.49
- Type
- journal article
- Language
- en
- Open access
- closed
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