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Metabolomics Approaches and Applications in Prostate Cancer Research

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Abstract

Prostate cancer is a leading cause of cancer deaths in men worldwide. Although prostate-specific antigen (PSA) has been extensively used as a serum biomarker to detect prostate cancer, this screening method has suffered from a lack of specificities and sensitivities. The successful prevention and treatment of prostate cancer relies on the early and accurate detection of the disease; therefore, more sensitive biomarkers are urgently needed. Prostate has long been known to exhibit unique metabolite profiles, fortunately, metabolomics, the study of all metabolites produced in the body, can be considered most closely related to a patient’s phenotype. It may provide clinically useful biomarkers applied toward identifying metabolic alterations in prostate cancer and has introduced new insights into the pathology of prostate cancer. This advanced bioanalytic method may now open door for diagnostics. Metabolomics has a great and largely potential in the field of disease, and the analysis of the cancer metabolome to identify novel biomarkers and targets can now be undertaken in many research laboratories. In this review, we take a closer look at the metabolomics in the field of prostate cancer and highlight the interesting publications as references for the interested reader.

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References

  1. Yachida, S., Jones, S., Bozic, I., Antal, T., Leary, R., Fu, B., et al. (2010). Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature, 467(7319), 1114–1117.

    Article  CAS  Google Scholar 

  2. Richman, E. L., Kenfield, S. A., Stampfer, M. J., Giovannucci, E. L., & Chan, J. M. (2011). Egg, red meat, and poultry intake and risk of lethal prostate cancer in the prostate-specific antigen-era: incidence and survival. Cancer Prevention Research (Philadelphia, Pa.), 4(12), 2110–2121.

    Article  CAS  Google Scholar 

  3. DeNicola, G. M., Karreth, F. A., Humpton, T. J., Gopinathan, A., Wei, C., Frese, K., et al. (2011). Oncogene-induced Nrf2 transcription promotes ROS detoxification and tumorigenesis. Nature, 475(7354), 106–109.

    Article  CAS  Google Scholar 

  4. Ru, P., Steele, R., Nerurkar, P. V., Phillips, N., & Ray, R. B. (2011). Bitter melon extract impairs prostate cancer cell-cycle progression and delays prostatic intraepithelial neoplasia in TRAMP model. Cancer Prevention Research (Philadelphia, Pa.), 4(12), 2122–2130.

    Article  Google Scholar 

  5. Sun, T., Oh, W. K., Jacobus, S., Regan, M., Pomerantz, M., Freedman, M. L., et al. (2011). The impact of common genetic variations in genes of the sex hormone metabolic pathways on steroid hormone levels and prostate cancer aggressiveness. Cancer Prevention Research (Philadelphia, Pa.), 4(12), 2044–2050.

    Article  CAS  Google Scholar 

  6. Xu, J., Liu, C., Cai, S., Dong, J., Li, X., Feng, J., et al. (2013). Metabolomic profilings of urine and serum from high fat-fed rats via 1H NMR spectroscopy and pattern recognition. Applied Biochemistry and Biotechnology, 169(4), 1250–1261.

    Article  CAS  Google Scholar 

  7. Ten Ori, L., Oakman, C., Claudino, W. M., Bernini, P., Cappadona, S., Nepi, S., et al. (2012). Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Molecular Oncology, 6(4), 437–444.

    Article  Google Scholar 

  8. OuYang, D., Xu, J., Huang, H., & Chen, Z. (2011). Metabolomic profiling of serum from human pancreatic cancer patients using 1H NMR spectroscopy and principal component analysis. Applied Biochemistry and Biotechnology, 165(1), 148–154.

    Article  Google Scholar 

  9. Zhang, A. H., Sun, H., & Wang, X. J. (2013). Recent advances in metabolomics in neurological disease, and future perspectives. Analytical and Bioanalytical Chemistry, 405(25), 8143–8150.

    Article  CAS  Google Scholar 

  10. Arakaki, A. K., Skolnick, J., & McDonald, J. F. (2008). Marker metabolites can be therapeutic targets as well. Nature, 456, 443.

    Article  CAS  Google Scholar 

  11. Kleiner, M., Wentrup, C., Lott, C., Teeling, H., Wetzel, S., Young, J., et al. (2012). Metaproteomics of a gutless marine worm and its symbiotic microbial community reveal unusual pathways for carbon and energy use. Proceedings of the National Academy of Sciences of the United States of America, 109(19), E1173–E1182.

    Article  CAS  Google Scholar 

  12. Zhang, A., Sun, H., Han, Y., Yuan, Y., Wang, P., Song, G., et al. (2012). Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach. Analyst, 137(18), 4200–4208.

    Article  CAS  Google Scholar 

  13. Zhang, A., Sun, H., & Wang, X. (2012). Saliva metabolomics opens door to biomarker discovery, disease diagnosis, and treatment. Applied Biochemistry and Biotechnology, 168(6), 1718–1727.

    Article  CAS  Google Scholar 

  14. Zhang, A., Sun, H., Yan, G., Wang, P., Han, Y., & Wang, X. (2014). Metabolomics in diagnosis and biomarker discovery of colorectal cancer. Cancer Letters, 345(1), 17–20.

    Article  CAS  Google Scholar 

  15. Ding, M. Z., Lu, H., Cheng, J. S., Chen, Y., Jiang, J., Qiao, B., et al. (2012). Comparative metabolomic study of Penicillium chrysogenum during pilot and industrial penicillin fermentations. Applied Biochemistry and Biotechnology, 168(5), 1223–1238.

    Article  CAS  Google Scholar 

  16. Park, C., Yun, S., Lee, S. Y., Park, K., & Lee, J. (2012). Metabolic profiling of Klebsiella oxytoca: evaluation of methods for extraction of intracellular metabolites using UPLC/Q-TOF-MS. Applied Biochemistry and Biotechnology, 167(3), 425–438.

    Article  CAS  Google Scholar 

  17. Patterson, A. D., Maurhofer, O., Beyoglu, D., Lanz, C., Krausz, K. W., Pabst, T., et al. (2011). Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Research, 71(21), 6590–6600.

    Article  CAS  Google Scholar 

  18. Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K., Chan, Q., et al. (2008). Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 453, 396–401.

    Article  CAS  Google Scholar 

  19. Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., et al. (2011). Metabolite profiles and the risk of developing diabetes. Nature Medicine, 17(4), 448–453.

    Article  Google Scholar 

  20. Zhang, A. H., Qiu, S., Xu, H. Y., Sun, H., & Wang, X. J. (2014). Metabolomics in diabetes. Clinica Chimica Acta, 429, 106–110.

    Article  CAS  Google Scholar 

  21. Moazzami, A. A., Zhang, J. X., Kamal-Eldin, A., Aman, P., Hallmans, G., Johansson, J. E., et al. (2011). Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. Journal of Nutrition, 141(12), 2126–2132.

    Article  CAS  Google Scholar 

  22. Stephan, C., Siemssen, K., Cammann, H., Friedersdorff, F., Deger, S., Schrader, M., et al. (2011). Between-method differences in prostate-specific antigen assays affect prostate cancer risk prediction by nomograms. Clinical Chemistry, 57(7), 995–1004.

    Article  CAS  Google Scholar 

  23. Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457(7231), 910–914.

    Article  CAS  Google Scholar 

  24. Wang, X., Zhang, A., Han, Y., Wang, P., Sun, H., Song, G., et al. (2012). Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease. Molecular and Cellular Proteomics, 11(8), 370–380.

    Article  Google Scholar 

  25. Gu, H., Pan, Z., Xi, B., Asiago, V., Musselman, B., & Raftery, D. (2011). Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer. Analitica Chimica Acta, 686(1–2), 57–63.

    Article  CAS  Google Scholar 

  26. Zhang, A. H., Sun, H., Qiu, S., & Wang, X. J. (2013). Metabolomics in noninvasive breast cancer. Clinica Chimica Acta, 424, 3–7.

    Article  CAS  Google Scholar 

  27. Kaushik, A. K., Vareed, S. K., Basu, S., Putluri, V., Putluri, N., Panzitt, K., et al. (2014). Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer. Journal of Proteome Research, 13(2), 1088–1100.

    Article  CAS  Google Scholar 

  28. Oakman, C., Tenori, L., Biganzoli, L., Santarpia, L., Cappadona, S., Luchinat, C., et al. (2011). Uncovering the metabolomic fingerprint of breast cancer. International Journal of Biochemistry and Cell Biology, 43(7), 1010–1020.

    Article  CAS  Google Scholar 

  29. Vermeer, L. S., Fruhwirth, G. O., Pandya, P., Ng, T., & Mason, A. J. (2012). NMR metabolomics of MTLn3E breast cancer cells identifies a role for CXCR4 in lipid and choline regulation. Journal of Proteome Research, 11(5), 2996–3003.

    Article  CAS  Google Scholar 

  30. Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics, 6(1), 78–95.

    Article  CAS  Google Scholar 

  31. Bailey, S. T., Shin, H., Westerling, T., Liu, X. S., & Brown, M. (2012). Estrogen receptor prevents p53-dependent apoptosis in breast cancer. Proceedings of the National Academy of Sciences of the United States of America, 109(44), 18060–18065.

    Article  CAS  Google Scholar 

  32. Klawitter, J., Shokati, T., Moll, V., Christians, U., & Klawitter, J. (2010). Effects of lovastatin on breast cancer cells: a proteo-metabonomic study. Breast Cancer Research, 12(2), R16.

    Article  Google Scholar 

  33. ter Sit, B., Bathen, T. F., Singstad, T. E., Fjøsne, H. E., Lundgren, S., Halgunset, J., et al. (2010). Quantification of metabolites in breast cancer patients with different clinical prognosis using HR MAS MR spectroscopy. NMR in Biomedicine, 23(4), 424–431.

    Google Scholar 

  34. Rivas-Ubach, A., Sardans, J., Pérez-Trujillo, M., Estiarte, M., & Peñuelas, J. (2012). Strong relationship between elemental stoichiometry and metabolome in plants. Proceedings of the National Academy of Sciences of the United States of America, 109(11), 4181–4186.

    Article  CAS  Google Scholar 

  35. Wu, R., Wu, Z., Wang, X., Yang, P., Yu, D., Zhao, C., et al. (2012). Metabolomic analysis reveals that carnitines are key regulatory metabolites in phase transition of the locusts. Proceedings of the National Academy of Sciences of the United States of America, 109(9), 3259–3263.

    Article  CAS  Google Scholar 

  36. Finley, L. W., Lee, J., Souza, A., Desquiret-Dumas, V., Bullock, K., Rowe, G. C., et al. (2012). Skeletal muscle transcriptional coactivator PGC-1α mediates mitochondrial, but not metabolic, changes during calorie restriction. Proceedings of the National Academy of Sciences of the United States of America, 109(8), 2931–2936.

    Article  CAS  Google Scholar 

  37. Mintz-Oron, S., Meir, S., Malitsky, S., Ruppin, E., Aharoni, A., & Shlomi, T. (2012). Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proceedings of the National Academy of Sciences of the United States of America, 109(1), 339–344.

    Article  CAS  Google Scholar 

  38. Jiang, Y., Cheng, X., Wang, C., & Ma, Y. (2010). Quantitative determination of sarcosine and related compounds in urinary samples by liquid chromatography with tandem mass spectrometry. Analytical Chemistry, 82(21), 9022–9027.

    Article  CAS  Google Scholar 

  39. Ward, J. L., Baker, J. M., Llewellyn, A. M., Hawkins, N. D., & Beale, M. H. (2011). Metabolomic analysis of Arabidopsis reveals hemiterpenoid glycosides as products of a nitrate ion-regulated, carbon flux overflow. Proceedings of the National Academy of Sciences of the United States of America, 108(26), 10762–10767.

    Article  CAS  Google Scholar 

  40. Burns, M. A., He, W., Wu, C. L., & Cheng, L. L. (2004). Quantitative pathology in tissue MR spectroscopy based human prostate metabolomics. Technology in Cancer Research & Treatment, 3(6), 591–598.

    Google Scholar 

  41. Wu, H., Liu, T., Ma, C., Xue, R., Deng, C., Zeng, H., et al. (2011). GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization. Analytical and Bioanalytical Chemistry, 401(2), 635–646.

    Article  CAS  Google Scholar 

  42. Wu, C. L., Jordan, K. W., Ratai, E. M., Sheng, J., Adkins, C. B., Defeo, E. M., et al. (2010). Metabolomic imaging for human prostate cancer detection. Science Translational Medicine, 2(16), 16ra8.

    Article  Google Scholar 

  43. Huang, G., Liu, X., Jiao, L., Xu, C., Zhang, Z., Wang, L., et al. (2014). Metabolomic evaluation of the response to endocrine therapy in patients with prostate cancer. European Journal of Pharmacology, 729, 132–137.

    Article  CAS  Google Scholar 

  44. Maxeiner, A., Adkins, C. B., Zhang, Y., Taupitz, M., Halpern, E. F., McDougal, W. S., et al. (2010). Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles. Prostate, 70(7), 710–717.

    Google Scholar 

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Acknowledgments

This work was supported by the grants from the Key Program of Natural Science Foundation of State (Grant No. 90709019, 81173500, 81373930, 81302905, 81102556, 81202639), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2011BAI03B03, 2011BAI03B06, 2011BAI03B08), National Key Subject of Drug Innovation (Grant No. 2009ZX09502-005), and Foundation of Heilongjiang University of Chinese Medicine (Grant no. 201209).

Conflict of Interests

The authors have declared that they have no competing interests.

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Authors and Affiliations

  1. Department of Pharmaceutical Analysis, Key Laboratory of Metabolomics and Chinmedomics, National TCM Key Laboratory of Serum Pharmacochemistry, Heilongjiang University of Chinese Medicine, Heping Road, Harbin, 150010, China

    Aihua Zhang, Guangli Yan, Ying Han & Xijun Wang

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  1. Aihua Zhang
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  2. Guangli Yan
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Correspondence to Aihua Zhang or Xijun Wang.

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Zhang, A., Yan, G., Han, Y. et al. Metabolomics Approaches and Applications in Prostate Cancer Research. Appl Biochem Biotechnol 174, 6–12 (2014). https://doi.org/10.1007/s12010-014-0955-6

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