Skip to main content

Advertisement

SpringerLink
Log in

Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

Abstract

The clinical exploration of urinary metabonomic analysis on discriminating between the top-two-incidence urological cancers, bladder and kidney cancers (BC and KC), is still virgin land. In this study, we discovered and evaluated the clinical utility of holistic metabonomic profiling and current single biomarker methods, and ultimately suggested a potential screening test for BC and KC. Urine metabonomic profiling for 19 BC patients, 25 KC patients, and 24 healthy controls was carried out using an LC–MS based platform, which utilized both reversed phase chromatography and hydrophilic interaction chromatography. The holistic method that refers to orthogonal partial least-squares-discriminant analysis based on all qualified profile data, successfully classified BC, KC and healthy control groups, showing 100 % sensitivity and specificity. Taurine, hippuric acid, phenylacetylglutamine and carnitine species contributed most to the BC and KC discrimination. The predictive power of each above metabolite is evaluated using receiver operator characteristic technique. Hippuric acid was found 10-fold decrease in concentration relative healthy controls, and performed the best as a biomarker for BC diagnosis, with its sensitivity and specificity of 78.9 and 86.5 %, respectively. Carnitine C10:3 was found 1.5-fold decrease, and reached 84.0 % of sensitivity and 60.5 % of specificity for KC diagnosis. In view of both sensitivity and specificity, the holistic method is more accurate for detecting BC and KC, than current single metabonomic biomarker methods, and it could be advocated as a prescreen to other forms of more invasive or uncomfortable screening. Moreover, this approach also demonstrates attractive performance in diagnosis of early stage (ES) BC and KC patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

BC:

Bladder cancer

KC:

Kidney cancer

RPLC:

Reversed phase liquid chromatography

HILIC:

Hydrophilic interaction chromatography

TNM:

Tumor nodes metastasis

ESI:

Electrospray ionization

TIC:

Total ion chromatogram

PCA:

Principal component analysis

OPLS-DA:

Orthogonal partial least-squares-discriminant analysis

ES:

Early stage

HA:

Hippuric acid

PAGN:

Phenylacetylglutamine

ROC:

Receiver operating characteristic

QC:

Quality control

VIP:

Variable importance in the project

AUC:

Area under the curve

References

  • An, Z. L., Chen, Y. H., Zhang, R. P., Song, Y. M., Sun, J. H., He, J. M., et al. (2010). Integrated ionization approach for RRLC–MS/MS-based metabonomics: finding potential biomarkers for lung cancer. Journal of Proteome Research, 9(8), 4071–4081. doi:10.1021/pr100265g.

    Article  PubMed  CAS  Google Scholar 

  • Beckonert, O., Monnerjahn, J., Bonk, U., & Leibfritz, D. (2003). Visualizing metabolic changes in breast-cancer tissue using 1H-NMR spectroscopy and self-organizing maps. NMR in Biomedicine, 16(1), 1–11. doi:10.1002/nbm.797.

    Article  PubMed  CAS  Google Scholar 

  • Bruce, S. J., Jonsson, P., Antti, H., Cloarec, O., Trygg, J., Marklund, S. L., et al. (2008). Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: from extraction to data analysis. Analytical Biochemistry, 372(2), 237–249. doi:10.1016/j.ab.2007.09.037.

    Article  PubMed  CAS  Google Scholar 

  • Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8–10), 341–351. doi:10.1002/cem.1006.

    Article  Google Scholar 

  • Catchpole, G., Platzer, A., Weikert, C., Kempkensteffen, C., Johannsen, M., Krause, H., et al. (2011). Metabolic profiling reveals key metabolic features of renal cell carcinoma. Journal of Cellular and Molecular Medicine, 15(1), 109–118. doi:10.1111/j.1582-4934.2009.00939.x.

    Article  PubMed  CAS  Google Scholar 

  • Chen, J., Wang, W., Lv, S., Yin, P., Zhao, X., Lu, X., et al. (2009). Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Analytica Chimica Acta, 650(1), 3–9. doi:10.1016/j.aca.2009.03.039.

    Article  PubMed  CAS  Google Scholar 

  • Claudino, W. M., Quattrone, A., Biganzoli, L., Pestrin, M., Bertini, I., & Di Leo, A. (2007). Metabolomics: Available results, current research projects in breast cancer, and future applications. Journal of Clinical Oncology, 25(19), 2840–2846. doi:10.1200/Jco.2006.09.7550.

    Article  PubMed  CAS  Google Scholar 

  • Clayton, T. A., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., et al. (2006). Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 440(7087), 1073–1077. doi:10.1038/nature04648.

    Article  PubMed  CAS  Google Scholar 

  • Cohen, H. T., & McGovern, F. J. (2005). Renal-cell carcinoma. New England Journal of Medicine, 353(23), 2477–2490. doi:10.1056/NEJMra043172.

    Article  PubMed  CAS  Google Scholar 

  • Cubbon, S., Antonio, C., Wilson, J., & Thomas-Oates, J. (2010). Metabolomic applications of HILIC–LC–MS. Mass Spectrometry Reviews, 29(5), 671–684. doi:10.1002/Mas.20252.

    Article  PubMed  CAS  Google Scholar 

  • Cubbon, S., Bradbury, T., Wilson, J., & Thomas-Oates, J. (2007). Hydrophilic interaction chromatography for mass spectrometric metabonomic studies of urine. Analytical Chemistry, 79(23), 8911–8918. doi:10.1021/ac071008v.

    Article  PubMed  CAS  Google Scholar 

  • Evan, G. I., & Vousden, K. H. (2001). Proliferation, cell cycle and apoptosis in cancer. Nature, 411(6835), 342–348. doi:10.1038/35077213.

    Article  PubMed  CAS  Google Scholar 

  • Gamagedara, S., Shi, H., & Ma, Y. (2012). Quantitative determination of taurine and related biomarkers in urine by liquid chromatography–tandem mass spectrometry. Analytical and Bioanalytical Chemistry, 402(2), 763–770. doi:10.1007/s00216-011-5491-4.

    Article  PubMed  CAS  Google Scholar 

  • Gika, H. G., Theodoridis, G. A., & Wilson, I. D. (2008). Hydrophilic interaction and reversed-phase ultra-performance liquid chromatography TOF-MS for metabonomic analysis of Zucker rat urine. Journal of Separation Science, 31(9), 1598–1608. doi:10.1002/jssc.200700644.

    Article  PubMed  CAS  Google Scholar 

  • Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC–MS-based method for metabonomic analysis: Application to human urine. Journal of Proteome Research, 6(8), 3291–3303. doi:10.1021/Pr070183p.

    Article  PubMed  CAS  Google Scholar 

  • Greene, F. L., Page, D. L., Fleming, I. D., Fritz, A., Balch, C. M., Haller, D. G., et al. (Eds.). (2002). AJCC cancer staging manual (6th ed.). New York: Springer.

    Google Scholar 

  • Issaq, H. J., Nativ, O., Waybright, T., Luke, B., Veenstra, T. D., Issaq, E. J., et al. (2008). Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. The Journal of Urology, 179(6), 2422–2426. doi:10.1016/j.juro.2008.01.084.

    Article  PubMed  CAS  Google Scholar 

  • Kim, K., Aronov, P., Zakharkin, S. O., Anderson, D., Perroud, B., Thompson, I. M., et al. (2009). Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Molecular and Cellular Proteomics, 8(3), 558–570. doi:10.1074/mcp.M800165-MCP200.

    Article  PubMed  CAS  Google Scholar 

  • Kind, T., & Fiehn, O. (2010). Advances in structure elucidation of small molecules using mass spectrometry. Bioanalytical Reviews, 2(1), 23–60. doi:10.1007/s12566-010-0015-9.

    Article  PubMed  Google Scholar 

  • Kind, T., Tolstikov, V., Fiehn, O., & Weiss, R. H. (2007). A comprehensive urinary metabolomic approach for identifying kidney cancer. Analytical Biochemistry, 363(2), 185–195. doi:10.1016/j.ab.2007.01.028.

    Article  PubMed  CAS  Google Scholar 

  • Kutikov, A., Egleston, B. L., Wong, Y.-N., & Uzzo, R. G. (2010). Evaluating overall survival and competing risks of death in patients with localized renal cell carcinoma using a comprehensive nomogram. Journal of Clinical Oncology, 28(2), 311–317. doi:10.1200/jco.2009.22.4816.

    Article  PubMed  Google Scholar 

  • Lin, L., Hang, W., Huang, Z. Z., Gao, Y., Yan, X. M., & Xing, J. C. (2011). LC–MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. Journal of Proteome Research, 10(3), 1396–1405. doi:10.1021/pr101161u.

    Article  PubMed  CAS  Google Scholar 

  • Linehan, W. M., Walther, M. M., & Zbar, B. (2003). The genetic basis of cancer of the kidney. Journal of Urology, 170(6), 2163–2172. doi:10.1097/01.ju.0000096060.92397.ed.

    Article  PubMed  CAS  Google Scholar 

  • McClinton, S., Moffat, L. E., Horrobin, D. F., & Manku, M. S. (1991). Abnormalities of essential fatty acid distribution in the plasma phospholipids of patients with bladder cancer. British Journal of Cancer, 63(2), 314–316.

    Article  PubMed  CAS  Google Scholar 

  • Michael, A., & Pandha, H. S. (2003). Renal-cell carcinoma: tumour markers, T-cell epitopes, and potential for new therapies. Lancet Oncology, 4(4), 215–223. doi:10.1016/S1470-2045(03)01044-1.

    Article  PubMed  CAS  Google Scholar 

  • Mitra, A. P., & Cote, R. J. (2010). Molecular screening for bladder cancer: progress and potential. Nature Reviews Urology, 7(1), 11–20. doi:10.1038/nrurol.2009.236.

    Article  PubMed  CAS  Google Scholar 

  • Mohammed, S. I., & Rahman, M. (2008). Proteomics and genomics of urinary bladder cancer. Proteomics Clinical Application, 2(9), 1194–1207. doi:0.1002/prca.200780089.

    Article  CAS  Google Scholar 

  • Moreno, A., Rey, M., Montane, J. M., Alonso, J., & Arús, C. (1993). 1H NMR spectroscopy of colon tumors and normal mucosal biopsies; elevated taurine levels and reduced polyethyleneglycol absorption in tumors may have diagnostic significance. NMR in Biomedicine, 6(2), 111–118. doi:10.1002/nbm.1940060202.

    Article  PubMed  CAS  Google Scholar 

  • Morrissey, J. J., London, A. N., Luo, J. Q., & Kharasch, E. D. (2010). Urinary biomarkers for the early diagnosis of kidney cancer. Mayo Clinic Proceedings, 85(5), 413–421. doi:10.4065/mcp.20090709.

    Article  PubMed  CAS  Google Scholar 

  • Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 1181–1189. doi:10.1080/004982599238047.

    Article  PubMed  CAS  Google Scholar 

  • Ott, O. J., Rödel, C., Weiss, C., Wittlinger, M., St. Krause, F., Dunst, J., et al. (2009). Radiochemotherapy for bladder cancer. Clinical Oncology, 21(7), 557–565. doi:10.1016/j.clon.2009.05.005.

    Article  PubMed  CAS  Google Scholar 

  • Paik, M. J., Kim, J. W., Lee, G., Moon, S. M., Park, M. J., Hong, S. K., et al. (2010). Metabolomic screening and star pattern recognition by urinary amino acid profile analysis from bladder cancer patients. Metabolomics, 6(2), 202–206. doi:10.1007/s11306-010-0199-6.

    Article  Google Scholar 

  • Pasikanti, K. K., Esuvaranathan, K., Ho, P. C., Mahendran, R., Kamaraj, R., Wu, Q. H., et al. (2010). Noninvasive urinary metabonomic diagnosis of human bladder cancer. Journal of Proteome Research, 9(6), 2988–2995. doi:10.1021/Pr901173v.

    Article  PubMed  CAS  Google Scholar 

  • Poynard, T., Halfon, P., Castera, L., Munteanu, M., Imbert-Bismut, F., Ratziu, V., et al. (2007). Standardization of ROC curve areas for diagnostic evaluation of liver fibrosis markers based on prevalences of fibrosis stages. Clinical Chemistry, 53(9), 1615–1622. doi:10.1373/clinchem.2007.085795.

    Article  PubMed  CAS  Google Scholar 

  • Qin, F., Zhao, Y. Y., Sawyer, M. B., & Li, X. F. (2008). Hydrophilic interaction liquid chromatography-tandem mass spectrometry determination of estrogen conjugates in human urine. Analytical Chemistry, 80(9), 3404–3411. doi:10.1021/ac702613k.

    Article  PubMed  CAS  Google Scholar 

  • Quackenbush, J. (2006). Microarray analysis and tumor classification. New England Journal of Medicine, 354(23), 2463–2472. doi:10.1056/NEJMra042342.

    Article  PubMed  CAS  Google Scholar 

  • Siegel, R., Ward, E., Brawley, O., & Jemal, A. (2011). Cancer statistics, 2011. CA: A Cancer Journal for Clinicians, 61(4), 212–236. doi:10.3322/caac.20121.

    Article  Google Scholar 

  • Slupsky, C. M., Steed, H., Wells, T. H., Dabbs, K., Schepansky, A., Capstick, V., et al. (2010). Urine metabolite analysis offers potential early diagnosis of ovarian and breast cancers. Clinical Cancer Research, 16(23), 5835–5841. doi:10.1158/1078-0432.ccr-10-1434.

    Article  PubMed  CAS  Google Scholar 

  • Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009). Clinical applications of metabolomics in oncology: A review. Clinical Cancer Research, 15(2), 431–440. doi:10.1158/1078-0432.ccr-08-1059.

    Article  PubMed  CAS  Google Scholar 

  • Srivastavaa, S., Roy, R., Singh, S., Kumar, P., Dalela, D., Sankhwarc, S. N., et al. (2010). Taurine—a possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by 1H NMR spectroscopy. Cancer Biomarkers, 6(1), 11–20. doi:10.3233/CBM-2009-0115.

    Google Scholar 

  • Theodoridis, G., Gika, H. G., & Wilson, I. D. (2011). Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies. Mass Spectrometry Reviews, 30(5), 884–906. doi:10.1002/mas.20306.

    CAS  Google Scholar 

  • Umetrics, A. (2005). User’s guide to SIMCA-P, SIMCA-P+ version 12.0.

  • Wiklund, S., Johansson, E., Sjostrom, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., et al. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry, 80(1), 115–122. doi:10.1021/Ac0713510.

    Article  PubMed  CAS  Google Scholar 

  • Yang, Q., Shi, X., Wang, Y., Wang, W., He, H., Lu, X., et al. (2010). Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC–MS system. Journal of Separation Science, 33(10), 1495–1503. doi:10.1002/jssc.200900798.

    Article  PubMed  CAS  Google Scholar 

  • Yin, P., Wan, D., Zhao, C., Chen, J., Zhao, X., Wang, W., et al. (2009). A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Molecular BioSystems, 5(8), 868–876. doi:10.1039/b820224a.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge the financial support from the National Natural Science Foundation of China, Department of Science & Technology of Fujian Province (2009D023), and the Medical Center Construction Foundation of Xiamen.

Author information

Authors and Affiliations

  1. Department of Chemistry, Key Laboratory of Analytical Sciences, College of Chemistry and Chemical Engineering, Xiamen University, 422 Simingnan Road, Xiamen, 361005, China

    Zhenzhen Huang, Yongjing Chen, Wei Hang, Yao Gao, Lin Lin & Xiaomei Yan

  2. State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China

    Wei Hang

  3. Department of Medicine, University of California, San Diego, CA, USA

    Daniel Y. Li

  4. Department of Urology, Xiamen First Hospital, Xiamen, China

    Jinchun Xing

Authors
  1. Zhenzhen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongjing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Hang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel Y. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinchun Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaomei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Hang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 218 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, Z., Chen, Y., Hang, W. et al. Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers. Metabolomics 9, 119–129 (2013). https://doi.org/10.1007/s11306-012-0433-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11306-012-0433-5

Keywords

Search

Navigation