Pathway analysis of kidney cancer using proteomics and metabolic profiling

Bertrand Perroud, Jinoo Lee, Nelly Valkova, Amy Dhirapong, Pei Yin Lin, Oliver Fiehn, Dietmar Kültz, Robert H Weiss

Research output: Contribution to journalArticle

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Abstract

Background: Renal cell carcinoma (RCC) is the sixth leading cause of cancer death and is responsible for 11,000 deaths per year in the US. Approximately one-third of patients present with disease which is already metastatic and for which there is currently no adequate treatment, and no biofluid screening tests exist for RCC. In this study, we have undertaken a comprehensive proteomic analysis and subsequently a pathway and network approach to identify biological processes involved in clear cell RCC (ccRCC). We have used these data to investigate urinary markers of RCC which could be applied to high-risk patients, or to those being followed for recurrence, for early diagnosis and treatment, thereby substantially reducing mortality of this disease. Results: Using 2-dimensional electrophoresis and mass spectrometric analysis, we identified 31 proteins which were differentially expressed with a high degree of significance in ccRCC as compared to adjacent non-malignant tissue, and we confirmed some of these by immunoblotting, immunohistochemistry, and comparison to published transcriptomic data. When evaluated by several pathway and biological process analysis programs, these proteins are demonstrated to be involved with a high degree of confidence (p values < 2.0 E-05) in glycolysis, propanoate metabolism, pyruvate metabolism, urea cycle and arginine/proline metabolism, as well as in the non-metabolic p53 and FAS pathways. In a pilot study using random urine samples from both ccRCC and control patients, we performed metabolic profiling and found that only sorbitol, a component of an alternative glycolysis pathway, is significantly elevated at 5.4-fold in RCC patients as compared to controls. Conclusion: Extensive pathway and network analysis allowed for the discovery of highly significant pathways from a set of clear cell RCC samples. Knowledge of activation of these processes will lead to novel assays identifying their proteomic and/or metabolomic signatures in biofluids of patient at high risk for this disease; we provide pilot data for such a urinary bioassay. Furthermore, we demonstrate how the knowledge of networks, processes, and pathways altered in kidney cancer may be used to influence the choice of optimal therapy.

Original languageEnglish (US)
Article number64
JournalMolecular Cancer
Volume5
DOIs
StatePublished - Nov 24 2006

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Kidney Neoplasms
Renal Cell Carcinoma
Metabolism
Proteomics
Biological Phenomena
Sorbitol
Bioassay
Propionates
Glycolysis
Electric network analysis
Electrophoresis
Pyruvic Acid
Proline
Arginine
Urea
Assays
Screening
Proteins
Chemical activation
Cells

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Cancer Research
  • Molecular Medicine

Cite this

Pathway analysis of kidney cancer using proteomics and metabolic profiling. / Perroud, Bertrand; Lee, Jinoo; Valkova, Nelly; Dhirapong, Amy; Lin, Pei Yin; Fiehn, Oliver; Kültz, Dietmar; Weiss, Robert H.

In: Molecular Cancer, Vol. 5, 64, 24.11.2006.

Research output: Contribution to journalArticle

Perroud, B, Lee, J, Valkova, N, Dhirapong, A, Lin, PY, Fiehn, O, Kültz, D & Weiss, RH 2006, 'Pathway analysis of kidney cancer using proteomics and metabolic profiling', Molecular Cancer, vol. 5, 64. https://doi.org/10.1186/1476-4598-5-64
Perroud B, Lee J, Valkova N, Dhirapong A, Lin PY, Fiehn O et al. Pathway analysis of kidney cancer using proteomics and metabolic profiling. Molecular Cancer. 2006 Nov 24;5. 64. https://doi.org/10.1186/1476-4598-5-64
Perroud, Bertrand ; Lee, Jinoo ; Valkova, Nelly ; Dhirapong, Amy ; Lin, Pei Yin ; Fiehn, Oliver ; Kültz, Dietmar ; Weiss, Robert H. / Pathway analysis of kidney cancer using proteomics and metabolic profiling. In: Molecular Cancer. 2006 ; Vol. 5.
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