Pathway analysis of kidney cancer using proteomics and metabolic profiling

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.

Figure 1

Proteomic analysis of RCC. 2-D gel electrophoresis shows proteins decreased (blue) or increased (red) in ccRCC as compared to adjacent normal renal tissue. Numbers 101 – 110 refer to the 10 internal standard spots used for normalization (see Materials and Methods)

Figure 2

MS and MS/MS spectra for HSP27. A) MS spectrum of HSP27. The peptide whose MS/MS spectrum is shown in panel B is indicated. B) MS/MS spectrum of the peptide ion m/z 1163 obtained in CID mode.

Figure 3

Anti-apoptotic proteins are upregulated in ccRCC as confirmed by immunoblotting. Three RCC cell lines, and 2 tumors which were used in the proteomic analysis were immunoblotted with Hsp27 or phospho-Hsp27 antibodies; actin is a loading control. Solid line indicates same kidney.

Figure 4

The HIF-1 target, PKM2, is increased in ccRCC as confirmed by immunoblotting. Two tumors which were used in the proteomic analysis were immunoblotted with PKM2 antibody; actin is a loading control. Solid line indicates same kidney.

Figure 5

Network of interactions between the 31 differentially expressed proteins. TNF is not one of the 31 proteins but is shown here as it interacts with many components of the network. Nodes represent proteins and links indicate known interactions or modulations from neighboring nodes. Proteins at the bottom of the figure are in the 31 protein set but are not involved in the network. Links scheme -gray square: regulation; -green square: direct regulation; -green circle: promoter binding; -green open square: molecular transport; -blue open square: molecular synthesis; -blue square: expression; -yellow circle: protein modification; -purple circle: binding.

Figure 6

Significant Panther Biological Process (p < 0.01) shown in yellow. The RCC 31 protein set is compared to a human reference set of 23,401 translated transcripts and allocated in 242 biological process classes. Two sections of the histogram, encompassing glycolysis and amino acid metabolism pathways, are shown here; the complete histogram is available in Supplemental Fig. 3. Red bars correspond to percentage of proteins attributed to a given process, when looking at the 23,401 human reference set. Blue bars indicate the percentage of proteins from the RCC 31 protein sets in that class. Yellow bars indicate the process which we identified from our proteomic analysis to have a significance greater than 0.01. Glycolysis is the most significant pathway identified by this analysis.

Figure 7

Proteins differentially regulated in RCC involved in carbohydrate metabolism are shown overlaid on the glycolysis and gluconeogenesis pathway KEGG #00010 diagram. Anaerobic glycolysis is upregulated while other carbohydrates metabolism appears down regulated. The enzymes colored in red correspond to the proteins which we found upregulated in ccRCC, and in green those (or pathways associated) we found downregulated in ccRCC. We added the corresponding gene symbol next to the enzyme or enriched process.