Quantitative proteomic analysis reveals potential diagnostic markers and pathways involved in pathogenesis of renal cell carcinoma

Abstract

There are no serum biomarkers for the accurate diagnosis of clear cell renal cell carcinoma (ccRCC). Diagnosis and decision of nephrectomy rely on imaging which is not always accurate. Non-invasive diagnostic biomarkers are urgently required. In this study, we preformed quantitative proteomics analysis on a total of 199 patients including 30 matched pairs of normal kidney and ccRCC using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify differentially expressed proteins. We found 55 proteins significantly dysregulated in ccRCC compared to normal kidney tissue. 54 were previously reported to play a role in carcinogenesis, and 39 are secreted proteins. Dysregulation of alpha-enolase (ENO1), L-lactate dehydrogenase A chain (LDHA), heat shock protein beta-1 (HSPB1/Hsp27), and 10 kDa heat shock protein, mitochondrial (HSPE1) was confirmed in two independent sets of patients by western blot and immunohistochemistry. Pathway analysis, validated by PCR, showed glucose metabolism is altered in ccRCC compared to normal kidney tissue. In addition, we examined the utility of Hsp27 as biomarker in serum and urine. In ccRCC patients, Hsp27 was elevated in the urine and serum and high serum Hsp27 was associated with high grade (Grade 3-4) tumors. These data together identify potential diagnostic biomarkers for ccRCC and shed new light on the molecular mechanisms that are dysregulated and contribute to the pathogenesis of ccRCC. Hsp27 is a promising diagnostic marker for ccRCC although further large-scale studies are required. Also, molecular profiling may help pave the road to the discovery of new therapies.

Figure 1. Hierarchical clustering analysis of dysregulated proteins between ccRCC and normal kidney tissues

Clustering analysis was performed based on 345 proteins for which quantitative information was available. The samples clustered into two main groups: one that contained the 9 of 10 ccRCC samples (C1-C5, C7-C10); and second contained all normal samples (N1-N10) plus one cancer sample; C6. The difference in expressions of dysregulated proteins between ccRCC and normal kidney samples was statistically significant (p<0.001).

Figure 2. Verification of protein dysregulation in ccRCC by Western blot and immunohistochemical analyses

A: Representative blots showing the expression of proteins in normal kidney tissues (N1, N2) and ccRCC (C1, C2). For AHNAK, ENO1, and HSP27, expression was significantly higher and for HSPE1 it was significantly lower in cancer compared to normal kidney tissue, β-actin was used as a loading control. B: Graphical representation of the average fold change in expression of the proteins between ten ccRCCs and matched normal specimens as determined by densitometry (C/N). Expressions of protein in normal samples were normalized. C-J: Representative photomicrographs showing differential expression of ENO1, HSPB1, HSPE1 and LDHA in ccRCC compared to normal kidney tissue by immunohistochemistry (Original magnification × 200).

Figure 3. The involvement of dysregulated proteins in kidney cancer in metabolic pathways

A: Most of the upregulated proteins are enzymes which catalyze the reactions of glycolysis, citric acid cycle, metabolism and catabolism of Acetyl-CoA. ETFB serves as a specific electron acceptor for several dehydrogenases, including five acytyl-CoA dehydrogenases (AD), glutaryl-CoA and sarcosine dehydrogenase; and HSP27 forms a complex with G6PDH that increased its activity. Upregulated proteins are shown in red and downregulated ones in green. B: Visualization of protein-protein interactions for dysregulated proteins in ccRCC using STRING analysis. Dysregulated proteins were used as input for STRING and are represented as spheres of distinct colors. Blue lines represent interactions between proteins and the thickness of the lines display the level of confidence associated with each interaction. Our dysregulated proteins formed one main cluster. Within this cluster, we identified three mini-clusters which contained proteins related to cell metabolism. C: PCR validation of the down-regulation of genes involved in the TCA cycle. We found that 18 of the 28 (64%) genes examined had significant decreased expression in ccRCC tissues when compared to normal kidney tissue. SUCLG1, p=0.003; IDH3A, p=0.026; PDHB, p<0.001; SUCLG2, p=0.006; SDHD, p=0.012; DLD, p=0.020; SDHB, p=0.007; PCK2, p=0.037; MDH2, p=0.043; MDH1, p<0.001; IDH2, p=0.030; DLST, p=0.037; FH, p=0.006; SUCLA2, p=0.001; DLAT, p<0.001; SDHC, p<0.001; IDH3B, p=0.006; IDH3G, p<0.001. D: Bar graph showing differential expression of Hsp27 in urine. We assayed the expression of Hsp27 by ELISA in 21 pre-operative RCC patients and 9 individuals with no malignancy. The average expression in RCC patients was significantly higher than those with no malignancy (1.822ng/mL vs. 0.3365ng/mL, respectively, p<0.05).