Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

Abstract

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

High expression of Aldolase A predicts poor survival in patients with clear-cell renal cell carcinoma

“…Therefore, it would be appropriate to speculate that ALDOA might also promote progression of renal carcinoma. In the present study, we found that ALDOA expression was increased along with elevated T stage, which was consistent with a previous study 13. ALDOA expression was less observed in the lower stages, and ALDOA seemed to correlate with OS in every stage except stage I.…”

“…Aberrant expression of ALDOA was found in various cancers,15,17,18 suggesting enhanced glycolysis in these cancer cells. Abnormal expression of ALDOA was also observed in renal carcinoma by a recent proteotranscriptomic study, which revealed that ALDOA were decreased at early stages relative to normal tissue but increased at later stages 13. However, the prognostic value of ALDOA in renal carcinoma remained unknown.…”

“…Reliance on aerobic glycolysis was identified as a common feature in sporadic and hereditary forms of kidney cancer, including clear-cell RCC (ccRCC) 10–13. Aldolase A (ALDOA) is a key enzyme in glycolysis responsible for catalyzing the reversible conversion of fructose-1,6-bisphosphate to glyceraldehyde-3-phosphate and dihydroxyacetone phosphate 14.…”

Defining the human kidney N‐glycome in normal and cancer tissues using MALDI imaging mass spectrometry

“…Previous collaborative genomic and proteomic studies from our groups reported the use of paired ccRCC tissues representing nontumor and tumor stages 1 to 4 . These studies identified multiple transcriptomic and proteomic changes in tumors associated with a fundamental Warburg effect type metabolism of glucose utilization.…”

PRESTO, a new tool for integrating large-scale -omics data and discovering disease-specific signatures

“…As a proof-of-concept that PRESTO is applicable to other types of -omics data, we analyzed published data of label-free mass spectrometry from clear cell renal cell carcinoma (ccRCC) biopsies taken from 84 patients with stage 1-4 tumors or adjacent tumor-free tissue (Supplemental Table 1; 2 conditions x 84 patients x 783 proteins) 41 . Four distinct protein gates were identified (Supplemental Fig.…”