Analysis and interpretation of transcriptomic data obtained from extended Warburg effect genes in patients with clear cell renal cell carcinoma.

BACKGROUND

Many cancers adopt a metabolism that is characterized by the well-known Warburg effect (aerobic glycolysis). Recently, numerous attempts have been made to treat cancer by targeting one or more gene products involved in this pathway without notable success. This work outlines a transcriptomic approach to identify genes that are highly perturbed in clear cell renal cell carcinoma (CCRCC).

METHODS

We developed a model of the extended Warburg effect and outlined the model using Cytoscape. Following this, gene expression fold changes (FCs) for tumor and adjacent normal tissue from patients with CCRCC (GSE6344) were mapped on to the network. Gene expression values with FCs of greater than two were considered as potential targets for treatment of CCRCC.

RESULTS

The Cytoscape network includes glycolysis, gluconeogenesis, the pentose phosphate pathway (PPP), the TCA cycle, the serine/glycine pathway, and partial glutaminolysis and fatty acid synthesis pathways. Gene expression FCs for nine of the 10 CCRCC patients in the GSE6344 data set were consistent with a shift to aerobic glycolysis. Genes involved in glycolysis and the synthesis and transport of lactate were over-expressed, as was the gene that codes for the kinase that inhibits the conversion of pyruvate to acetyl-CoA. Interestingly, genes that code for unique proteins involved in gluconeogenesis were strongly under-expressed as was also the case for the serine/glycine pathway. These latter two results suggest that the role attributed to the M2 isoform of pyruvate kinase (PKM2), frequently the principal isoform of PK present in cancer: i.e. causing a buildup of glucose metabolites that are shunted into branch pathways for synthesis of key biomolecules, may not be operative in CCRCC. The fact that there was no increase in the expression FC of any gene in the PPP is consistent with this hypothesis. Literature protein data generally support the transcriptomic findings.

CONCLUSIONS

A number of key genes have been identified that could serve as valid targets for anti-cancer pharmaceutical agents. Genes that are highly over-expressed include ENO2, HK2, PFKP, SLC2A3, PDK1, and SLC16A1. Genes that are highly under-expressed include ALDOB, PKLR, PFKFB2, G6PC, PCK1, FBP1, PC, and SUCLG1.