Metabolomics is increasingly being used in
cancer biology for
biomarker discovery and identification of potential novel therapeutic targets. However, a systematic metabolomics study of multiple biofluids to determine their interrelationships and to describe their use as
tumor proxies is lacking. Using a mouse xenograft model of
kidney cancer, characterized by subcapsular implantation of Caki-1 clear cell human
kidney cancer cells, we examined tissue, serum, and urine all obtained simultaneously at baseline (urine) and at, or close to, animal sacrifice (urine, tissue, and plasma). Uniform metabolomics analysis of all three "matrices" was accomplished using gas chromatography- and liquid chromatography-mass spectrometry. Of all the metabolites identified (267 in tissue, 246 in serum, and 267 in urine), 89 were detected in all 3 matrices, and the majority was altered in the same direction. Heat maps of individual metabolites showed that alterations in serum were more closely related to tissue than was urine. Two metabolites,
cinnamoylglycine and
nicotinamide, were concordantly and significantly (when corrected for multiple testing) altered in tissue and serum, and
cysteine-glutathione disulfide showed the highest change (232.4-fold in tissue) of any metabolite. On the basis of these and other considerations, three pathways were chosen for
biologic validation of the metabolomic data, resulting in potential therapeutic target identification. These data show that serum metabolomics analysis is a more accurate proxy for tissue changes than urine and that
tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in
renal cell carcinoma, and support the concept that
PPAR-α antagonism may be a potential therapeutic approach for this disease.