Kidney cancer is the seventh most common cancer in the Western world, its incidence is increasing, and it is frequently metastatic at presentation, at which stage patient survival statistics are grim. In addition, there are no useful biofluid markers for this disease, such that diagnosis is dependent on imaging techniques that are not generally used for screening. In the present study, we use metabolomics techniques to identify metabolites in kidney cancer patients' urine, which appear at different levels (when normalized to account for urine volume and concentration) from the same metabolites in nonkidney cancer patients. We found that quinolinate, 4-hydroxybenzoate, and gentisate are differentially expressed at a false discovery rate of 0.26, and these metabolites are involved in common pathways of specific amino acid and energetic metabolism, consistent with high tumor protein breakdown and utilization, and the Warburg effect. When added to four different (three kidney cancer-derived and one ''normal'') cell lines, several of the significantly altered metabolites, quinolinate, aketoglutarate, and gentisate, showed increased or unchanged cell proliferation that was cell line-dependent. Further evaluation of the global metabolomics analysis, as well as confirmation of the specific potential biomarkers using a larger sample size, will lead to new avenues of kidney cancer diagnosis and therapy.

Kidney cancer often diagnosed at late stages when treatment options are severely limited. Thus, greater understanding of tumor metabolism leading ultimately to novel approaches to diagnosis are needed. Our laboratory has been utilizing metabolomics to evaluate compounds appearing in kidney cancer patients’ biofluids at concentrations different from control patients. Here, we collected urine samples from kidney cancer patients and analyzed them by chromatography coupled to mass spectrometry. Once normalized to control for urinary concentration, samples were analyzed by two independent laboratories. After technical validation, we now show differential urinary concentrations of several acylcarnitines as a function of both cancer status and kidney cancer grade, with most acylcarnitines being increased in the urine of cancer patients and in those patients with high cancer grades. This finding was validated in a mouse xenograft model of human kidney cancer. Biological validation shows carbon chain length-dependent effects of the acylcarnitines on cytotoxicity in vitro, and higher chain length acylcarnitines demonstrated inhibitory effects on NF-κB activation, suggesting an immune modulatory effect of these compounds. Thus, acylcarnitines in the kidney cancer urine may reflect alterations in metabolism, cell component synthesis, and/or immune surveillance, and may help explain the profound chemotherapy resistance seen with this cancer. This study shows for the first time the value of a novel class of metabolites which may lead to new therapeutic approaches for cancer and may prove useful in cancer biomarker studies. Furthermore, these findings open up a new area of investigation into the metabolic basis of kidney cancer.

Metabolomics is increasingly being utilized 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 utility as tumor proxies is lacking. Using a mouse xenograft model of kidney cancer, characterized by sub-capsular 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 (tissue and plasma). Uniform metabolomics analysis of all three “matrices” was accomplished using GC- and LC-MS. Of all the metabolites identified (267 in tissue, 246 in serum, 267 in urine), 89 were detected in all 3 matrices, and the majority were 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. Based on these and other considerations, three pathways were chosen for biological 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, that tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in renal cell carcinoma, and support the concept that PPAR-alpha antagonism may be a potential therapeutic approach for this disease.