Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jul 15;72(14):3471-9.
doi: 10.1158/0008-5472.CAN-11-3105. Epub 2012 May 24.

Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

Sheila Ganti  1 , Sandra L Taylor, Omran Abu Aboud, Joy Yang, Christopher Evans, Michael V Osier, Danny C Alexander, Kyoungmi Kim, Robert H Weiss
Affiliations

Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

Sheila Ganti et al. Cancer Res. .

Abstract

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.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Venn diagram of all identified metabolites in each matrix Tissue and the indicated biofluids were analyzed by GC- and LC-MS and those metabolites identified in each of the 3 matrices are shown.
Fig. 2
Fig. 2
Variation in metabolites in each matrix Distribution of fold change (log2) of metabolites between xenograft and sham surgery mice in the three matricesDotted line shows a log2 fold change of 1 indicating no difference between xenograft and sham surgery.
Fig. 3
Fig. 3
Heatmaps for each matrix of the 89 metabolites in common. The color of each section is proportional to the significance of change of metabolites (red=upregulated; blue=downregulated) in (a) tissue, (b) serum, and (c) urineTissue and serum values are z-scores of raw intensities of each metaboliteFor urine, values are z-scores of creatinine-normalized metabolite intensities in urine obtained 2 days before sacrifice in both cancer and corresponding control animals.
Fig. 4
Fig. 4
Comparison of metabolites across matrices The differences in identified metabolites shown as cancer:control are indicated for each matrix (red=upregulated; blue=downregulated; solid=significant [p≤0.05]; unfilled=not significant [p > 0.05]).

  1. using raw p-values

  2. using FDRs

Fig. 5
Fig. 5
PPAR-α modulation affects RCC and other renal epithelial cell lines The indicated cell lines (NHK = primary human renal tubular epithelial cells) were grown to confluence and incubated continuously with the indicated concentrations (10-100 μM) of the PPAR-α agonist Wy-14,643 or antagonist GW6471An MTT assay was performed as described in Experimental ProceduresThe experiment shown is representative of 3 experimentsAsterisks indicate significance at a p-value<0.05 when compared to the DMSO treated control for that cell line.
Fig. 6
Fig. 6
1-methyltryptophan increases an inflammatory marker in cancer cells The RCC cell lines A498 and ACHN were grown to confluence and treated for 72 hours with 1-methyltryptophan as indicatedWhole cell protein extracts were then run using 10% SDS-PAGE and incubated with the indicated antibodiesActin is a loading control and LPS is a positive controlThe experiments shown are representative of 3 independent experimentsDensitometry measurements are shown for this blot: incubation of both cell lines with 1-MT at 200 μM was significant (p=0.015 for A498 and p=0.034 for ACHN).
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Kim K, Taylor SL, Ganti S, Guo L, Osier MV, Weiss RH. Urine Metabolomic Analysis Identifies Potential Biomarkers and Pathogenic Pathways in Kidney Cancer. OMICS. 2011;15:293–303. - PMC - PubMed
    1. Kim K, Aronov P, Zakharkin SO, Anderson D, Perroud B, Thompson IM, et al. Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Mol Cell Proteomics. 2009;8:558–70. - PMC - PubMed
    1. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457:910–4. - PMC - PubMed
    1. Bathe OF, Shaykhutdinov R, Kopciuk K, Weljie AM, McKay A, Sutherland FR, et al. Feasibility of identifying pancreatic cancer based on serum metabolomics. Cancer Epidemiol Biomarkers Prev. 2011;20:140–7. - PubMed
    1. Weiss RH, Kim K. Metabolomics in the study of kidney diseases. Nat Rev Nephrology. 2011;8:22–33. - PubMed

Publication types