Metabolic profiling reveals key metabolic features of renal cell carcinoma
Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were a...
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| Veröffentlicht in: | Journal of cellular and molecular medicine 2011-01, Vol.15 (1), p.109-118 |
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| creator | Catchpole, Gareth Platzer, Alexander Weikert, Cornelia Kempkensteffen, Carsten Johannsen, Manfred Krause, Hans Jung, Klaus Miller, Kurt Willmitzer, Lothar Selbig, Joachim Weikert, Steffen |
| description | Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α‐tocopherol, hippuric acid, myoinositol, fructose‐1‐phosphate and glucose‐1‐phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC. |
| doi_str_mv | 10.1111/j.1582-4934.2009.00939.x |
| format | Article |
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Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α‐tocopherol, hippuric acid, myoinositol, fructose‐1‐phosphate and glucose‐1‐phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.</description><identifier>ISSN: 1582-1838</identifier><identifier>EISSN: 1582-4934</identifier><identifier>DOI: 10.1111/j.1582-4934.2009.00939.x</identifier><identifier>PMID: 19845817</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Adult ; Aged ; Amino acids ; Arachidonic acid ; Biomarkers ; Biomarkers, Tumor - metabolism ; Carcinoma, Renal Cell - metabolism ; Carcinoma, Renal Cell - pathology ; Classification ; Decision trees ; Fatty acids ; Female ; Females ; Glucose metabolism ; Glucose-1-phosphate ; Humans ; Inositol phosphate ; Kidney cancer ; Kidney Neoplasms - metabolism ; Kidney Neoplasms - pathology ; Male ; Mass spectroscopy ; Metabolic pathways ; Metabolism ; Metabolites ; Metabolome ; Metabolomics ; metastasis ; Middle Aged ; Phenotypes ; Prognosis ; Renal cell carcinoma ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ; Tricarboxylic acid cycle ; Tumor Cells, Cultured ; Tumors ; Uracil ; Vitamin E</subject><ispartof>Journal of cellular and molecular medicine, 2011-01, Vol.15 (1), p.109-118</ispartof><rights>2011 The Author Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing Ltd</rights><rights>2011 The Author Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing Ltd.</rights><rights>2011. 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Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α‐tocopherol, hippuric acid, myoinositol, fructose‐1‐phosphate and glucose‐1‐phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.</description><subject>Adult</subject><subject>Aged</subject><subject>Amino acids</subject><subject>Arachidonic acid</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Carcinoma, Renal Cell - metabolism</subject><subject>Carcinoma, Renal Cell - pathology</subject><subject>Classification</subject><subject>Decision trees</subject><subject>Fatty acids</subject><subject>Female</subject><subject>Females</subject><subject>Glucose metabolism</subject><subject>Glucose-1-phosphate</subject><subject>Humans</subject><subject>Inositol phosphate</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - metabolism</subject><subject>Kidney Neoplasms - pathology</subject><subject>Male</subject><subject>Mass spectroscopy</subject><subject>Metabolic pathways</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>metastasis</subject><subject>Middle Aged</subject><subject>Phenotypes</subject><subject>Prognosis</subject><subject>Renal cell carcinoma</subject><subject>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization</subject><subject>Tricarboxylic acid cycle</subject><subject>Tumor Cells, Cultured</subject><subject>Tumors</subject><subject>Uracil</subject><subject>Vitamin E</subject><issn>1582-1838</issn><issn>1582-4934</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNUctO4zAUtdAgYIBfQJFYzKrBj8SxFyChCmYGUbFhb92aG3BJ4mI3QP8eh1blscKS7Sudh-7RISRjNGfpnMxyVio-KrQock6pztMVOn_dInsb4Nd6ZkqoXfI7xhmlQjKhd8gu06ooFav2yNUEFzD1jbPZPPjaNa67zwI-IzQxe8Rl1m7wGmHRB4yZrxOjgyaz2KQHgnWdb-GAbNdJhYfrf5_cXl7cjv-Nrm_-_h-fX49sSZkeFRUgV1NAKxiA5NWd5oJWglIJqGWJFJBSRrXUyKnkltfSWmAoweopFfvkbGU776ct3lnsFgEaMw-uhbA0Hpz5inTuwdz7ZyMU54VWyeDP2iD4px7jwrQuDlGgQ99Ho4qqEFxJnpjH35gz34eUPJq0calLpfngp1YsG3yMAevNLoyaoS4zM0MTZmjFDHWZ97rMa5Iefc7yIVz3kwinK8KLa3D5Y2NzNZ5M0iTeABhspIA</recordid><startdate>201101</startdate><enddate>201101</enddate><creator>Catchpole, Gareth</creator><creator>Platzer, Alexander</creator><creator>Weikert, Cornelia</creator><creator>Kempkensteffen, Carsten</creator><creator>Johannsen, Manfred</creator><creator>Krause, Hans</creator><creator>Jung, Klaus</creator><creator>Miller, Kurt</creator><creator>Willmitzer, Lothar</creator><creator>Selbig, Joachim</creator><creator>Weikert, Steffen</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201101</creationdate><title>Metabolic profiling reveals key metabolic features of renal cell carcinoma</title><author>Catchpole, Gareth ; Platzer, Alexander ; Weikert, Cornelia ; Kempkensteffen, Carsten ; Johannsen, Manfred ; Krause, Hans ; Jung, Klaus ; Miller, Kurt ; Willmitzer, Lothar ; Selbig, Joachim ; Weikert, Steffen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5019-47ae28baec31aa627d923073006ae965e0ae0010969e2062c2f6cca1e6ac9b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Amino acids</topic><topic>Arachidonic acid</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - 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Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α‐tocopherol, hippuric acid, myoinositol, fructose‐1‐phosphate and glucose‐1‐phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>19845817</pmid><doi>10.1111/j.1582-4934.2009.00939.x</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aged Amino acids Arachidonic acid Biomarkers Biomarkers, Tumor - metabolism Carcinoma, Renal Cell - metabolism Carcinoma, Renal Cell - pathology Classification Decision trees Fatty acids Female Females Glucose metabolism Glucose-1-phosphate Humans Inositol phosphate Kidney cancer Kidney Neoplasms - metabolism Kidney Neoplasms - pathology Male Mass spectroscopy Metabolic pathways Metabolism Metabolites Metabolome Metabolomics metastasis Middle Aged Phenotypes Prognosis Renal cell carcinoma Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Tricarboxylic acid cycle Tumor Cells, Cultured Tumors Uracil Vitamin E |
| title | Metabolic profiling reveals key metabolic features of renal cell carcinoma |
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