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Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O - Metabolites (2015)

Bottom Line: Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05).Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased.Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

View Article: PubMed Central - PubMed

Affiliation: Division of Hematology/Oncology, UC Davis Cancer Center, 4501 X Street, Room 3016, Sacramento, CA 95817, USA. smiyamoto@ucdavis.edu.

ABSTRACT
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

No MeSH data available.


Related in: MedlinePlus

MetaMapp mapping of metabolomic analysis of lung cancer blood samples: a MetaMapp clustering metabolites based on biochemical reactant pairs in the KEGG RPAIR Database in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information.
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metabolites-05-00192-f004: MetaMapp mapping of metabolomic analysis of lung cancer blood samples: a MetaMapp clustering metabolites based on biochemical reactant pairs in the KEGG RPAIR Database in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information.

Mentions: While diagnosis of lung cancer phenotypes is clinically important, a differential analysis of plasma metabolic changes between lung cancer patients and matched controls should also reflect known mechanisms in cancer biology or lead to new hypotheses. We have therefore used less stringent thresholds (p < 0.1) to increase the number of metabolites that are potentially metabolically connected and visualized all metabolites at p < 0.1 using a combined biochemical and chemical network graph, MetaMapp (Figure 4). This graph clusters metabolites based on biochemical reactant pairs in the KEGG RPAIR Database [38,39] in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information [40]. This type of analysis gives us a better perspective on how metabolic changes detected in blood samples might be due to the overall systemic effect of tumour growth and helps us to identify the potential biochemical links between the metabolic changes in blood from lung cancer (Figure 4). The use of MetaMAPP graphs enable understanding which metabolic modules are more affected by a disease or a treatment than others [40]. For example, few hydroxyl acids were differentially regulated, which means that trichloroacetic (TCA) cycle metabolites in blood plasma did not reflect the disease status, whereas plasma lactic acid was significantly increased in ACD NSLC patients. A range of both proteinogenic (tryptophan, lysine, histidine, valine) and non-proteinogenic amino acids (N-methylalanine, trans-4-hydroxyproline, cis-4-hydroxyproline) were found at lower levels in cancer patients, reflecting the increased use of carbon skeletons of amino acids in tumor cells. Reduced levels of amino acids have also been observed in cancer cachexia showing greater protein turnover and metabolism in advanced stage disease [41].


Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O - Metabolites (2015)

MetaMapp mapping of metabolomic analysis of lung cancer blood samples: a MetaMapp clustering metabolites based on biochemical reactant pairs in the KEGG RPAIR Database in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495369&req=5

metabolites-05-00192-f004: MetaMapp mapping of metabolomic analysis of lung cancer blood samples: a MetaMapp clustering metabolites based on biochemical reactant pairs in the KEGG RPAIR Database in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information.
Mentions: While diagnosis of lung cancer phenotypes is clinically important, a differential analysis of plasma metabolic changes between lung cancer patients and matched controls should also reflect known mechanisms in cancer biology or lead to new hypotheses. We have therefore used less stringent thresholds (p < 0.1) to increase the number of metabolites that are potentially metabolically connected and visualized all metabolites at p < 0.1 using a combined biochemical and chemical network graph, MetaMapp (Figure 4). This graph clusters metabolites based on biochemical reactant pairs in the KEGG RPAIR Database [38,39] in addition to Tanimoto chemical structure similarity for identified metabolites that lack enzymatic information [40]. This type of analysis gives us a better perspective on how metabolic changes detected in blood samples might be due to the overall systemic effect of tumour growth and helps us to identify the potential biochemical links between the metabolic changes in blood from lung cancer (Figure 4). The use of MetaMAPP graphs enable understanding which metabolic modules are more affected by a disease or a treatment than others [40]. For example, few hydroxyl acids were differentially regulated, which means that trichloroacetic (TCA) cycle metabolites in blood plasma did not reflect the disease status, whereas plasma lactic acid was significantly increased in ACD NSLC patients. A range of both proteinogenic (tryptophan, lysine, histidine, valine) and non-proteinogenic amino acids (N-methylalanine, trans-4-hydroxyproline, cis-4-hydroxyproline) were found at lower levels in cancer patients, reflecting the increased use of carbon skeletons of amino acids in tumor cells. Reduced levels of amino acids have also been observed in cancer cachexia showing greater protein turnover and metabolism in advanced stage disease [41].

Bottom Line: Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05).Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased.Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

View Article: PubMed Central - PubMed

Affiliation: Division of Hematology/Oncology, UC Davis Cancer Center, 4501 X Street, Room 3016, Sacramento, CA 95817, USA. smiyamoto@ucdavis.edu.

ABSTRACT
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

No MeSH data available.


Related in: MedlinePlus