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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

Box plots of top unknown compounds with electron ionization mass spectra comparing the two studies. Box-whisker plots (top panels) of the top unknown candidates from each study (Study 1 and Study 2) with the electron ionization MS spectra (lower panels) of the compound to show the mass fragmentation of the compounds to help with the identification of the compound.
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metabolites-05-00192-f003: Box plots of top unknown compounds with electron ionization mass spectra comparing the two studies. Box-whisker plots (top panels) of the top unknown candidates from each study (Study 1 and Study 2) with the electron ionization MS spectra (lower panels) of the compound to show the mass fragmentation of the compounds to help with the identification of the compound.

Mentions: Some of the metabolites that we found to be differentially expressed were unknown compounds (Tables S1 and S2). We have intentionally used an untargeted metabolomic screen to detect novel metabolites that might be involved in the pathogenesis of NSLC and thereby detected “unknown” compounds in our metabolomic analysis. Most of these unknown compounds have been previously observed in other samples from different species, mammalian (human, mouse, rat), plant, bacterial and others which have been carefully tabulated in the BinBase database [25]. Searching our BinBase database and comparing the MS spectra of the unknowns with known compounds with similar electron ionization fragment spectra and similar retention times can help identify several interesting compounds that the unknown may be linked or is related to, based on the similarity of the spectra to known spectral fragmentation (Figure 3). For example, based on its fragmentation scan, the BinBase unknown compound #200595 has evidence of being an amino-compound, #200595 shows substructure patterns of carbohydrates and a retention index close to glucoheptose, #220177 can also be matched to carbohydrate substructures, in addition to showing a characteristic fragment m/z 144 typical for amines, and #223597 is a very high boiling compound with fragments found in sterols. Once additional cohort studies validate the importance of such unidentified metabolites, accurate mass GC-QTOF MS data can be acquired to obtain elemental formulas and matching structures from database queries [26,27].


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)

Box plots of top unknown compounds with electron ionization mass spectra comparing the two studies. Box-whisker plots (top panels) of the top unknown candidates from each study (Study 1 and Study 2) with the electron ionization MS spectra (lower panels) of the compound to show the mass fragmentation of the compounds to help with the identification of the compound.
© Copyright Policy
Related In: Results  -  Collection

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

metabolites-05-00192-f003: Box plots of top unknown compounds with electron ionization mass spectra comparing the two studies. Box-whisker plots (top panels) of the top unknown candidates from each study (Study 1 and Study 2) with the electron ionization MS spectra (lower panels) of the compound to show the mass fragmentation of the compounds to help with the identification of the compound.
Mentions: Some of the metabolites that we found to be differentially expressed were unknown compounds (Tables S1 and S2). We have intentionally used an untargeted metabolomic screen to detect novel metabolites that might be involved in the pathogenesis of NSLC and thereby detected “unknown” compounds in our metabolomic analysis. Most of these unknown compounds have been previously observed in other samples from different species, mammalian (human, mouse, rat), plant, bacterial and others which have been carefully tabulated in the BinBase database [25]. Searching our BinBase database and comparing the MS spectra of the unknowns with known compounds with similar electron ionization fragment spectra and similar retention times can help identify several interesting compounds that the unknown may be linked or is related to, based on the similarity of the spectra to known spectral fragmentation (Figure 3). For example, based on its fragmentation scan, the BinBase unknown compound #200595 has evidence of being an amino-compound, #200595 shows substructure patterns of carbohydrates and a retention index close to glucoheptose, #220177 can also be matched to carbohydrate substructures, in addition to showing a characteristic fragment m/z 144 typical for amines, and #223597 is a very high boiling compound with fragments found in sterols. Once additional cohort studies validate the importance of such unidentified metabolites, accurate mass GC-QTOF MS data can be acquired to obtain elemental formulas and matching structures from database queries [26,27].

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