Showing information for HMDB0000148 ('glutamate', 'L-glutamic acid', 'glutamic acid', 'L-glutamate')

Metabolite information
HMDB ID HMDB0000148
Synonyms
(2S)-2-Aminopentanedioate
(2S)-2-Aminopentanedioic acid
(S)-(+)-Glutamate
(S)-(+)-Glutamic acid
(S)-2-Aminopentanedioate
(S)-2-Aminopentanedioic acid
(S)-Glutamate
(S)-Glutamic acid
1-Aminopropane-1,3-dicarboxylate
1-Aminopropane-1,3-dicarboxylic acid
1-amino-Propane-1,3-dicarboxylate
1-amino-Propane-1,3-dicarboxylic acid
2-Aminoglutarate
2-Aminoglutaric acid
2-Aminopentanedioate
2-Aminopentanedioic acid
Acide glutamique
Acidum glutamicum
Aciglut
Aluminum L glutamate
Aluminum L-glutamate
Aminoglutarate
Aminoglutaric acid
D Glutamate
D-Glutamate
E
GLUTAMIC ACID
Glt
Glu
Glusate
Glut
Glutacid
Glutamate
Glutamate, potassium
Glutamic acid, (D)-isomer
Glutamicol
Glutamidex
Glutaminate
Glutaminic acid
Glutaminol
Glutaton
L Glutamate
L Glutamic acid
L-(+)-Glutamate
L-(+)-Glutamic acid
L-Glu
L-Glutamate
L-Glutamate, aluminum
L-Glutaminate
L-Glutaminic acid
L-Glutaminsaeure
L-a-Aminoglutarate
L-a-Aminoglutaric acid
L-alpha-Aminoglutarate
L-alpha-Aminoglutaric acid
Potassium glutamate
a-Aminoglutarate
a-Aminoglutaric acid
a-Glutamate
a-Glutamic acid
acido Glutamico
alpha-Aminoglutarate
alpha-Aminoglutaric acid
alpha-Glutamate
alpha-Glutamic acid
Chemical formula C5H9NO4
IUPAC name
(2S)-2-aminopentanedioic acid
CAS registry number 56-86-0
Monoisotopic molecular weight 147.053157781

Chemical taxonomy
Super class Organic acids and derivatives
Class Carboxylic acids and derivatives
Sub class Amino acids, peptides, and analogues

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000148 as a lung cancer biomarker




The studies that identify HMDB0000148 as a lung cancer-related metabolite

Reference Country Specimen Marker function Participants (Case) Participants (Control)
Cancer type Stage Number Gender (M,F) Age mean (range) (M/F) Smoking status Type Number Gender (M,F) Age mean (range) (M/F) Smoking status
Miyamoto et al. 2015 US blood diagnosis NSCLC, SCLC, mesothelioma, secondary metastasis to lung I, II, III, IV 11 4, 7 67 (61-73) / 67 (47-76) smoker, non-smoker healthy 11 5, 6 69 (61-83) / 54 (44-61) unknown
Miyamoto et al. 2015 US blood diagnosis adenocarcinoma unknown (mostly late stage) 18 10, 8 67 (50-85) / 62 (53-72) former, current healthy 20 8, 12 64 (49-80) / 66 (58-82) former, current
Mazzone et al. 2016 US serum – adenocarcinoma, squamous cell carcinoma I, II, III 94 55.3%, 44.7% 68.7 – at-risk controls 190 50.5%, 49.5% 66.2 –
Ro?-Mazurczyk et al. 2017 Poland serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 31 17, 14 52-72 – healthy 92 52, 40 52-73 –
Fahrmann et al. 2015 US serum diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Yue et al. 2018 China plasma diagnosis SCLC – 20 – – – healthy 20 – – –
Fahrmann et al. 2015 US plasma diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Fahrmann et al. 2015 US plasma diagnosis adenocarcinoma I, II, III, IV 52 17, 35 65.9 ± 9.66 – healthy 31 11, 20 64.1 ± 8.97 –
Ni et al. 2016 China serum diagnosis lung cancer – 40 14, 26 67 – healthy 100 – – –
Klupczynska et al. 2016a Poland serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 90 58, 32 64 (48-86) current, non-smoker, unknown healthy 63 41, 22 62 (43-78) smoker, non-smoker, unknown
Fahrmann et al. 2015 US serum diagnosis adenocarcinoma I, II, III, IV 49 17, 32 65.9 ± 9.87 – healthy 31 11, 20 64.1 ± 8.97 –
Hori et al. 2011 Japan tissue diagnosis adenocarcinoma, squamous cell carcinoma, SCLC – 7 6, 1 median: 61 (53-82) smoker, non-smoker tumor vs. adjacent normal tissue 7 6, 1 median: 61 (53-82) smoker, non-smoker
Hori et al. 2011 Japan serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II 11 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Hori et al. 2011 Japan serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II, III, IV 33 26, 7 median: 65 (55-81) smoker, non-smoker, unknown healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Hori et al. 2011 Japan serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC III, IV 22 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Wikoff et al. 2015b US tissue diagnosis adenocarcinoma I 39 15, 24 72.33 ± 8.78 smoker, non-smoker tumor vs. adjacent normal tissue 39 15, 24 72.33 ± 8.78 smoker, non-smoker
Callejon-Leblic et al. 2016 Spain bronchoalveolar lavage fluid diagnosis lung cancer – 24 16, 8 66 ± 11 – noncancerous lung diseases 31 23, 8 56 ± 13 –
Moreno et al. 2018 Spain tissue therapy, diagnosis squamous cell carcinoma I, II, III 35 35, 0 68.71 ± 7.46 – tumor vs. adjacent normal tissue 35 35, 0 68.71 ± 7.46 –
Moreno et al. 2018 Spain tissue therapy, diagnosis adenocarcinoma I, II, III 33 24, 9 62.11 ± 9.73 – tumor vs. adjacent normal tissue 33 24, 9 62.11 ± 9.73 –
Sappington et al. 2018 US lymph node aspirates diagnosis adenocarcinoma, squamous cell carcinoma II, III, IV 50 31, 19 – – non-malignant 29 15, 14 – –
Sappington et al. 2018 US lymph node aspirates diagnosis adenocarcinoma II, III, IV 31 17, 14 – – squamous cell carcinoma 19 14, 5 – –
Callej?n-Leblic et al. 2019 Spain blood diagnosis NSCLC, SCLC II, III, IV 30 25, 5 67 ± 12 former, current, non-smoker healthy 30 14, 16 56 ± 14 former, non-smoker
Mu et al. 2019 China serum diagnosis NSCLC I, II, III, IV 30 0, 30 60.4 ± 9.7 non-smoker healthy 30 0, 30 54.7 ± 14.3 non-smoker
Yang et al. 2020 China pleural effusion diagnosis adenocarcinoma – 46 15, 31 63 ± 12 – pulmonary tuberculosis, other pulmonary diseases 32 26, 6 49 ± 19 –
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – healthy control 20 13, 7 61.5 ± 12.06 –
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Miyamoto et al. 2015 GC EI – TOF MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Fahrmann et al. 2015 GC EI – TOF –
Yue et al. 2018 LC ESI both QTRAP MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Ni et al. 2016 LC ESI positive Triple quadrupole MS/MS
Klupczynska et al. 2016a LC – – QTRAP MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Wikoff et al. 2015b GC EI – TOF –
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF MS/MS
Moreno et al. 2018 LC, GC ESI, EI both LC: linear ion-trap, GC: single-quadrupole LC: MS/MS
Moreno et al. 2018 LC, GC ESI, EI both LC: linear ion-trap, GC: single-quadrupole LC: MS/MS
Sappington et al. 2018 LC ESI positive Q-TOF MS/MS
Sappington et al. 2018 LC ESI positive Q-TOF MS/MS
Callej?n-Leblic et al. 2019 DI ESI positive Q-TOF MS/MS
Mu et al. 2019 GC – – – –
Yang et al. 2020 LC ESI positive Q-Orbitrap MS/MS
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Reference Data processing software Database search
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Yue et al. 2018 Analyst, MultiQuant –
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Ni et al. 2016 – –
Klupczynska et al. 2016a – –
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Wikoff et al. 2015b BinBase NIST11, BinBase
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Sappington et al. 2018 MassHunter, MassProfilerProfessional HMDB
Sappington et al. 2018 MassHunter, MassProfilerProfessional HMDB
Callej?n-Leblic et al. 2019 – HMDB, Metlin
Mu et al. 2019 – –
Yang et al. 2020 XCMS HMDB, METLIN, LipidSearch
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 31486.3636363636 34887.1818181818 0.90 0.91 – –
Miyamoto et al. 2015 Analysis of Covariance 39179.2222222222 27793.75 1.41 0.03 – –
Mazzone et al. 2016 two- sample independent t test 1.125203± 0.4834663 1.039322± 0.4192745 1.08 0.12 0.22 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 1.5425 ± 1.0398 1.5055 ± 1.5135 1.02 0.40 0.68 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 11081 ± 4803 5709 ± 2286 1.94 0.00e+00 1.00e-03 –
Yue et al. 2018 OPLS-DA, student’s t-test 1532.78±321.05 ng/mL 1092.18±192.11 ng/mL 3.07 5.05e-06 – 1.58
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 4070 ± 1522 3213 ± 1694 1.27 3.00e-03 0.04 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 7102 ± 3398 5700 ± 1876 1.25 0.02 0.19 –
Ni et al. 2016 one-way ANOVA 76.50 ± 21.56 μmol/L 87.55 ± 27.25 μmol/L – 0.02 – –
Klupczynska et al. 2016a t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA 80.81±36.92 ?M 72.48±40.75 ?M 1.11 0.08 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 9148 ± 4508 6083 ± 1891 1.50 0.00e+00 0.04 –
Hori et al. 2011 student’s t-test, PLS-DA – – 2.31 3.00e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.83 0.37 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.72 0.03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.67 0.03 – –
Wikoff et al. 2015b OPLS-DA – – 1.40 – 2.00e-03 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.84 0.02 – 1.58
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.46 2.96e-10 1.62e-09 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.24 1.96e-04 6.14e-04 –
Sappington et al. 2018 OPLS-DA, PLS-DA – – 1.44 0.04 – –
Sappington et al. 2018 OPLS-DA, PLS-DA – – 1.10 0.74 – –
Callej?n-Leblic et al. 2019 PCA, PLS-DA, one-way ANOVA – – 1.35 2.00e-04 – 1.35
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.38 1.00e-03 1.00e-03 1.72
Yang et al. 2020 PLS-DA – – 1.84 – 1.20e-04 1.69
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 6.95e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 0.03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 5.85e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 1.28e-04 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 1.95e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 0.02 – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Mazzone et al. 2016 – – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Fahrmann et al. 2015 random forest – – – – –
Yue et al. 2018 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Ni et al. 2016 – – – – – –
Klupczynska et al. 2016a ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis – 0.584 – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Wikoff et al. 2015b – – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.51 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Sappington et al. 2018 – – – – – –
Sappington et al. 2018 – – – – – –
Callej?n-Leblic et al. 2019 ROC curve – 0.71 – – –
Mu et al. 2019 – – – – – –
Yang et al. 2020 ROC analysis – 0.839 – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –