Showing information for HMDB0000267 ('oxoproline', '5-oxoproline', 'pyroglutamic acid', '5-oxo-l-proline', 'pyroglutamate', 'L-pyroglutamic acid', 'pyroglutamic xa0acidxa0')

Metabolite information
HMDB ID HMDB0000267
Synonyms
(-)-2-Pyrrolidone-5-carboxylate
(-)-2-Pyrrolidone-5-carboxylic acid
(-)-Pyroglutamate
(-)-Pyroglutamic acid
(5S)-2-Oxopyrrolidine-5-carboxylate
(5S)-2-Oxopyrrolidine-5-carboxylic acid
(S)-(-)-2-Pyrrolidone-5-carboxylate
(S)-(-)-2-Pyrrolidone-5-carboxylic acid
(S)-(-)-g-Butyrolactam-g-carboxylate
(S)-(-)-g-Butyrolactam-g-carboxylic acid
(S)-(-)-gamma-Butyrolactam-gamma-carboxylate
(S)-(-)-gamma-Butyrolactam-gamma-carboxylic acid
(S)-2-Pyrrolidone-5-carboxylate
(S)-2-Pyrrolidone-5-carboxylic acid
(S)-5-oxo-2-Pyrrolidinecarboxylate
(S)-5-oxo-2-Pyrrolidinecarboxylic acid
(S)-Pyroglutamate
(S)-Pyroglutamic acid
2-L-Pyrrolidone-5-carboxylate
2-L-Pyrrolidone-5-carboxylic acid
2-Oxopyrrolidine-5(S)-carboxylate
2-Oxopyrrolidine-5(S)-carboxylic acid
2-Pyrrolidinone-5-carboxylate
2-Pyrrolidinone-5-carboxylic acid
5-Carboxy-2-pyrrolidinone
5-Ketoproline
5-L-Oxoproline
5-Oxoproline
5-Oxopyrrolidine-2-carboxylic acid
5-Pyrrolidinone-2-carboxylate
5-Pyrrolidinone-2-carboxylic acid
5-Pyrrolidone-2-carboxylate
5-Pyrrolidone-2-carboxylic acid
5-oxo-L-Proline
Ajidew a 100
Glutimate
Glutimic acid
Glutiminate
Glutiminic acid
L-2-Pyrrolidone-5-carboxylate
L-2-Pyrrolidone-5-carboxylic acid
L-5-Carboxy-2-pyrrolidinone
L-5-Oxoproline
L-5-Pyrrolidone-2-carboxylate
L-5-Pyrrolidone-2-carboxylic acid
L-5-oxo-2-Pyrrolidinecarboxylate
L-5-oxo-2-Pyrrolidinecarboxylic acid
L-Glutamic acid g-lactam
L-Glutimate
L-Glutimic acid
L-Glutiminate
L-Glutiminic acid
L-Pyroglutamate
L-Pyroglutamic acid
L-Pyrrolidinonecarboxylate
L-Pyrrolidinonecarboxylic acid
L-Pyrrolidonecarboxylate
L-Pyrrolidonecarboxylic acid
Magnesium pidolate
Oxoproline
Oxopyrrolidinecarboxylate
Oxopyrrolidinecarboxylic acid
Pidolate
Pidolate, magnesium
Pidolic acid
Pidolidone
Pyroglutamate
Pyrrolidinonecarboxylate
Pyrrolidinonecarboxylic acid
Pyrrolidone-5-carboxylate
Pyrrolidone-5-carboxylic acid
Pyrrolidonecarboxylic acid
Chemical formula C5H7NO3
IUPAC name
(2S)-5-oxopyrrolidine-2-carboxylic acid
CAS registry number 98-79-3
Monoisotopic molecular weight 129.042593095

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 HMDB0000267 as a lung cancer biomarker




The studies that identify HMDB0000267 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 –
Yue et al. 2018 China plasma diagnosis SCLC – 20 – – – healthy 20 – – –
Klupczynska et al. 2016b Poland serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 90 58, 32 64 ± 6.9 smoker, non-smoker, unknown healthy 62 40, 22 62 ± 8.8 smoker, non-smoker, unknown
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 –
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 –
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 –
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
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – before vs. after treatment (operation) 30 – 61.58 ± 10.67 –
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015b China serum – lung cancer (postoperative) – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
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 –
Klupczynska et al. 2017 Poland serum diagnosis adenocarcinoma, squamous cell carcinoma I, II 50 28, 22 65 (53-86) – healthy 25 14, 11 64 (50-78) –
Klupczynska et al. 2017 Poland serum diagnosis adenocarcinoma, squamous cell carcinoma I, II 50 28, 22 65 (53-86) – healthy 25 14, 11 64 (50-78) –
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 –
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
Huang et al. 2019 China plasma diagnosis lung cancer – 31 19, 12 28-64 – healthy 35 24, 11 23-60 –
Huang et al. 2019 China plasma diagnosis lung cancer – 31 19, 12 28-64 – healthy 35 24, 11 23-60 –
Zhao et al. 2021 China Serum diagnosis LCC, ADC, SCC, SCLC I, II, III, IV 39 21, 85 – – healthy control 40 18, 89 – –
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 –
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 LC ESI negative linear ion-trap MS/MS
Yue et al. 2018 LC ESI both QTRAP MS/MS
Klupczynska et al. 2016b LC ESI negative triple quadrupole MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
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 – – – –
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF MS/MS
Klupczynska et al. 2017 LC ESI positive Q-Orbitrap MS/MS
Klupczynska et al. 2017 LC ESI positive Q-Orbitrap 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
Mu et al. 2019 GC – – – –
Huang et al. 2019 LC ESI negative Q-Orbitrap MS/MS
Huang et al. 2019 LC ESI positive Q-Orbitrap MS/MS
Zhao et al. 2021 LC ESI both Q-TOF 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 –
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
Yue et al. 2018 Analyst, MultiQuant –
Klupczynska et al. 2016b Analyst software –
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
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)
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Klupczynska et al. 2017 MZmine 2.19 software In-house library
Klupczynska et al. 2017 MZmine 2.19 software In-house library, HMDB
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Mu et al. 2019 – –
Huang et al. 2019 XCMS OSI-SMMS
Huang et al. 2019 XCMS OSI-SMMS
Zhao et al. 2021 XCMS, CAMERA, metaX KEGG, 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 195323.272727273 203595.090909091 0.96 0.90 – –
Miyamoto et al. 2015 Analysis of Covariance 201697.888888889 197444.35 1.02 0.61 – –
Mazzone et al. 2016 two- sample independent t test 1.003324± 0.2046687 1.020258± 0.1668089 0.98 0.46 0.54 –
Yue et al. 2018 OPLS-DA, student’s t-test 2.91±0.84 ng/mL 1.71±0.92 ng/mL 6.36 1.23e-09 – 1.98
Klupczynska et al. 2016b Mann-Whitney U test 26.99 ± 11.56 μmol/l 35.28 ± 10.22 μmol/l 0.77 2.64e-08 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 44799 ± 14621 46504 ± 12210 0.96 0.42 0.67 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 45736 ± 9536 39616 ± 8849 1.15 5.00e-03 0.09 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 60393 ± 13891 56530 ± 11065 1.07 0.20 0.54 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 80594 ± 21724 83104 ± 16481 0.97 0.39 0.69 –
Hori et al. 2011 student’s t-test, PLS-DA – – 2.06 5.00e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.89 0.11 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.85 1.90e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.83 6.20e-03 – –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.82 1.00e-03 – 1.35
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.82 1.00e-03 – 1.09
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.82 0.02 – 1.00
Wikoff et al. 2015b OPLS-DA – – 1.10 – 0.09 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.83 0.04 – 1.01
Klupczynska et al. 2017 t-test – – 0.79 3.50e-04 4.54e-03 –
Klupczynska et al. 2017 t-test – – 0.78 1.26e-03 0.01 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.17 0.06 0.08 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.00 0.99 0.99 –
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.77 1.00e-03 1.00e-03 1.47
Huang et al. 2019 OPLS-DA, Mann-Whitney U test – – 0.69 9.47e-04 – 1.72
Huang et al. 2019 OPLS-DA, Mann-Whitney U test – – 0.52 8.00e-07 – 2.07
Zhao et al. 2021 Student’s t-test, PLS-DA, – – 1.62 1.28e-05 – 2.16
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 1.27e-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) – – – 5.61e-03 – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Mazzone et al. 2016 – – – – – –
Yue et al. 2018 – – – – – –
Klupczynska et al. 2016b ROC curve analysis stage I vs. control: 29.6; stage II vs. control: 29.6; stage III vs. control: 28 0.766; stage I vs. control: 0.752; stage II vs. control: 0.748; stage III vs. control: 0.799 stage I vs. control: 0.8; stage II vs. control: 0.8; stage III vs. control: 0.8 stage I vs. control: 0.7; stage II vs. control: 0.7; stage III vs. control: 0.7 –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.57 – – –
Klupczynska et al. 2017 ROC curve analysis (Monte-Carlo cross validation) – 0.705 (0.560–0.813) 0.63 0.72 –
Klupczynska et al. 2017 ROC curve analysis (Monte-Carlo cross validation) – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Mu et al. 2019 – – – – – –
Huang et al. 2019 – – – – – –
Huang et al. 2019 – – – – – –
Zhao et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –