Showing information for HMDB0000134 ('fumaric acid', 'fumarate')

Metabolite information
HMDB ID HMDB0000134
Synonyms
(2E)-2-Butenedioate
(2E)-2-Butenedioic acid
(2E)-But-2-enedioate
(2E)-But-2-enedioic acid
(e)-2-Butenedioate
(e)-2-Butenedioic acid
2-(e)-Butenedioate
2-(e)-Butenedioic acid
Allomaleate
Allomaleic acid
Ammonium fumarate
Boletate
Boletic acid
FC 33
Fumarate
Fumarsaeure
Furamag
Lichenate
Lichenic acid
Magnesium fumarate
Sodium fumarate
e297
trans-1,2-Ethylenedicarboxylate
trans-1,2-Ethylenedicarboxylic acid
trans-2-Butenedioate
trans-2-Butenedioic acid
trans-But-2-enedioate
trans-But-2-enedioic acid
trans-Butenedioate
trans-Butenedioic acid
Chemical formula C4H4O4
IUPAC name
(2E)-but-2-enedioic acid
CAS registry number 110-17-8
Monoisotopic molecular weight 116.010958616

Chemical taxonomy
Super class Organic acids and derivatives
Class Carboxylic acids and derivatives
Sub class Dicarboxylic acids and derivatives

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000134 as a lung cancer biomarker



The studies that identify HMDB0000134 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
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 –
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 –
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 III, IV 22 – – – 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 I, II 11 – – – 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
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
Ahmed et al. 2021 Canada Urine diagnosis NSCLC pre-surgery I, II 29 11,18 63.8 ± 7.0 former, current, non-smoker NSCLC post-surgery 29 11,18 63.8 ± 7.0 former, current, non-smoker
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
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Mazzone et al. 2016 GC EI – quadrupole 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 – – – –
Wikoff et al. 2015b GC EI – TOF –
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 – – – –
Ahmed 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
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
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)
Wikoff et al. 2015b BinBase NIST11, BinBase
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Mu et al. 2019 – –
Ahmed et al. 2021 MassHunter, Mass Profiler Professional HMDB, METLIN
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 1203.72727272727 1162.36363636364 1.04 0.68 – –
Miyamoto et al. 2015 Analysis of Covariance 1230.55555555556 1140.45 1.08 0.33 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.30476 ± 0.18536 0.3389 ± 0.2455 0.90 0.16 0.47 –
Mazzone et al. 2016 two- sample independent t test 1.042788± 0.480176 1.037012± 0.2996676 1.01 0.90 0.79 –
Klupczynska et al. 2016b Mann-Whitney U test 1.29 ± 1.15 μmol/l 1.5 ± 1.22 μmol/l 0.86 3.00e-04 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 181 ± 62 156 ± 53 1.17 0.07 0.23 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 260 ± 103 259 ± 123 1.00 1.00 1.00 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 316 ± 117 328 ± 126 0.96 0.51 0.74 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 333 ± 100 284 ± 73 1.17 0.02 0.15 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.86 2.00e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.84 1.00e-04 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.76 1.00e-04 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.61 1.00e-04 – –
Wikoff et al. 2015b OPLS-DA – – 1.00 – 0.84 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.49 9.26e-07 2.42e-06 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.09 0.24 0.31 –
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 1.15 0.02 0.04 1.38
Ahmed et al. 2021 Pair t-test – – 17.00 0.01 – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Mazzone et al. 2016 – – – – – –
Klupczynska et al. 2016b ROC curve analysis – 0.673 – – –
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 – – – – – –
Wikoff et al. 2015b – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Mu et al. 2019 – – – – – –
Ahmed et al. 2021 – – – – – –