Showing information for HMDB0000744 ('malic acid', 'malate')

Metabolite information
HMDB ID HMDB0000744
Synonyms
2-Hydroxybutanedioate
2-Hydroxybutanedioic acid
2-Hydroxyethane-1,2-dicarboxylate
2-Hydroxyethane-1,2-dicarboxylic acid
2-Hydroxysuccinate
2-Hydroxysuccinic acid
Aepfelsaeure
Apple acid
Calcium [hydroxy-1-malate] hexahydrate
Csf
Cytoplasma
DL-Malate
DL-Malic acid
Deoxytetrarate
Deoxytetraric acid
Digestion
Faecal
Faeces
Fauna
Fecal
Flora
Gramineae
H2Mal
Hydroxybutanedioate
Hydroxybutanedioic acid
Hydroxysuccinate
Hydroxysuccinic acid
Legume
Malate
Malic acid, [R]-isomer
Malic acid, calcium salt, [1:1], [S]-isomer
Malic acid, disodium salt
Malic acid, disodium salt, [R]-isomer
Malic acid, disodium salt, [S]-isomer
Malic acid, magnesium salt [2:1]
Malic acid, monopotassium salt, [+-]-isomer
Malic acid, potassium salt, [R]-isomer
Malic acid, sodium salt, [+-]-isomer
Musashi-NO-ringosan
Papilionoideae
Pomalus acid
R,S-Malate
R,S-Malic acid
R,SMalate
R,SMalic acid
Soy
Soya
Soya bean
Soybean
Stool
a-Hydroxysuccinate
a-Hydroxysuccinic acid
alpha-Hydroxysuccinate
alpha-Hydroxysuccinic acid
e296
α-hydroxysuccinate
α-hydroxysuccinic acid
Chemical formula C4H6O5
IUPAC name
2-hydroxybutanedioic acid
CAS registry number 6915-15-7
Monisotopic molecular weight 134.021523302

Chemical taxonomy
Super class Organic acids and derivatives
Class Hydroxy acids and derivatives
Sub class Beta hydroxy acids and derivatives

Biological properties
Pahtways
Fructose-1,6-diphosphatase deficiency
Gluconeogenesis
Glycogen Storage Disease Type 1A [GSD1A] or Von Gierke Disease
Glycogenosis, Type IA. Von gierke disease
Glycogenosis, Type IB
Glycogenosis, Type IC
Malate-Aspartate Shuttle
Phosphoenolpyruvate carboxykinase deficiency 1 [PEPCK1]
The Oncogenic Action of Fumarate
Triosephosphate isomerase
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000744 ('malic acid', 'malate')

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
Fahrmann et al. 2015 – plasma diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Mazzone et al. 2016 – 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 –
Miyamoto et al. 2015 – 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 – 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
Hori et al. 2011 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC III, IV 22 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Fahrmann et al. 2015 – 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 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II 11 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Fahrmann et al. 2015 – 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 – serum diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Huang et al. 2019 – plasma diagnosis lung cancer – 31 19, 12 28-64 – healthy 35 24, 11 23-60 –
Klupczynska et al. 2017 – serum diagnosis adenocarcinoma, squamous cell carcinoma I, II 50 28, 22 65 (53-86) – healthy 25 14, 11 64 (50-78) –
Hori et al. 2011 – 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 – 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
Yue et al. 2018 – plasma diagnosis SCLC – 20 – – – healthy 20 – – –
Moreno et al. 2018 – 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 –
Moreno et al. 2018 – 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 –
Wikoff et al. 2015b – 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
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Fahrmann et al. 2015 GC EI – TOF –
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Hori et al. 2011 GC – – – –
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Huang et al. 2019 LC ESI negative Q-Orbitrap MS/MS
Klupczynska et al. 2017 LC ESI positive Quadrupole- Orbitrap MS/MS
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Yue et al. 2018 LC ESI positive, negative QTRAP MS/MS
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Wikoff et al. 2015b GC EI – TOF –
Reference Data processing software Database search
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Miyamoto et al. 2015 ChromaTOF software (Leco) 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)
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)
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Huang et al. 2019 XCMS OSI-SMMS
Klupczynska et al. 2017 MZmine 2.19 software HMDB
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)
Yue et al. 2018 Analyst, MultiQuant –
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Wikoff et al. 2015b BinBase NIST11, BinBase
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 163 ± 51 194 ± 258 0.84 0.888 0.958 –
Mazzone et al. 2016 two- sample independent t test 1.151086± 0.6901209 1.061722± 0.5293276 1.08416892557562 0.2280164 0.341191697 –
Miyamoto et al. 2015 Analysis of Covariance 1149.09090909091 1557.81818181818 0.737628384687208 0.120036056161392 – –
Miyamoto et al. 2015 Analysis of Covariance 1463.88888888889 1254.1 1.16728242475791 0.0856627899454845 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.43 0.033 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 248 ± 113 173 ± 104 1.43 0.004 0.117 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.35 0.0036 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 265 ± 109 197 ± 94 1.35 0.002 0.052 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 439 ± 1174 193 ± 55 2.27 0.001 0.012 –
Huang et al. 2019 OPLS-DA, Mann-Whitney U test – – 0.690930505 0.000749386 – 1.414421201
Klupczynska et al. 2017 t-test – – 0.81 0.00049 0.00565 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.84 0.0003 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.4 0.0002 – –
Yue et al. 2018 OPLS-DA, student’s t-test 398.03±103.15 ng/mL 623.55±63.80 ng/mL 0.181746564665039 0.00000929 – 1.57
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.63439602171191 0.00000272599905188882 0.0000163933367640985 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.28742572366836 0.00000232941977013598 0.00000553237195407296 –
Wikoff et al. 2015b OPLS-DA – – 1.3 – 0.045 –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Fahrmann et al. 2015 random forest – – – – –
Mazzone et al. 2016 – – – – – –
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Hori et al. 2011 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Huang et al. 2019 – – – – – –
Klupczynska et al. 2017 ROC curve analysis (Monte-Carlo cross validation) – 0.717 (0.599–0.843) 0.71 0.64 –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Yue et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Wikoff et al. 2015b – – – – – –