Showing information for HMDB0000625 ('gluconic acid', 'D-gluconic acid', 'gluconate')

Metabolite information
HMDB ID HMDB0000625
Synonyms
(2R,3S,4R,5R)-2,3,4,5,6-Pentahydroxyhexanoate
(2R,3S,4R,5R)-2,3,4,5,6-Pentahydroxyhexanoic acid
2,3,4,5,6-Pentahydroxy-hexanoate
2,3,4,5,6-Pentahydroxy-hexanoic acid
2,3,4,5,6-Pentahydroxyhexanoate
2,3,4,5,6-Pentahydroxyhexanoic acid
Boron gluconate
D-Gluconate
D-Gluconic acid
D-Gluconsaeure
D-Glukonsaeure
D-gluco-Hexonate
D-gluco-Hexonic acid
Dextronate
Dextronic acid
GCO
Glosanto
Gluconate
Gluconic acid, (113)indium-labeled
Gluconic acid, (14)C-labeled
Gluconic acid, (159)dysprosium-labeled salt
Gluconic acid, (99)technecium (5+) salt
Gluconic acid, 1-(14)C-labeled
Gluconic acid, 6-(14)C-labeled
Gluconic acid, aluminum (3:1) salt
Gluconic acid, ammonium salt
Gluconic acid, calcium salt
Gluconic acid, cesium(+3) salt
Gluconic acid, cobalt (2:1) salt
Gluconic acid, copper salt
Gluconic acid, fe(+2) salt, dihydrate
Gluconic acid, lanthanum(+3) salt
Gluconic acid, magnesium (2:1) salt
Gluconic acid, manganese (2:1) salt
Gluconic acid, monolithium salt
Gluconic acid, monopotassium salt
Gluconic acid, monosodium salt
Gluconic acid, potassium salt
Gluconic acid, sodium salt
Gluconic acid, strontium (2:1) salt
Gluconic acid, tin(+2) salt
Gluconic acid, zinc salt
Glycogenate
Glycogenic acid
Glyconate
Glyconic acid
Hexonate
Hexonic acid
Lithium gluconate
Magnerot
Magnesium gluconate
Maltonate
Maltonic acid
Manganese gluconate
Pentahydroxycaproate
Pentahydroxycaproic acid
Sodium gluconate
Zinc gluconate
Chemical formula C6H12O7
IUPAC name
(2R,3S,4R,5R)-2,3,4,5,6-pentahydroxyhexanoic acid
CAS registry number 526-95-4
Monoisotopic molecular weight 196.058302738

Chemical taxonomy
Super class Organic oxygen compounds
Class Organooxygen compounds
Sub class Carbohydrates and carbohydrate conjugates

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000625 as a lung cancer biomarker

The studies that identify HMDB0000625 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 –
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 –
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 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 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 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
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
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 –
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 –
Callejon-Leblic et al. 2019 Spain urine diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, 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
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Mazzone et al. 2016 GC EI – 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 – – – –
Chen et al. 2015b GC EI – quadrupole –
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
Callejon-Leblic et al. 2019 GC EI – ion trap –
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
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
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)
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 1560.63636363636 1274.18181818182 1.22 0.27 – –
Miyamoto et al. 2015 Analysis of Covariance 1576.5 1274.25 1.24 0.26 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.093606 ± 0.039298 0.089903 ± 0.045331 1.04 0.15 0.46 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.19286 ± 0.12173 0.25519 ± 0.23889 0.76 0.25 0.59 –
Mazzone et al. 2016 two- sample independent t test 1.205331± 0.596744 1.080778± 0.4651412 1.12 0.05 0.12 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 142 ± 137 158 ± 66 0.90 0.01 0.09 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 207 ± 150 209 ± 71 0.99 0.15 0.50 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 215 ± 151 199 ± 61 1.08 0.67 0.88 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 254 ± 366 220 ± 74 1.16 0.77 0.94 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.73 5.00e-03 – –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.32 1.00e-03 – 1.37
Wikoff et al. 2015b OPLS-DA – – 2.10 – 3.80e-04 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 0.89 0.37 0.43 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 0.74 0.03 0.04 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 3.35 0.02 – 2.01
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 – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Mazzone et al. 2016 – – – – – –
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 – – – – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.74 – – –