Showing information for HMDB0000115 ('glycolic acid', 'glycolate')

Metabolite information
HMDB ID HMDB0000115
Synonyms
2-Hydroxyacetate
2-Hydroxyacetic acid
2-Hydroxyethanoate
2-Hydroxyethanoic acid
GlyPure
GlyPure 70
Glycocide
Glycolate
Glycolic acid, 1-(14)C-labeled
Glycolic acid, 2-(14)C-labeled
Glycolic acid, calcium salt
Glycolic acid, monoammonium salt
Glycolic acid, monolithium salt
Glycolic acid, monopotassium salt
Glycolic acid, monosodium salt
Glycolic acid, potassium salt
Glycollate
Glycollic acid
HOCH2COOH
Hydroxyacetate
Hydroxyacetic acid
Hydroxyethanoate
Hydroxyethanoic acid
Sodium glycolate
a-Hydroxyacetate
a-Hydroxyacetic acid
alpha-Hydroxyacetate
alpha-Hydroxyacetic acid
α-hydroxyacetate
α-hydroxyacetic acid
Chemical formula C2H4O3
IUPAC name
2-hydroxyacetic acid
CAS registry number 79-14-1
Monoisotopic molecular weight 76.016043994

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

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000115 as a lung cancer biomarker


The studies that identify HMDB0000115 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 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
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
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 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 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 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
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
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
Qi et al. 2021 China blood diagnosis adenocarcinoma, squamous cell carcinoma, small cell lung cancer, other types, unknown types I, II, III, IV 98 51, 47 Median: 50 (32-69) – healthy 75 36, 39 Median: 50 (31-69) –
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
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 –
Mu et al. 2019 GC – – – –
Qi et al. 2021 LC ESI both Q-Orbitrap MS/MS
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
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
Mu et al. 2019 – –
Qi et al. 2021 ProteoWizard, XCMS, Xcalibur, CAMERA mzCloud, ChemSpider, LipidBlast and Fiehn HILIC
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 2792.83333333333 2829.2 0.99 0.79 – –
Miyamoto et al. 2015 Analysis of Covariance 3048.09090909091 2575.54545454545 1.18 0.07 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.44052 ± 0.38849 0.78842 ± 0.56528 0.56 7.21e-04 0.02 –
Mazzone et al. 2016 two- sample independent t test 0.9956798± 0.2439497 1.0617837± 0.3093829 0.94 0.07 0.14 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 507 ± 187 516 ± 169 0.98 0.59 0.79 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 629 ± 211 568 ± 199 1.11 0.20 0.57 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 639 ± 167 672 ± 219 0.95 0.84 0.94 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 886 ± 233 987 ± 246 0.90 0.10 0.33 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.33 0.01 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.98 0.85 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.95 0.51 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.94 0.47 – –
Wikoff et al. 2015b OPLS-DA – – 1.00 – 0.51 –
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 1.50 1.00e-03 1.00e-03 1.36
Qi et al. 2021 PCA, OPLS-DA, Student’s t test – – 1.28 1.23e-05 – 1.89
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 – – combination of nine metabolites: 100 combination of nine metabolites: 86 –
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 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Wikoff et al. 2015b – – – – – –
Mu et al. 2019 – – – – – –
Qi et al. 2021 – – – – – –