Showing information for HMDB0000008 ('butanoic acid, 2-hydroxy', '2-hydroxybutanoic acid', '2-hydroxybutyrate', '2-hydroxybutyric acid', 'α-hydroxybutyric acid')

Metabolite information
HMDB ID HMDB0000008
Synonyms
(RS)-2-Hydroxybutyrate
(RS)-2-Hydroxybutyric acid
2-Hydroxy-DL-butyrate
2-Hydroxy-DL-butyric acid
2-Hydroxy-N-butyrate
2-Hydroxy-N-butyric acid
2-Hydroxy-butanoate
2-Hydroxy-butanoic acid
2-Hydroxybutanoate
2-Hydroxybutanoic acid
2-Hydroxybutyrate
2-Hydroxybutyric acid, (+-)-isomer
2-Hydroxybutyric acid, (R)-isomer
2-Hydroxybutyric acid, monosodium salt
2-Hydroxybutyric acid, monosodium salt, (+-)-isomer
DL-2-Hydroxybutanoate
DL-2-Hydroxybutanoic acid
DL-a-Hydroxybutyrate
DL-a-Hydroxybutyric acid
DL-alpha-Hydroxybutyrate
DL-alpha-Hydroxybutyric acid
a-Hydroxy-N-butyrate
a-Hydroxy-N-butyric acid
a-Hydroxybutanoate
a-Hydroxybutanoic acid
a-Hydroxybutyrate
a-Hydroxybutyric acid
alpha-Hydroxy-N-butyrate
alpha-Hydroxy-N-butyric acid
alpha-Hydroxybutanoate
alpha-Hydroxybutanoic acid
alpha-Hydroxybutyrate
alpha-Hydroxybutyric acid
α-hydroxybutanoate
α-hydroxybutanoic acid
α-hydroxybutyrate
α-hydroxybutyric acid
Chemical formula C4H8O3
IUPAC name
2-hydroxybutanoic acid
CAS registry number 600-15-7
Monoisotopic molecular weight 104.047344122

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 HMDB0000008 as a lung cancer biomarker



The studies that identify HMDB0000008 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
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 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 –
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 –
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 –
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 –
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
Hao et al. 2016 Canada serum diagnosis lung cancer I, II, III, IV 25 15, 10 64 (42–77) smoker, non-smoker before vs. after treatment (radiation treatment) – – – 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 –
Zheng et al. 2021 China Serum diagnosis lung cancer I, II, III, IV 57 38, 19 Median: 62 (52-69) smoker, non-smoker healthy 59 48, 11 Median: 60 (59-62) smoker, 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
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 –
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Klupczynska et al. 2016b LC ESI negative triple quadrupole MS/MS
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 –
Hao et al. 2016 GC – – 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
Zheng et al. 2021 GC EI – quadrupole –
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
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
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Klupczynska et al. 2016b Analyst software –
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
Hao et al. 2016 Chenomx NMR Suite 7.1, Metabolite Detector HMDB
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Zheng et al. 2021 MassHunter Workstation software, Mass Profiler Professional software NIST14, HMDB, Golm Metabolome Database
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 46231.1666666667 42560.5 1.09 0.39 – –
Miyamoto et al. 2015 Analysis of Covariance 48482.7272727273 40115.8181818182 1.21 0.19 – –
Mazzone et al. 2016 two- sample independent t test 1.403545± 0.7636425 0.99769± 0.5246037 1.41 3.00e-07 0.02 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 12996 ± 7017 11486 ± 8094 1.13 0.22 0.59 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 16545 ± 8851 12608 ± 6812 1.31 6.00e-03 0.07 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 16636 ± 8787 14667 ± 9574 1.13 0.29 0.62 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 5.4432 ± 1.9203 8.5115 ± 8.1791 0.64 0.40 0.68 –
Klupczynska et al. 2016b Mann-Whitney U test 72.43 ± 33.38 μmol/l 58.99 ± 33.39 μmol/l 1.23 2.83e-03 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 8718 ± 4691 7190 ± 4294 1.21 0.08 0.26 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.64 0.02 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.00 0.91 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.96 0.61 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.95 0.53 – –
Wikoff et al. 2015b OPLS-DA – – 1.10 – 0.18 –
Hao et al. 2016 OPLS-DA, CV-ANOVA – – – – – >1
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.23 1.60e-05 3.36e-05 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.06 0.29 0.35 –
Zheng et al. 2021 Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA – – 1.02 3.57e-10 4.02e-10 1.03
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Mazzone et al. 2016 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Klupczynska et al. 2016b ROC curve analysis – 0.643 – – –
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 – – – – – –
Hao et al. 2016 – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Zheng et al. 2021 ROC analysis – 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) – – –