Showing information for HMDB0000357 ('3-hydroxybutanoic acid', '3-hydroxybutyrate', '3-hydroxybutyric acid', 'β-hydroxybutyric acid')

Metabolite information
HMDB ID HMDB0000357
Synonyms
(+ -)-3-Hydroxybutyric acid
(1)-3-Hydroxybutyrate
(1)-3-Hydroxybutyric acid
3 HBA
3 Hydroxybutyrate
3 Hydroxybutyric acid
3-Hydroxy-butanoate
3-Hydroxy-butanoic acid
3-Hydroxy-butyrate
3-Hydroxy-butyric acid
3-Hydroxybutanoate
3-Hydroxybutanoic acid
3-Hydroxybuttersaeure
3-Hydroxybutyrate
3-OH-Butyrate
3-OH-Butyric acid
BHBA
Biopol
DL-3-Hydroxybutyrate
DL-3-Hydroxybutyric acid
DL-b-Hydroxybutyrate
DL-b-Hydroxybutyric acid
DL-beta-Hydroxybutyrate
DL-beta-Hydroxybutyric acid
DL-β-hydroxybutyrate
DL-β-hydroxybutyric acid
b-Hydroxy-N-butyrate
b-Hydroxy-N-butyric acid
b-Hydroxybutanoate
b-Hydroxybutanoic acid
b-Hydroxybuttersaeure
b-Hydroxybutyrate
b-Hydroxybutyric acid
beta Hydroxybutyrate
beta Hydroxybutyric acid
beta-Hydroxy-N-butyrate
beta-Hydroxy-N-butyric acid
beta-Hydroxybutanoate
beta-Hydroxybutanoic acid
beta-Hydroxybuttersaeure
beta-Hydroxybutyrate
beta-Hydroxybutyric acid
β-hydroxy-N-butyrate
β-hydroxy-N-butyric acid
β-hydroxybutanoate
β-hydroxybutanoic acid
β-hydroxybuttersaeure
β-hydroxybutyrate
β-hydroxybutyric acid
Chemical formula C4H8O3
IUPAC name
3-hydroxybutanoic acid
CAS registry number 300-85-6
Monoisotopic molecular weight 104.047344122

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

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000357 as a lung cancer biomarker


The studies that identify HMDB0000357 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
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 –
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 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
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
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
Wen et al. 2013 China plasma diagnosis adenocarcinoma I 31 15, 16 median: 63 (40-81) smoker, non-smoker healthy 28 20, 8 median: 37 (29-50) smoker, non-smoker
Chen et al. 2015b China serum – lung cancer – 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
Chen et al. 2018 China serum diagnosis NSCLC I, II 90 40, 50 58.1 ± 9.0 – healthy 90 42, 48 53.0 ± 11.8 –
Chen et al. 2018 China serum diagnosis NSCLC I, II 90 40, 50 58.1 ± 9.0 – healthy 90 42, 48 53.0 ± 11.8 –
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 –
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
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 –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Wen et al. 2013 GC EI – – –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Chen et al. 2018 GC EI – TOF –
Chen et al. 2018 LC ESI negative Q-TOF MS/MS
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
Reference Data processing software Database search
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
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
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)
Wen et al. 2013 MassHunter, Mass Profiler Professional software (Agilent) NIST 08, HMDB, METLIN, LIPID MAPS
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Chen et al. 2018 Chroma TOF LECO-Fiehn Rtx 5
Chen et al. 2018 Analyst TF, XCMS in-house
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
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 11704 ± 22167 5402 ± 5482 2.17 0.10 0.50 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 14199 ± 25818 6404 ± 6606 2.22 0.09 0.40 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 2.1996 ± 2.0896 3.8785 ± 3.8139 0.57 2.94e-03 0.07 –
Mazzone et al. 2016 two- sample independent t test 3.210772± 4.479762 1.303132± 1.82276 2.46 6.46e-07 0.02 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 5559 ± 5925 4093 ± 5107 1.36 0.17 0.42 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 9752 ± 10721 7082 ± 9138 1.38 0.11 0.35 –
Hori et al. 2011 student’s t-test, PLS-DA – – 2.41 3.80e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 2.20 2.90e-03 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 2.09 0.10 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.48 0.42 – –
Wen et al. 2013 Mann–Whitney–Wilcoxon test, OPLS-DA – – 0.36 1.46e-03 – 1.57
Chen et al. 2015b PCA, PLS-DA, independent t test – – 5.21 1.00e-03 – 1.14
Wikoff et al. 2015b OPLS-DA – – 1.10 – 0.52 –
Chen et al. 2018 PCA, OPLS-DA – – 1.60 – – 3.29
Chen et al. 2018 PCA, OPLS-DA – – 0.50 – – 1.54
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.13 0.10 0.14 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.08 0.11 0.13 –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Mazzone et al. 2016 – – – – – –
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 – – – – – –
Wen et al. 2013 ROC curve analysis – 0.74 – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Chen et al. 2018 ROC curve – – – – –
Chen et al. 2018 ROC curve – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –