Showing information for HMDB0000883 ('val', 'L-valine', 'valine')

Metabolite information
HMDB ID HMDB0000883
Synonyms
2-amino-3-Methylbutanoate
2-amino-3-Methylbutanoic acid
2-amino-3-Methylbutyrate
2-amino-3-Methylbutyric acid
Beautification product
Carcinoma of the lung
Coffee
Coffee bean
Csf
Cucurbits
Cytoplasma
Dietary supplement
Digestion
Epileptic spasms
Essential mineral
Extracellular region
Faecal
Faeces
Fauna
Fecal
Flora
Gourds
Gramineae
Hypoglycaemia
L Valine
L-Valin
L-[+]-a-Aminoisovalerate
L-[+]-a-Aminoisovaleric acid
L-[+]-alpha-Aminoisovalerate
L-[+]-alpha-Aminoisovaleric acid
L-[+]-α-aminoisovalerate
L-[+]-α-aminoisovaleric acid
L-a-amino-b-Methylbutyrate
L-a-amino-b-Methylbutyric acid
L-alpha-amino-beta-Methylbutyrate
L-alpha-amino-beta-Methylbutyric acid
L-α-amino-β-methylbutyrate
L-α-amino-β-methylbutyric acid
Legume
Leukaemia
Lung carcinoma
Nutraceutical
Papilionoideae
Pcp
Personal hygiene
Soy
Soya
Soya bean
Soybean
Stool
Toiletries
Toiletry
Trace mineral
V
VALINE
Val
Valine transaminase deficiency
Valinemia
[2S]-2-amino-3-Methylbutanoate
[2S]-2-amino-3-Methylbutanoic acid
[S]-2-amino-3-Methyl-butanoate
[S]-2-amino-3-Methyl-butanoic acid
[S]-2-amino-3-Methylbutanoate
[S]-2-amino-3-Methylbutanoic acid
[S]-2-amino-3-Methylbutyrate
[S]-2-amino-3-Methylbutyric acid
[S]-Valine
[S]-a-amino-b-Methylbutyrate
[S]-a-amino-b-Methylbutyric acid
[S]-alpha-amino-beta-Methylbutyrate
[S]-alpha-amino-beta-Methylbutyric acid
Chemical formula C5H11NO2
IUPAC name
(2S)-2-amino-3-methylbutanoic acid
CAS registry number 72-18-4
Monisotopic molecular weight 117.078978601

Chemical taxonomy
Super class Organic acids and derivatives
Class Carboxylic acids and derivatives
Sub class Amino acids, peptides, and analogues

Biological properties
Pahtways
2-Methyl-3-Hydroxybutryl CoA Dehydrogenase Deficiency
3-Hydroxy-3-Methylglutaryl-CoA Lyase Deficiency
3-Methylcrotonyl Coa Carboxylase Deficiency Type I
3-Methylglutaconic Aciduria Type I
3-Methylglutaconic Aciduria Type III
3-Methylglutaconic Aciduria Type IV
3-hydroxyisobutyric acid dehydrogenase deficiency
3-hydroxyisobutyric aciduria
Amikacin Action Pathway
Arbekacin Action Pathway
Azithromycin Action Pathway
Beta-Ketothiolase Deficiency
Chloramphenicol Action Pathway
Clarithromycin Action Pathway
Clindamycin Action Pathway
Clomocycline Action Pathway
Demeclocycline Action Pathway
Doxycycline Action Pathway
Erythromycin Action Pathway
Gentamicin Action Pathway
Isobutyryl-coa dehydrogenase deficiency
Isovaleric Aciduria
Isovaleric acidemia
Josamycin Action Pathway
Kanamycin Action Pathway
Lincomycin Action Pathway
Lymecycline Action Pathway
Malonic Aciduria
Malonyl-coa decarboxylase deficiency
Maple Syrup Urine Disease
Methacycline Action Pathway
Methylmalonate Semialdehyde Dehydrogenase Deficiency
Methylmalonic Aciduria
Methylmalonic Aciduria Due to Cobalamin-Related Disorders
Minocycline Action Pathway
Neomycin Action Pathway
Netilmicin Action Pathway
Oxytetracycline Action Pathway
Paromomycin Action Pathway
Propanoate Metabolism
Propionic Acidemia
Rolitetracycline Action Pathway
Roxithromycin Action Pathway
Spectinomycin Action Pathway
Streptomycin Action Pathway
Telithromycin Action Pathway
Tetracycline Action Pathway
Tigecycline Action Pathway
Tobramycin Action Pathway
Transcription/Translation
Troleandomycin Action Pathway
Valine, Leucine and Isoleucine Degradation
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000883 ('val', 'L-valine', 'valine')

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
Ni et al. 2019 – serum diagnosis lung cancer – 40 26, 14 66.7 (49-83) – healthy 100 65, 35 64.1 (41-90) –
Ni et al. 2016 – serum diagnosis lung cancer – 40 14, 26 67 – healthy 100 – – –
Chen et al. 2015 – serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015 – serum – lung cancer (postoperative) – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015 – serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
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 –
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
Klupczynska et al. 2016a – serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 90 58, 32 64 (48-86) current, non-smoker, unknown healthy 63 41, 22 62 (43-78) smoker, non-smoker, unknown
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
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
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
Maeda et al. 2010 – plasma – NSCLC I, II, III, IV 141 93, 48 62.7 ± 9.2 former, current, non-smoker healthy 423 279, 144 61.1 ± 8.7 former, current, non-smoker
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 –
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 –
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
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 –
Callejon-Leblic et al. 2016 – bronchoalveolar lavage fluid diagnosis lung cancer – 24 16, 8 66 ± 11 – noncancerous lung diseases 31 23, 8 56 ± 13 –
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
Roś-Mazurczyk et al. 2017 – serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 31 17, 14 52-72 – healthy 92 52, 40 52-73 –
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 –
Ni et al. 2019 – serum diagnosis NSCLC, SCLC II, III, IV 17 13, 4 66.3 (53-77) former, current, non-smoker healthy 30 23, 7 62.8 (34-85) former, current, non-smoker
Callejon-Leblic et al. 2019 – serum diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, non-smoker
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
Yang et al. 2010 – urine diagnosis adenocarcinoma, squamous cell carcinoma – 35 23, 12 61.8 ± 13.3, 57.4 ± 9.8 – healthy 32 27, 5 57.1 ± 9.9 / 45.6 ± 10.8 –
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Ni et al. 2019 LC ESI positive triple quadrupole MS/MS
Ni et al. 2016 LC ESI positive Triple quadrupole MS/MS
Chen et al. 2015 LC ESI positive Q-TOF –
Chen et al. 2015 LC ESI positive Q-TOF –
Chen et al. 2015 GC EI – quadrupole –
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Klupczynska et al. 2016a LC – – QTRAP MS/MS
Hori et al. 2011 GC – – – –
Miyamoto et al. 2015 GC EI – TOF MS/MS
Hori et al. 2011 GC – – – –
Maeda et al. 2010 LC ESI positive quadrupole –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Miyamoto et al. 2015 GC EI – TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF MS/MS
Hori et al. 2011 GC – – – –
Roś-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Mazzone et al. 2016 LC ESI positive linear ion-trap MS/MS
Ni et al. 2019 LC ESI positive triple quadrupole MS/MS
Callejon-Leblic et al. 2019 GC EI – ion trap –
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 –
Yang et al. 2010 LC ESI positive QTRAP MS/MS
Reference Data processing software Database search
Ni et al. 2019 – HMDB, KEGG, SMPDB
Ni et al. 2016 – –
Chen et al. 2015 Mass Hunter Qualitative Analysis Software (Agilent Technologies) METLIN
Chen et al. 2015 Mass Hunter Qualitative Analysis Software (Agilent Technologies) METLIN
Chen et al. 2015 ChemStation NIST
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)
Klupczynska et al. 2016a – –
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
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)
Maeda et al. 2010 Xcalibur –
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Roś-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Ni et al. 2019 – HMDB, KEGG, SMPDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Wikoff et al. 2015b BinBase NIST11, BinBase
Yang et al. 2010 MarkerView HMDB, KEGG, Pubchem, mass bank
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Ni et al. 2019 Mann-Whitney U test, Student's t-test, Welch's F test 136.6 165.03 – <0.001 – –
Ni et al. 2016 one‐way ANOVA 136.60 ± 35.57 μmol/L 165.62 ± 28.08 μmol/L – <0.0001 – –
Chen et al. 2015 PCA, PLS-DA, independent t test – – 1.50003898928582 <0.001 – 1.418
Chen et al. 2015 PCA, PLS-DA, independent t test – – 1.37745004638314 <0.001 – 1.402
Chen et al. 2015 PCA, PLS-DA, independent t test – – 0.835087919428369 <0.001 – 1.28
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 41243 ± 15012 42586 ± 15433 0.97 0.864 0.951 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.04 0.807 – –
Klupczynska et al. 2016a t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA 233.43±55.25 ?M 226.97±48.97 ?M 1.03 0.7597 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.06 0.581 – –
Miyamoto et al. 2015 Analysis of Covariance 321619.454545455 349720.636363636 0.919646772605771 0.561051969647996 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.05 0.553 – –
Maeda et al. 2010 Mann-Whitney U-test, PCA 244.8 ± 47.5 μM 239.8±46.1 μM – 0.28 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 84670 ± 27049 78685 ± 25425 1.076 0.229 0.524 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 76204 ± 21657 81099 ± 18314 0.94 0.219 0.589 –
Miyamoto et al. 2015 Analysis of Covariance 309110.611111111 359573.6 0.859658804514879 0.0753407571079377 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 85730 ± 20658 93604 ± 18189 0.92 0.056 0.31 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.57 0.04 – 1.87
Hori et al. 2011 student’s t-test, PLS-DA – – 1.88 0.019 – –
Roś-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 42.645 ± 15.846 55.047 ± 29.244 0.774701618616818 0.017197 0.20063 –
Mazzone et al. 2016 two- sample independent t test 0.9836223± 0.1859707 1.0549163± 0.24668 0.932417387047674 0.0138691 0.041500997 –
Ni et al. 2019 Mann-Whitney U test, Student's t-test, Welch's F test 150.73 171.69 – 0.009 – –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 0.53 0.001 – 1.5
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.20335558915963 0.000482680810771471 0.00130800540696713 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.3541368919274 0.00000131404010660435 0.00000328715989050242 –
Wikoff et al. 2015b OPLS-DA – – 1 – 0.846 –
Yang et al. 2010 OSC PLS‐DA – – 1.8 – – 1.47
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Ni et al. 2019 ROC analysis – 0.183 – – –
Ni et al. 2016 – – – – – –
Chen et al. 2015 – – – – – –
Chen et al. 2015 – – – – – –
Chen et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Klupczynska et al. 2016a ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis – 0.515 alanine+histidine+ornithine+isoleucine+tryptophan+valine=84.4 alanine+histidine+ornithine+isoleucine+tryptophan+valine=52.4 –
Hori et al. 2011 – – – – – –
Miyamoto et al. 2015 – – – – – –
Hori et al. 2011 – – – – – –
Maeda et al. 2010 ROC curve – combination of 21 amino acid: 0.812 – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.66 – – –
Hori et al. 2011 – – – – – –
Roś-Mazurczyk et al. 2017 ROC curve – – – – –
Mazzone et al. 2016 – – – – – –
Ni et al. 2019 ROC analysis – 0.299 – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.75 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Wikoff et al. 2015b – – – – – –
Yang et al. 2010 – – – – – –