Showing information for HMDB0000695 ('ketoleucine', '2-ketoisocaproic acid', 'ketoleucinexa0', '4-methyl-2-oxopentanoate')

Metabolite information
HMDB ID HMDB0000695
Synonyms
2-Ketoisocaproate
2-Ketoisocaproic acid
2-Oxoisocaproate
2-Oxoisocaproic acid
2-Oxoleucine
2-keto-4-Methylvalerate
2-keto-4-Methylvaleric acid
2-oxo-4-METHYLPENTANOIC ACID
2-oxo-4-METHYLPENTANOate
2-oxo-4-Methylvalerate
2-oxo-4-Methylvaleric acid
4-Methyl-2-oxo-valerate
4-Methyl-2-oxo-valeric acid
4-Methyl-2-oxopentanoate
4-Methyl-2-oxopentanoic acid
4-Methyl-2-oxovalerate
4-Methyl-2-oxovaleric acid
Keto-leucine
Ketoisocaproate
Ketoisocaproic acid
Methyloxovalerate
Methyloxovaleric acid
Oxoisocaproate
Oxoisocaproic acid
a-Ketoisocaproate
a-Ketoisocaproic acid
a-Ketoisocapronate
a-Ketoisocapronic acid
a-Oxoisocaproate
a-Oxoisocaproic acid
alpha-Ketoisocaproate
alpha-Ketoisocaproic acid
alpha-Ketoisocapronate
alpha-Ketoisocapronic acid
alpha-Oxoisocaproate
alpha-Oxoisocaproic acid
alpha-keto-Isocaproate
alpha-keto-Isocaproic acid
α-ketoisocaproate
α-ketoisocaproic acid
Chemical formula C6H10O3
IUPAC name
4-methyl-2-oxopentanoic acid
CAS registry number 816-66-0
Monoisotopic molecular weight 130.062994186

Chemical taxonomy
Super class Organic acids and derivatives
Class Keto acids and derivatives
Sub class Short-chain keto acids and derivatives

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000695 as a lung cancer biomarker

The studies that identify HMDB0000695 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 –
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 –
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 –
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 –
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
Jiang et al. 2021 China Saliva diagnosis lung cancer I 45 16, 29 Median: 57.8 smoker, non-smoker healthy 25 10, 15 Median: 52.9 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
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Mazzone et al. 2016 LC ESI negative linear ion-trap 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 –
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
Zheng et al. 2021 GC EI – quadrupole –
Jiang et al. 2021 – MALDI Negative TOF/TOF 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
Wikoff et al. 2015b BinBase NIST11, BinBase
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
Jiang et al. 2021 FlexAnalysis, ClinproTools software, R script HMDB
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 3391.18181818182 3573.18181818182 0.95 0.95 – –
Miyamoto et al. 2015 Analysis of Covariance 3495.05555555556 3470.75 1.01 0.92 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.42639 ± 0.26632 0.58974 ± 0.43089 0.72 3.62e-03 0.07 –
Mazzone et al. 2016 two- sample independent t test 1.055456± 0.3916672 1.062205± 0.3537116 0.99 0.88 0.79 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 2042 ± 664 1939 ± 701 1.05 0.27 0.57 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 2058 ± 962 1734 ± 741 1.19 0.06 0.27 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 3305 ± 1151 3421 ± 1210 0.97 0.65 0.82 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 3629 ± 1120 3587 ± 1005 1.01 0.95 0.98 –
Wikoff et al. 2015b OPLS-DA – – 1.20 – 0.28 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 0.82 6.10e-03 0.01 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 0.75 4.51e-03 6.61e-03 –
Zheng et al. 2021 Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA – – 0.89 4.73e-13 8.51e-13 1.08
Jiang et al. 2021 Student’s t-test, PCA, Cluster analysis by Matlab. OPLS-DA – – – 3.90e-11 2.62e-10 1.66
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 – – – – –
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 – – – – –
Wikoff et al. 2015b – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Zheng et al. 2021 – – – – – –
Jiang et al. 2021 ROC analysis – 0.986 (Combination) 97.2 (Combination) 92% (Combination) –