Showing information for HMDB0000207 ('oleate', 'oleic acid')

Metabolite information
HMDB ID HMDB0000207
Synonyms
(9Z)-9-Octadecenoate
(9Z)-9-Octadecenoic acid
(9Z)-Octadecenoate
(9Z)-Octadecenoic acid
(Z)-9-Octadecanoate
(Z)-9-Octadecanoic acid
(Z)-Octadec-9-enoate
(Z)-Octadec-9-enoic acid
18:1 N-9
18:1DElta9cis
9 Octadecenoic acid
9,10-Octadecenoate
9,10-Octadecenoic acid
9-(Z)-Octadecenoate
9-(Z)-Octadecenoic acid
9-Octadecenoate
9-Octadecenoic acid
Acid, 9-octadecenoic
Acid, cis-9-octadecenoic
Acid, oleic
C18:1 N-9
Century CD fatty acid
Distoline
Emersol 210
Emersol 211
Emersol 213
Emersol 220 white oleate
Emersol 220 white oleic acid
Emersol 221 low titer white oleate
Emersol 221 low titer white oleic acid
Emersol 233LL
Emersol 6321
Emersol 6333 NF
Emersol 7021
Glycon ro
Glycon wo
Industrene 104
Industrene 105
Industrene 205
Industrene 206
L'acide oleique
Metaupon
Octadec-9-enoate
Octadec-9-enoic acid
Oelsaeure
Oelsauere
Oleate
Oleic acid extra pure
Oleinate
Oleinic acid
Pamolyn
Pamolyn 100
Pamolyn 100 FG
Pamolyn 100 FGK
Pamolyn 125
Priolene 6900
Red oil
Vopcolene 27
Wecoline oo
Z-9-Octadecenoate
Z-9-Octadecenoic acid
cis 9 Octadecenoic acid
cis-9-Octadecenoate
cis-9-Octadecenoic acid
cis-Delta(9)-Octadecenoic acid
cis-Octadec-9-enoate
cis-Octadec-9-enoic acid
cis-Oleate
cis-Oleic acid
cis-delta(9)-Octadecenoate
cis-δ(9)-octadecenoate
cis-δ(9)-octadecenoic acid
groco 2
groco 4
groco 5l
groco 6
tego-Oleic 130
Chemical formula C18H34O2
IUPAC name
(9Z)-octadec-9-enoic acid
CAS registry number 112-80-1
Monoisotopic molecular weight 282.255880332

Chemical taxonomy
Super class Lipids and lipid-like molecules
Class Fatty Acyls
Sub class Fatty acids and conjugates

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000207 as a lung cancer biomarker



The studies that identify HMDB0000207 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
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 –
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 –
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 serum diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Chen et al. 2015a China serum diagnosis adenocarcinoma, squamous cell carcinoma, large cell carcinoma I, II, III 30 9, 21 61.58 ± 10.67 – healthy 30 11, 19 60.35 ± 12.48 –
Chen et al. 2015a China serum diagnosis adenocarcinoma, squamous cell carcinoma, large cell carcinoma I, II, III 30 9, 21 61.58 ± 10.67 – before vs. after treatment (operation) 30 9, 21 61.58 ± 10.67 –
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
Li et al. 2014 China serum diagnosis NSCLC, SCLC – 23 12, 11 63.0 ± 9.8 / 59.4 ± 5.8 – healthy 23 11, 12 51.0 ± 11.1 / 56.3 ± 14.3 –
Lam et al. 2014 China pleural effusion diagnosis NSCLC, SCLC, anaplastic carcinoma – 32 13, 19 72.8 ± 11.4 smoker, non-smoker pulmonary tuberculosis 18 10, 8 59.7 ± 25.2 smoker, non-smoker
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – before vs. after treatment (operation) 30 – 61.58 ± 10.67 –
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
Callejon-Leblic et al. 2016 Spain bronchoalveolar lavage fluid diagnosis lung cancer – 24 16, 8 66 ± 11 – noncancerous lung diseases 31 23, 8 56 ± 13 –
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 –
Callejon-Leblic et al. 2019 Spain bronchoalveolar lavage fluid diagnosis NSCLC, SCLC – 24 16, 8 65± 12 former, current noncancerous lung diseases 30 25, 5 55 ± 15 former, current, non-smoker
Callej?n-Leblic et al. 2019 Spain blood diagnosis NSCLC, SCLC II, III, IV 30 25, 5 67 ± 12 former, current, non-smoker healthy 30 14, 16 56 ± 14 former, non-smoker
Sun et al. 2019 China serum diagnosis lung cancer I, II, III, IV 31 21, 10 54.1 ± 9.9 smoker, non-smoker healthy 29 15, 14 52.1 ± 14.6 smoker, 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) –
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
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
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 –
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Chen et al. 2015a GC – – – –
Chen et al. 2015a GC – – – –
Wen et al. 2013 LC ESI – Q-TOF MS/MS
Li et al. 2014 LC – both Q-TOF MS/MS
Lam et al. 2014 LC ESI both TripleTOF MS/MS
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Callejon-Leblic et al. 2016 GC EI – ion trap –
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
Callejon-Leblic et al. 2019 GC EI – ion trap –
Callej?n-Leblic et al. 2019 DI ESI negative Q-TOF MS/MS
Sun et al. 2019 GC – – – –
Qi et al. 2021 LC ESI both Q-Orbitrap MS/MS
Zheng et al. 2021 GC EI – quadrupole –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
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
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
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
Chen et al. 2015a GC/MSD ChemStation software (Agilent Technologies) NIST
Chen et al. 2015a GC/MSD ChemStation software (Agilent Technologies) NIST
Wen et al. 2013 MassHunter, Mass Profiler Professional software (Agilent) NIST 08, HMDB, METLIN, LIPID MAPS
Li et al. 2014 MarkerLynx METLIN, HMDB, KEGG
Lam et al. 2014 PeakView, LipidView (AB SCIEX), XCMS HMDB
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Callejon-Leblic et al. 2016 XCMS NIST Mass Spectral Library
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Callej?n-Leblic et al. 2019 – HMDB, Metlin
Sun et al. 2019 – BinBase, KEGG
Qi et al. 2021 ProteoWizard, XCMS, Xcalibur, CAMERA mzCloud, ChemSpider, LipidBlast and Fiehn HILIC
Zheng et al. 2021 MassHunter Workstation software, Mass Profiler Professional software NIST14, HMDB, Golm Metabolome Database
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and 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 12571.4545454545 11845.1818181818 1.06 0.32 – –
Miyamoto et al. 2015 Analysis of Covariance 15337.9444444444 9391.95 1.63 0.13 – –
Mazzone et al. 2016 two- sample independent t test 1.292579± 0.6680247 1.065943± 0.7456325 1.21 0.01 0.04 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 2557 ± 1453 2335 ± 1796 1.10 0.28 0.57 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 3.8226 ± 2.6189 4.8695 ± 5.9457 0.79 0.79 0.85 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 3836 ± 2605 2283 ± 1541 1.68 0.02 0.21 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 3919 ± 2555 2543 ± 1869 1.54 0.06 0.40 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 4121 ± 2227 4224 ± 2850 0.98 0.65 0.89 –
Chen et al. 2015a independent t-test 605.66 ± 361.44 244.99 ± 131.32 2.47 1.00e-03 – –
Chen et al. 2015a independent t-test 605.66 ± 361.44 346.58 ± 164.66 1.75 1.00e-03 – –
Wen et al. 2013 Mann–Whitney–Wilcoxon test, OPLS-DA – – 15.67 2.45e-10 – 1.28
Li et al. 2014 PCA, PLS-DA, OSC-PLS-DA, student’s t-test – – – 0.05 – 7.40
Lam et al. 2014 t-test, OPLS-DA – – – 8.23e-08 – –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 2.46 1.00e-03 – 1.72
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.57 1.00e-03 – 1.29
Wikoff et al. 2015b OPLS-DA – – 1.10 – 0.66 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.78 0.02 – 1.45
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.39 2.87e-05 5.75e-05 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.03 0.69 0.73 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 0.78 0.02 – 1.45
Callej?n-Leblic et al. 2019 PCA, PLS-DA, one-way ANOVA – – 1.68 0.01 – 1.48
Sun et al. 2019 Student t test, PLS-DA – – 2.20 8.95e-06 1.51e-04 0.73
Qi et al. 2021 PCA, OPLS-DA, Student’s t test – – 0.86 0.02 – 1.82
Zheng et al. 2021 Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA – – 0.91 1.53e-14 3.95e-14 1.07
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 0.01 – –
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 – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Chen et al. 2015a ROC curve analysis 402.22 0.749 (0.614-0.884) 70 86.67 –
Chen et al. 2015a ROC curve analysis 489.02 0.694 (0.550–0.838) 60 80 –
Wen et al. 2013 ROC curve analysis – 0.98 – – –
Li et al. 2014 ROC curve analysis – – – – –
Lam et al. 2014 ROC curve analysis – 0.866–0.996 84.4 100 –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.54 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.54 – – –
Callej?n-Leblic et al. 2019 ROC curve – 0.64 – – –
Sun et al. 2019 ROC curve analysis – – – – –
Qi et al. 2021 – – – – – –
Zheng et al. 2021 ROC analysis – 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) – – –
Kowalczyk et al. 2021 – – – – – –