Metabolite page

Showing information for HMDB0000207 ('oleate', 'oleate (18:1n9)', 'oleic acid')

Metabolite information
HMDB ID	HMDB0000207
Synonyms	18:1 N-9 18:1DElta9cis 9 Octadecenoic acid 9,10-Octadecenoate 9,10-Octadecenoic acid 9-Octadecenoate 9-Octadecenoic acid 9-[Z]-Octadecenoate 9-[Z]-Octadecenoic acid Acid, 9-octadecenoic Acid, cis-9-octadecenoic Acid, oleic Adipose Beautification product Body fat C18:1 N-9 Cellular membrane Century CD fatty acid Coffee Coffee bean Csf Cucurbits Cytoplasma Digestion 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 Extracellular region Faecal Faeces Fat tissue Fauna Fecal Flora Gdm Gestational diabetes mellitus Glycon ro Glycon wo Gourds Gramineae Industrene 104 Industrene 105 Industrene 205 Industrene 206 L'acide oleique Legume Lipid body Lipid droplet Lipid metabolic process Lipid particle Membrane integrity agent Membrane stability agent 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 Papilionoideae Pcp Personal hygiene Priolene 6900 Prostate gland Red oil Signal transduction Soy Soya Soya bean Soybean Stool Striated muscle Surface-active agent Toiletries Toiletry Vopcolene 27 Wecoline oo Z-9-Octadecenoate Z-9-Octadecenoic acid [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 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
Monisotopic molecular weight	282.255880332
Chemical taxonomy
Super class	Lipids and lipid-like molecules
Class	Fatty Acyls
Sub class	Fatty acids and conjugates
Biological properties
Pahtways
Author-emphasized biomarker in the paper(s)	Chen et al. 2015 Chen et al. 2015

Lung cancer metabolomics studies that identify HMDB0000207 ('oleate', 'oleate (18:1n9)', 'oleic acid')

Reference	Country	Specimen	Marker function	Participants (Case)						Participants (Control)
Reference	Country	Specimen	Marker function	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
Chen et al. 2015	–	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. 2015	–	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	–
Li et al. 2014	–	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	–
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	–	30	–	61.58 ± 10.67	–	before vs. after treatment (operation)	30	–	61.58 ± 10.67	–
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	–
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	–
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	–
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
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	–
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	–	plasma	diagnosis	adenocarcinoma	I, II, III, IV	52	17, 35	65.9 ± 9.66	–	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	–
Callejon-Leblic et al. 2019	–	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
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	–
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	–
Callejón-Leblic et al. 2019	–	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
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	–
Sun et al. 2019	–	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
Lam et al. 2014	–	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
Wen et al. 2013	–	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
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

Reference	Chromatography	Ion source	Positive/Negative mode	Mass analyzer	Identification level
Chen et al. 2015	GC	–	–	–	–
Chen et al. 2015	GC	–	–	–	–
Li et al. 2014	LC	–	positive, negative	Q-TOF	MS/MS
Chen et al. 2015	GC	EI	–	quadrupole	–
Chen et al. 2015	GC	EI	–	quadrupole	–
Roś-Mazurczyk et al. 2017	GC	–	–	TOF	In-source fragmentation
Moreno et al. 2018	LC, GC	ESI, EI	positive, negative	LC: linear ion‐trap, GC: single‐quadrupole	LC: MS/MS
Fahrmann et al. 2015	GC	EI	–	TOF	–
Miyamoto et al. 2015	GC	EI	–	TOF	MS/MS
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	GC	EI	–	ion trap	–
Callejon-Leblic et al. 2019	GC	EI	–	ion trap	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Mazzone et al. 2016	GC	EI	–	quadrupole	MS/MS
Callejón-Leblic et al. 2019	DI	ESI	negative	Q-TOF	MS/MS
Moreno et al. 2018	LC, GC	ESI, EI	positive, negative	LC: linear ion‐trap, GC: single‐quadrupole	LC: MS/MS
Sun et al. 2019	GC	–	–	–	–
Lam et al. 2014	LC	ESI	both	TripleTOF	MS/MS
Wen et al. 2013	LC	ESI	–	Q-TOF	MS/MS
Wikoff et al. 2015b	GC	EI	–	TOF	–

Reference	Data processing software	Database search
Chen et al. 2015	GC/MSD ChemStation software (Agilent Technologies)	NIST
Chen et al. 2015	GC/MSD ChemStation software (Agilent Technologies)	NIST
Li et al. 2014	MarkerLynx	METLIN, HMDB, KEGG
Chen et al. 2015	ChemStation	NIST
Chen et al. 2015	ChemStation	NIST
Roś-Mazurczyk et al. 2017	Leco ChromaTOF-GC	Replib, Mainlib and Fiehn libraries
Moreno et al. 2018	–	KEGG, HMDB
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
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	XCMS	NIST Mass Spectral Library
Callejon-Leblic et al. 2019	XCMS	NIST Mass Spectral Library
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Mazzone et al. 2016	Metabolon LIMS system	Metabolon LIMS system
Callejón-Leblic et al. 2019	–	HMDB, Metlin
Moreno et al. 2018	–	KEGG, HMDB
Sun et al. 2019	–	BinBase, KEGG
Lam et al. 2014	PeakView, LipidView (AB SCIEX), XCMS	HMDB
Wen et al. 2013	MassHunter, Mass Profiler Professional software (Agilent)	NIST 08, HMDB, METLIN, LIPID MAPS
Wikoff et al. 2015b	BinBase	NIST11, BinBase

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Chen et al. 2015	independent t-test	605.66 ± 361.44	244.99 ± 131.32	–	<0.001	–	–
Chen et al. 2015	independent t-test	605.66 ± 361.44	346.58 ± 164.66	–	<0.001	–	–
Li et al. 2014	PCA, PLS-DA, OSC-PLS-DA, student’s t-test	–	–	–	< 0.05	–	7.4
Chen et al. 2015	PCA, PLS-DA, independent t test	–	–	2.46228882668983	<0.001	–	1.72
Chen et al. 2015	PCA, PLS-DA, independent t test	–	–	1.75321144263207	<0.001	–	1.29
Roś-Mazurczyk et al. 2017	two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach	3.8226 ± 2.6189	4.8695 ± 5.9457	0.785008727795462	0.79322	0.85012	–
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	1.02863728236301	0.694134431432686	0.728333730939441	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	4121 ± 2227	4224 ± 2850	0.976	0.649	0.892	–
Miyamoto et al. 2015	Analysis of Covariance	12571.4545454545	11845.1818181818	1.06131376777669	0.319754538300005	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	2557 ± 1453	2335 ± 1796	1.1	0.277	0.573	–
Miyamoto et al. 2015	Analysis of Covariance	15337.9444444444	9391.95	1.63309477205952	0.126596215928545	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	3919 ± 2555	2543 ± 1869	1.54	0.063	0.395	–
Callejon-Leblic et al. 2016	PLS-LDA, one-way ANOVA	–	–	0.78	0.023	–	1.45
Callejon-Leblic et al. 2019	PLS-LDA, one-way ANOVA	–	–	0.78	0.023	–	1.45
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	0.208	–
Mazzone et al. 2016	two- sample independent t test	1.292579± 0.6680247	1.065943± 0.7456325	1.21261549632579	0.0131417	0.040458542	–
Callejón-Leblic et al. 2019	PCA, PLS-DA, one-way ANOVA	–	–	1.68	0.012	–	1.48
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	1.39276048080439	0.0000287262750385473	0.0000574525500770946	–
Sun et al. 2019	Student t test, PLS-DA	–	–	2.20331077465311	0.00000895	0.000151	0.73145
Lam et al. 2014	t-test, OPLS-DA	–	–	–	0.0000000823	–	–
Wen et al. 2013	Mann–Whitney–Wilcoxon test, OPLS-DA	–	–	15.6707247613908	0.000000000245	–	1.28
Wikoff et al. 2015b	OPLS-DA	–	–	1.1	–	0.663	–

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Chen et al. 2015	ROC curve analysis	402.22	0.749 (0.614-0.884)	70	86.67	–
Chen et al. 2015	ROC curve analysis	489.02	0.694 (0.550–0.838)	60	80	–
Li et al. 2014	ROC curve analysis	–	–	–	–	–
Chen et al. 2015	–	–	–	–	–	–
Chen et al. 2015	–	–	–	–	–	–
Roś-Mazurczyk et al. 2017	ROC curve	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Miyamoto et al. 2015	–	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Miyamoto et al. 2015	–	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Callejon-Leblic et al. 2016	ROC curve analysis	–	0.54	–	–	–
Callejon-Leblic et al. 2019	ROC curve analysis	–	0.54	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Mazzone et al. 2016	–	–	–	–	–	–
Callejón-Leblic et al. 2019	ROC curve	–	0.64	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Sun et al. 2019	ROC curve analysis	–	–	–	–	–
Lam et al. 2014	ROC curve analysis	–	0.866–0.996	84.4	100	–
Wen et al. 2013	ROC curve analysis	–	0.98	–	–	–
Wikoff et al. 2015b	–	–	–	–	–	–

Showing information for HMDB0000207 ('oleate', 'oleate (18:1n9)', 'oleic acid')

Metabolite information

18:1 N-9

18:1DElta9cis

9 Octadecenoic acid

9,10-Octadecenoate

9,10-Octadecenoic acid

9-Octadecenoate

9-Octadecenoic acid

9-[Z]-Octadecenoate

9-[Z]-Octadecenoic acid

Acid, 9-octadecenoic

Acid, cis-9-octadecenoic

Acid, oleic

Adipose

Beautification product

Body fat

C18:1 N-9

Cellular membrane

Century CD fatty acid

Coffee

Coffee bean

Csf

Cucurbits

Cytoplasma

Digestion

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

Extracellular region

Faecal

Faeces

Fat tissue

Fauna

Fecal

Flora

Gdm

Gestational diabetes mellitus

Glycon ro

Glycon wo

Gourds

Gramineae

Industrene 104

Industrene 105

Industrene 205

Industrene 206

L'acide oleique

Legume

Lipid body

Lipid droplet

Lipid metabolic process

Lipid particle

Membrane integrity agent

Membrane stability agent

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

Papilionoideae

Pcp

Personal hygiene