Showing information for HMDB0000806 ('C14', 'myristic acid', 'tetradecanoic acid', 'myristate')

Metabolite information
HMDB ID HMDB0000806
Synonyms
1-Tetradecanecarboxylate
1-Tetradecanecarboxylic acid
1-Tridecanecarboxylate
1-Tridecanecarboxylic acid
14
14:0
14:00
Acid, myristic
Acid, tetradecanoic
Acide tetradecanoique
C14
CH3-[CH2]12-COOH
Crodacid
Myristate
Myristic acid pure
Myristinsaeure
Myristoate
Myristoic acid
N-Tetradecan-1-Oate
N-Tetradecan-1-Oic acid
N-Tetradecanoate
N-Tetradecanoic acid
N-Tetradecoate
N-Tetradecoic acid
Tetradecanoate
Tetradecanoic (myristic) acid
Tetradecanoic acid
Tetradecoate
Tetradecoic acid
Chemical formula C14H28O2
IUPAC name
tetradecanoic acid
CAS registry number 544-63-8
Monoisotopic molecular weight 228.20893014

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 HMDB0000806 as a lung cancer biomarker

The studies that identify HMDB0000806 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 –
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 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 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 –
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 –
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 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 I, II 11 – – – 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
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
Huang et al. 2016 China dried blood spot diagnosis lung cancer – 222 94, 128 median: 57.47 (27-81) – healthy 96 30, 66 median: 56.07 (32-80) –
Huang et al. 2016 China dried blood spot diagnosis benign lung disease – 118 55, 63 median: 59.61 (32-80) – healthy 96 30, 66 median: 56.07 (32-80) –
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 –
Callejon-Leblic et al. 2019 Spain serum diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, non-smoker
Mu et al. 2019 China serum diagnosis NSCLC I, II, III, IV 30 0, 30 60.4 ± 9.7 non-smoker healthy 30 0, 30 54.7 ± 14.3 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) –
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
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 –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Huang et al. 2016 LC ESI positive QTrap MS/MS
Huang et al. 2016 LC ESI positive QTrap 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
Callejon-Leblic et al. 2019 GC EI – ion trap –
Mu et al. 2019 GC – – – –
Qi et al. 2021 LC ESI both Q-Orbitrap 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
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
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)
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Huang et al. 2016 Analyst software, ChemoView software –
Huang et al. 2016 Analyst software, ChemoView software –
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Mu et al. 2019 – –
Qi et al. 2021 ProteoWizard, XCMS, Xcalibur, CAMERA mzCloud, ChemSpider, LipidBlast and Fiehn HILIC
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 4617.45454545455 3964.81818181818 1.16 0.90 – –
Miyamoto et al. 2015 Analysis of Covariance 4762.38888888889 3867.2 1.23 0.23 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.11147 ± 0.051075 0.11417 ± 0.054058 0.98 0.76 0.85 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.69566 ± 0.45751 0.89545 ± 1.0872 0.78 0.29 0.63 –
Mazzone et al. 2016 two- sample independent t test 1.178252± 0.5712113 1.107921± 0.8465139 1.06 0.47 0.55 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1063 ± 548 1005 ± 287 1.06 0.87 0.95 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1162 ± 532 977 ± 393 1.19 0.23 0.60 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1239 ± 535 1070 ± 403 1.16 0.26 0.59 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 681 ± 266 632 ± 191 1.08 0.36 0.63 –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.89 0.34 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.88 0.09 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.86 0.18 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.69 0.02 – –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.29 1.00e-03 – 1.21
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.71 1.00e-03 – 1.30
Wikoff et al. 2015b OPLS-DA – – 1.10 – 0.08 –
Huang et al. 2016 PLS-DA, ANOVA, student’s t-test – – – 1.00e-03 – –
Huang et al. 2016 PLS-DA, ANOVA, student’s t-test – – – 0.02 – –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.42 7.20e-04 1.86e-03 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.36 4.37e-05 8.42e-05 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 0.50 0.04 – 2.42
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.77 1.00e-03 0.02 1.03
Qi et al. 2021 PCA, OPLS-DA, Student’s t test – – 1.53 0.01 – 1.85
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 – – – – –
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 – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Huang et al. 2016 – – – – – –
Huang et al. 2016 – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.73 – – –
Mu et al. 2019 – – – – – –
Qi et al. 2021 – – – – – –