Showing information for HMDB0000294 ('urea')

Metabolite information
HMDB ID HMDB0000294
Synonyms
ARF
Alphadrate
Basodexan
Bromisovalum
Bubber shet
Calmurid
Calmurid HC
Carbaderm
Carbamide
Carbamide resin
Carbonyl diamide
Carbonyl diamine
Carbonyldiamide
Carbonyldiamine
Carmol
H2NC(O)NH2
Harnstoff
Helicosol
Hyanit
Isourea
Karbamid
Keratinamin
Keratinamin kowa
Mocovina
Onychomal
Panafil
URE
Ureaphil
Uree
Ureophil
b-I-K
beta-I-K
e927b
ur
Chemical formula CH4N2O
IUPAC name
urea
CAS registry number 57-13-6
Monoisotopic molecular weight 60.03236276

Chemical taxonomy
Super class Organic acids and derivatives
Class Organic carbonic acids and derivatives
Sub class Ureas

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000294 as a lung cancer biomarker


The studies that identify HMDB0000294 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 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
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
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 52 17, 35 65.9 ± 9.66 – 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 –
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 serum 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 –
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
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
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – before vs. after treatment (operation) 30 – 61.58 ± 10.67 –
Chen et al. 2015b China serum – lung cancer (postoperative) – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
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 urine diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 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
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 –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
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 –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF 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 –
Sun et al. 2019 GC – – – –
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
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
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
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
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Sun et al. 2019 – BinBase, KEGG
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 620351.333333333 630235.1 0.98 0.80 – –
Miyamoto et al. 2015 Analysis of Covariance 666717.727272727 584389 1.14 0.22 – –
Mazzone et al. 2016 two- sample independent t test 1.05628± 0.4647057 1.043567± 0.3709509 1.01 0.80 0.75 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 157412 ± 102363 196899 ± 84339 0.80 0.02 0.22 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 184523 ± 42921 179532 ± 40525 1.03 0.61 0.81 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 200181 ± 90481 162301 ± 91842 1.23 0.34 0.65 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 227827 ± 68929 252052 ± 81218 0.90 0.37 0.67 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 574.66 ± 560.22 486.81 ± 397.39 1.18 0.24 0.58 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.02 0.95 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.00 0.92 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.99 0.55 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.98 0.37 – –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.57 1.00e-03 – 1.14
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.31 1.00e-03 – 1.03
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.25 1.00e-02 – 1.07
Wikoff et al. 2015b OPLS-DA – – 1.00 – 0.61 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.74 0.05 – 1.30
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.21 1.90e-06 4.62e-06 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.15 0.06 0.09 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 8.84 0.01 – 1.85
Sun et al. 2019 Student t test, PLS-DA – – 1.53 2.76e-04 2.60e-03 0.16
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 – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – combination of nine metabolites: 100 combination of nine metabolites: 86 –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Chen et al. 2015b – – – – – –
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 – 0.7 – – –
Sun et al. 2019 ROC curve analysis – – – – –