Showing information for HMDB0000294 ('urea')

Metabolite information
HMDB ID HMDB0000294
Synonyms
ARF
Alphadrate
Basodexan
Beautification product
Bromisovalum
Bubber shet
Calmurid
Calmurid HC
Carbaderm
Carbamide
Carbamide resin
Carbonyl diamide
Carbonyl diamine
Carbonyldiamide
Carbonyldiamine
Carmol
Csf
Cucurbits
Cytoplasma
Digestion
Extracellular region
Faecal
Faeces
Familial protein intolerance
Fauna
Fecal
Flora
Gourds
Gramineae
H2NC[O]NH2
Harnstoff
Helicosol
Hyanit
Hyperdibasic aminoaciduria type 2
Isourea
Karbamid
Keratinamin
Keratinamin kowa
Kidneys
Legume
Liver cirrhosis
Lpi
Mocovina
Onychomal
Panafil
Papilionoideae
Pcp
Personal hygiene
Prostate gland
Soy
Soya
Soya bean
Soybean
Stool
Tb meningitis
Toiletries
Toiletry
Tubercular meningitis
Tuberculosis meningitis
URE
Ureaphil
Uree
Ureophil
b-I-K
beta-I-K
e927b
ur
Chemical formula CH4N2O
IUPAC name
urea
CAS registry number 57-13-6
Monisotopic molecular weight 60.03236276

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

Biological properties
Pahtways
Amiloride Action Pathway
Arginine and Proline Metabolism
Arginine: Glycine Amidinotransferase Deficiency [AGAT Deficiency]
Argininemia
Argininosuccinic Aciduria
Bendroflumethiazide Action Pathway
Blue diaper syndrome
Bumetanide Action Pathway
Carbamoyl Phosphate Synthetase Deficiency
Chlorothiazide Action Pathway
Chlorthalidone Action Pathway
Citrullinemia Type I
Creatine deficiency, guanidinoacetate methyltransferase deficiency
Cyclothiazide Action Pathway
Cystinuria
D-Arginine and D-Ornithine Metabolism
Eplerenone Action Pathway
Ethacrynic Acid Action Pathway
Furosemide Action Pathway
Glucose Transporter Defect [SGLT2]
Guanidinoacetate Methyltransferase Deficiency [GAMT Deficiency]
Hartnup Disorder
Hydrochlorothiazide Action Pathway
Hydroflumethiazide Action Pathway
Hyperornithinemia with gyrate atrophy [HOGA]
Hyperornithinemia-hyperammonemia-homocitrullinuria [HHH-syndrome]
Hyperprolinemia Type I
Hyperprolinemia Type II
Iminoglycinuria
Indapamide Action Pathway
Kidney Function
L-arginine:glycine amidinotransferase deficiency
Lysinuric Protein Intolerance
Lysinuric protein intolerance [LPI]
Methyclothiazide Action Pathway
Metolazone Action Pathway
Ornithine Aminotransferase Deficiency [OAT Deficiency]
Ornithine Transcarbamylase Deficiency [OTC Deficiency]
Polythiazide Action Pathway
Prolidase Deficiency [PD]
Prolinemia Type II
Quinethazone Action Pathway
Spironolactone Action Pathway
Torsemide Action Pathway
Triamterene Action Pathway
Trichlormethiazide Action Pathway
Urea Cycle
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000294 ('urea')

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
Chen et al. 2015 – serum – lung cancer (postoperative) – 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 –
Hori et al. 2011 – 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 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC III, IV 22 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
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 –
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 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Hori et al. 2011 – 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
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 –
Hori et al. 2011 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II 11 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
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 –
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 –
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
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 –
Callejon-Leblic et al. 2016 – bronchoalveolar lavage fluid diagnosis lung cancer – 24 16, 8 66 ± 11 – noncancerous lung diseases 31 23, 8 56 ± 13 –
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. 2019 – urine diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, non-smoker
Chen et al. 2015 – serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
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
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 –
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 EI – quadrupole –
Chen et al. 2015 GC EI – quadrupole –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Fahrmann et al. 2015 GC EI – TOF –
Roś-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Miyamoto et al. 2015 GC EI – TOF MS/MS
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Callejon-Leblic et al. 2019 GC EI – ion trap –
Chen et al. 2015 GC EI – quadrupole –
Sun et al. 2019 GC – – – –
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Wikoff et al. 2015b GC EI – TOF –
Reference Data processing software Database search
Chen et al. 2015 ChemStation NIST
Chen et al. 2015 ChemStation NIST
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)
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Miyamoto et al. 2015 ChromaTOF software (Leco) 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)
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)
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Roś-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Chen et al. 2015 ChemStation NIST
Sun et al. 2019 – BinBase, KEGG
Moreno et al. 2018 – KEGG, HMDB
Wikoff et al. 2015b BinBase NIST11, BinBase
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Chen et al. 2015 PCA, PLS-DA, independent t test – – 1.31039340385836 <0.001 – 1.03
Chen et al. 2015 PCA, PLS-DA, independent t test – – 0.637280313659631 <0.001 – 1.14
Hori et al. 2011 student’s t-test, PLS-DA – – 1.02 0.951 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 1 0.916 – –
Mazzone et al. 2016 two- sample independent t test 1.05628± 0.4647057 1.043567± 0.3709509 1.01218225566734 0.8030662 0.751368269 –
Miyamoto et al. 2015 Analysis of Covariance 620351.333333333 630235.1 0.984317333854197 0.802285160952632 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 184523 ± 42921 179532 ± 40525 1.03 0.606 0.813 –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.99 0.546 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 227827 ± 68929 252052 ± 81218 0.904 0.369 0.674 –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.98 0.366 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 200181 ± 90481 162301 ± 91842 1.23 0.337 0.652 –
Roś-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 574.66 ± 560.22 486.81 ± 397.39 1.18046054929028 0.24283 0.57621 –
Miyamoto et al. 2015 Analysis of Covariance 666717.727272727 584389 1.14088000847505 0.224605689884092 – –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.15433060512295 0.0604991483587237 0.0919000904134245 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.74 0.048 – 1.3
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 157412 ± 102363 196899 ± 84339 0.8 0.02 0.222 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 8.84 0.011 – 1.85
Chen et al. 2015 PCA, PLS-DA, independent t test – – 1.24833054890161 0.01 – 1.07
Sun et al. 2019 Student t test, PLS-DA – – 1.52994310132377 0.000276 0.002596 0.15879
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.20606025136478 0.00000190081271884074 0.00000462453826108204 –
Wikoff et al. 2015b OPLS-DA – – 1 – 0.615 –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Chen et al. 2015 – – – – – –
Chen et al. 2015 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Mazzone et al. 2016 – – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Roś-Mazurczyk et al. 2017 ROC curve – – combination of nine metabolites: 100 combination of nine metabolites: 86 –
Miyamoto et al. 2015 – – – – – –
Moreno et al. 2018 – – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.54 – – –
Fahrmann et al. 2015 random forest – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.7 – – –
Chen et al. 2015 – – – – – –
Sun et al. 2019 ROC curve analysis – – – – –
Moreno et al. 2018 – – – – – –
Wikoff et al. 2015b – – – – – –