Showing information for HMDB0000292 ('xanthine')

Metabolite information
HMDB ID HMDB0000292
Synonyms
1H-Purine-2,6-diol
2,6-Dihydroxypurine
2,6-Dioxopurine
2,6-dioxo-1,2,3,6-Tetrahydropurine
2,6[1,3]-Purinedion
3,7-Dihydropurine-2,6-dione
3,7-dihydro-1H-Purine-2,6-dione
9H-Purine-2,6-[1H,3H]-dione
9H-Purine-2,6-diol
9H-Purine-2,6[1H,3H]-dione
Bladder calculi
Bladder stones
Coffee
Coffee bean
Csf
Cucurbits
Cutaneous [related but nor necessarily exact synonym]
Cystoliths
Cytoplasma
Ddd
Digestion
Dioxopurine
Eskf
Esrf
Faecal
Faeces
Fauna
Fecal
Flora
Gourds
Gramineae
Hypoxanthin guanine phosphoribosyl transferase
Isoxanthine
Kelley-seegmiller syndrome
Kidney failure
Kidneys
Legume
Lns
Nyhan's syndrome
Papilionoideae
Peroxisomal
Peroxisome vesicle
Prostate gland
Pseudoxanthine
Purine-2,6-diol
Purine-2,6[1H,3H]-dione
Purine-2[3H],6[1H]-dione
Skin contact
Soy
Soya
Soya bean
Soybean
Stool
Striated muscle
Testes
Testis
Topical
Transdermal
Urinary bladder calculi
Xan
Xanthic oxide
Xanthin
Xanthine dehydrogenase deficiency
Xanthine oxidase deficiency
Xanthinuria
Xdh deficiency
Chemical formula C5H4N4O2
IUPAC name
2,3,6,7-tetrahydro-1H-purine-2,6-dione
CAS registry number 69-89-6
Monisotopic molecular weight 152.033425392

Chemical taxonomy
Super class Organoheterocyclic compounds
Class Imidazopyrimidines
Sub class Purines and purine derivatives

Biological properties
Pahtways
AICA-Ribosiduria
Adenine phosphoribosyltransferase deficiency [APRT]
Adenosine Deaminase Deficiency
Adenylosuccinate Lyase Deficiency
Azathioprine Action Pathway
Gout or Kelley-Seegmiller Syndrome
Lesch-Nyhan Syndrome [LNS]
Mercaptopurine Action Pathway
Mitochondrial DNA depletion syndrome
Molybdenum Cofactor Deficiency
Myoadenylate deaminase deficiency
Purine Metabolism
Purine Nucleoside Phosphorylase Deficiency
Thioguanine Action Pathway
Xanthine Dehydrogenase Deficiency [Xanthinuria]
Xanthinuria type I
Xanthinuria type II
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000292 ('xanthine')

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
Mu et al. 2019 – 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
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 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 52 17, 35 65.9 ± 9.66 – healthy 31 11, 20 64.1 ± 8.97 –
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 –
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
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 –
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 –
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 –
Yue et al. 2018 – plasma diagnosis SCLC – 20 – – – healthy 20 – – –
Huang et al. 2019 – plasma diagnosis lung cancer – 31 19, 12 28-64 – healthy 35 24, 11 23-60 –
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
Chen et al. 2018 – serum diagnosis NSCLC I, II 90 40, 50 58.1 ± 9.0 – healthy 90 42, 48 53.0 ± 11.8 –
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Mu et al. 2019 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 –
Miyamoto et al. 2015 GC EI – TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Callejón-Leblic et al. 2019 DI ESI negative Q-TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Yue et al. 2018 LC ESI positive, negative QTRAP MS/MS
Huang et al. 2019 LC ESI negative Q-Orbitrap MS/MS
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 –
Chen et al. 2018 LC ESI negative Q-TOF MS/MS
Reference Data processing software Database search
Mu et al. 2019 – –
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
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Callejón-Leblic et al. 2019 – HMDB, Metlin
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Moreno et al. 2018 – KEGG, HMDB
Yue et al. 2018 Analyst, MultiQuant –
Huang et al. 2019 XCMS OSI-SMMS
Moreno et al. 2018 – KEGG, HMDB
Wikoff et al. 2015b BinBase NIST11, BinBase
Chen et al. 2018 Analyst TF, XCMS in-house
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.249 < 0.001 < 0.001 1.615
Mazzone et al. 2016 two- sample independent t test 1.094821± 0.5015046 1.083551± 1.0811354 1.01040098712474 0.9234785 0.804216432 –
Miyamoto et al. 2015 Analysis of Covariance 438.636363636364 541.818181818182 0.809563758389262 0.178638035221322 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 79 ± 37 69 ± 32 1.16 0.132 0.516 –
Miyamoto et al. 2015 Analysis of Covariance 571.722222222222 417.15 1.37054350287 0.108835510947556 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 57 ± 16 49 ± 12 1.17 0.024 0.127 –
Callejón-Leblic et al. 2019 PCA, PLS-DA, one-way ANOVA – – 1.86 0.022 – 1.83
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 145 ± 47 121 ± 39 1.2 0.009 0.09 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 169 ± 66 128 ± 43 1.32 0.005 0.117 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.50110111263995 0.00107060011419359 0.00270111178236198 –
Yue et al. 2018 OPLS-DA, student’s t-test 10.84±4.51 ng/mL 16.48±4.84 ng/mL 4 0.00031 – 1.33
Huang et al. 2019 OPLS-DA, Mann-Whitney U test – – 0.797313073 0.000000312694 – 1.894062845
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 2.70281561125291 0.00000000000299503337906127 0.0000000000287956221263963 –
Wikoff et al. 2015b OPLS-DA – – 2.7 – 0.00038 –
Chen et al. 2018 PCA, OPLS-DA – – 0.52 – – 1.29
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Mu et al. 2019 – – – – – –
Mazzone et al. 2016 – – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Callejón-Leblic et al. 2019 ROC curve – 0.52 – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Moreno et al. 2018 – – – – – –
Yue et al. 2018 – – – – – –
Huang et al. 2019 – – – – – –
Moreno et al. 2018 – – – – – –
Wikoff et al. 2015b – – – – – –
Chen et al. 2018 ROC curve – 0.264 (0.191–0.337) 77.8 60 –