Showing information for HMDB0000050 ('adenosine')

Metabolite information
HMDB ID HMDB0000050
Synonyms
1-[6-amino-9H-Purin-9-yl]-1-deoxy-beta-D-ribofuranose
1-[6-amino-9H-Purin-9-yl]-1-deoxy-beta-delta-ribofuranose
6-amino-9-b-D-Ribofuranosyl-9H-purine
6-amino-9-beta-D-Ribofuranosyl-9H-purine
6-amino-9-β-D-ribofuranosyl-9H-purine
6-amino-9beta-D-Ribofuranosyl-9H-purine
6-amino-9beta-delta-Ribofuranosyl-9H-purine
9-b-D-Ribofuranosidoadenine
9-b-D-Ribofuranosyl-9H-purin-6-amine
9-beta-D-Arabinofuranosyladenine
9-beta-D-Ribofuranosidoadenine
9-beta-D-Ribofuranosyl-9H-purin-6-amine
9-beta-D-Ribofuranosyladenine
9-beta-delta-Arabinofuranosyladenine
9-beta-delta-Ribofuranosidoadenine
9-beta-delta-Ribofuranosyl-9H-purin-6-amine
9-beta-delta-Ribofuranosyladenine
9-β-D-ribofuranosidoadenine
9-β-D-ribofuranosyl-9H-purin-6-amine
9beta-D-Ribofuranosyl-9H-purin-6-amine
9beta-D-Ribofuranosyladenine
9beta-delta-Ribofuranosyl-9H-purin-6-amine
9beta-delta-Ribofuranosyladenine
Ade-rib
Adenine deoxyribonucleoside
Adenine nucleoside
Adenine riboside
Adenine-9beta-D-ribofuranoside
Adenine-9beta-delta-ribofuranoside
Adenocard
Adenocor
Adenoscan
Adenosin
Adenyldeoxyriboside
Ado
Beautification product
Boniton
Csf
Cucurbits
Deoxyadenosine
Desoxyadenosine
Digestion
Endotoxic shock
Extracellular region
Faecal
Faeces
Fauna
Fecal
Flora
Gourds
Gramineae
Legume
Myocol
Nucleocardyl
Papilionoideae
Pcp
Personal hygiene
Sandesin
Soy
Soya
Soya bean
Soybean
Stool
Toiletries
Toiletry
[2R,3R,4S,5R]-2-[6-Aminopurin-9-yl]-5-[hydroxymethyl]oxolane-3,4-diol
b-D-Adenosine
beta-Adenosine
beta-D-Adenosine
beta-delta-Adenosine
β-D-adenosine
Chemical formula C10H13N5O4
IUPAC name
(2R,3R,4S,5R)-2-(6-amino-9H-purin-9-yl)-5-(hydroxymethyl)oxolane-3,4-diol
CAS registry number 58-61-7
Monisotopic molecular weight 267.096753929

Chemical taxonomy
Super class Nucleosides, nucleotides, and analogues
Class Purine nucleosides
Sub class –

Biological properties
Pahtways
AICA-Ribosiduria
Adenine phosphoribosyltransferase deficiency [APRT]
Adenosine Deaminase Deficiency
Adenylosuccinate Lyase Deficiency
Azathioprine Action Pathway
Betaine Metabolism
Cystathionine Beta-Synthase Deficiency
Glycine N-methyltransferase Deficiency
Gout or Kelley-Seegmiller Syndrome
Homocystinuria-megaloblastic anemia due to defect in cobalamin metabolism, cblG complementation type
Hypermethioninemia
Intracellular Signalling Through Adenosine Receptor A2a and Adenosine
Intracellular Signalling Through Adenosine Receptor A2b and Adenosine
Lesch-Nyhan Syndrome [LNS]
Mercaptopurine Action Pathway
Methionine Adenosyltransferase Deficiency
Methionine Metabolism
Methylenetetrahydrofolate Reductase Deficiency [MTHFRD]
Mitochondrial DNA depletion syndrome
Molybdenum Cofactor Deficiency
Myoadenylate deaminase deficiency
Purine Metabolism
Purine Nucleoside Phosphorylase Deficiency
S-Adenosylhomocysteine [SAH] Hydrolase Deficiency
Selenoamino Acid Metabolism
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 HMDB0000050 ('adenosine')

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
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 –
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
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 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 – 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
Mazzone et al. 2016 LC ESI positive linear ion-trap MS/MS
Hori et al. 2011 GC – – – –
Callejón-Leblic et al. 2019 DI ESI positive Q-TOF MS/MS
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: 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 –
Reference Data processing software Database search
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Callejón-Leblic et al. 2019 – HMDB, Metlin
Moreno et al. 2018 – KEGG, HMDB
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
Mazzone et al. 2016 two- sample independent t test 0.9329021± 1.946977 0.87674± 1.318514 1.06405787348587 0.7743854 0.738604005 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.69 0.037 – –
Callejón-Leblic et al. 2019 PCA, PLS-DA, one-way ANOVA – – 2.86 0.026 – 1.81
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.34839667890611 0.000350832229582916 0.000980989482719109 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.45731794841743 0.000226576970301796 0.000396509698028144 –
Wikoff et al. 2015b OPLS-DA – – 1.3 – 0.057 –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Mazzone et al. 2016 – – – – – –
Hori et al. 2011 – – – – – –
Callejón-Leblic et al. 2019 ROC curve – 0.65 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Wikoff et al. 2015b – – – – – –