Metabolite page

Showing information for HMDB0000034 ('adenine')

Metabolite information
HMDB ID	HMDB0000034
Synonyms	1,6-dihydro-6-Iminopurine 1H-Purin-6-amine 1H-Purine-6-amine 3,6-dihydro-6-Iminopurine 4, Vitamin b 6-Aminopurine 6-amino-1H-Purine 6-amino-3H-Purine 6-amino-7H-Purine 6-amino-9H-Purine 6-amino-Purine 9H-Purin-6-amine 9H-Purin-6-yl-amin 9H-Purin-6-ylamine 9H-Purine-6-amine A Ade Adenin Adeninimine Cell nucleus Coffee Coffee bean Csf Cucurbits Cytoplasma Digestion Extracellular region Faecal Faeces Fauna Fecal Flora Gourds Gramineae Legume Nucleic Papilionoideae Prostate gland Soy Soya Soya bean Soybean Stool Tb meningitis Tubercular meningitis Tuberculosis meningitis Vitamin b 4 Vitamin b4 b 4, Vitamin
Chemical formula	C5H5N5
IUPAC name	7H-purin-6-amine
CAS registry number	73-24-5
Monisotopic molecular weight	135.054495185
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)	Callejon-Leblic et al. 2016

Lung cancer metabolomics studies that identify HMDB0000034 ('adenine')

Reference	Country	Specimen	Marker function	Participants (Case)						Participants (Control)
Reference	Country	Specimen	Marker function	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
Callejon-Leblic et al. 2016	–	bronchoalveolar lavage fluid	diagnosis	lung cancer	–	24	16, 8	66 ± 11	–	noncancerous lung diseases	31	23, 8	56 ± 13	–
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	–
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	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
Callejon-Leblic et al. 2016	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
Callejón-Leblic et al. 2019	DI	ESI	negative	Q-TOF	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
Callejon-Leblic et al. 2016	Markerview	HMDB, METLIN
Moreno et al. 2018	–	KEGG, HMDB
Callejón-Leblic et al. 2019	–	HMDB, Metlin
Moreno et al. 2018	–	KEGG, HMDB
Wikoff et al. 2015b	BinBase	NIST11, BinBase

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Callejon-Leblic et al. 2016	PLS-LDA, one-way ANOVA	–	–	1.92	0.043	–	1.27
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	1.26241490438159	0.00638372169550007	0.0132191218128516	–
Callejón-Leblic et al. 2019	PCA, PLS-DA, one-way ANOVA	–	–	1.1	0.005	–	1.35
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	2.2643418554218	0.0000000188081500257296	0.0000000674558515407686	–
Wikoff et al. 2015b	OPLS-DA	–	–	1.5	–	0.00037	–

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Callejon-Leblic et al. 2016	ROC curve analysis	–	0.82	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Callejón-Leblic et al. 2019	ROC curve	–	0.58	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Wikoff et al. 2015b	–	–	–	–	–	–