Metabolite page

Showing information for HMDB0000112 ('γ-aminobutyric acid', 'gamma-aminobutyrate', 'GABA', '4-aminobutyric acid')

Metabolite information
HMDB ID	HMDB0000112
Synonyms	3-Carboxypropylamine 4 Aminobutanoic acid 4 Aminobutyric acid 4-Aminobutanoate 4-Aminobutanoic acid 4-Aminobutyrate 4-Aminobutyric acid 4Abu Acid, hydrochloride gamma-aminobutyric Aminalon Aminalone GABA GABA, lithium GAMMA-amino-BUTANOIC ACID Gaballon Gamarex Gammalon Gammalone Gammar Gammasol Hydrochloride gamma-aminobutyric acid Lithium gaba Mielogen Mielomade Omega-aminobutyrate Omega-aminobutyric acid Piperidate Piperidic acid Piperidinate Piperidinic acid W-Aminobutyrate W-Aminobutyric acid g-Aminobutanoate g-Aminobutanoic acid g-Aminobuttersaeure g-Aminobutyrate g-Aminobutyric acid g-amino-BUTANOate g-amino-BUTANOic acid g-amino-N-Butyrate g-amino-N-Butyric acid gamma Aminobutyrate gamma Aminobutyric acid gamma Aminobutyric acid, hydrochloride gamma Aminobutyric acid, monolithium salt gamma Aminobutyric acid, monosodium salt gamma-Aminobutanoate gamma-Aminobutanoic acid gamma-Aminobuttersaeure gamma-Aminobutyrate gamma-Aminobutyric acid, calcium salt (2:1) gamma-Aminobutyric acid, hydrochloride gamma-Aminobutyric acid, monolithium salt gamma-Aminobutyric acid, monosodium salt gamma-Aminobutyric acid, zinc salt (2:1) gamma-amino-BUTANOate gamma-amino-N-Butyrate gamma-amino-N-Butyric acid γ-amino-N-butyrate γ-amino-N-butyric acid γ-amino-butanoate γ-amino-butanoic acid γ-aminobutanoate γ-aminobutanoic acid γ-aminobuttersaeure γ-aminobutyrate γ-aminobutyric acid
Chemical formula	C4H9NO2
IUPAC name	4-aminobutanoic acid
CAS registry number	56-12-2
Monoisotopic molecular weight	103.063328537
Chemical taxonomy
Super class	Organic acids and derivatives
Class	Carboxylic acids and derivatives
Sub class	Amino acids, peptides, and analogues
Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000112 as a lung cancer biomarker

The studies that identify HMDB0000112 as a lung cancer-related metabolite

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
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	–
Chen et al. 2018	China	serum	diagnosis	NSCLC	I, II	90	40, 50	58.1 ± 9.0	–	healthy	90	42, 48	53.0 ± 11.8	–
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	–
Jiang et al. 2021	China	Saliva	diagnosis	lung cancer	I	45	16, 29	Median: 57.8	smoker, non-smoker	healthy	25	10, 15	Median: 52.9	smoker, non-smoker

Reference	Chromatography	Ion source	Positive/Negative mode	Mass analyzer	Identification level
Ro?-Mazurczyk et al. 2017	GC	–	–	TOF	In-source fragmentation
Chen et al. 2018	GC	EI	–	TOF	–
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
Jiang et al. 2021	–	MALDI	Negative	TOF/TOF	MS/MS

Reference	Data processing software	Database search
Ro?-Mazurczyk et al. 2017	Leco ChromaTOF-GC	Replib, Mainlib and Fiehn libraries
Chen et al. 2018	Chroma TOF	LECO-Fiehn Rtx 5
Moreno et al. 2018	–	KEGG, HMDB
Moreno et al. 2018	–	KEGG, HMDB
Jiang et al. 2021	FlexAnalysis, ClinproTools software, R script	HMDB

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Ro?-Mazurczyk et al. 2017	two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach	19.623 ± 9.3508	23.651 ± 11.954	0.83	0.13	0.45	–
Chen et al. 2018	PCA, OPLS-DA	–	–	0.67	–	–	1.24
Moreno et al. 2018	paired two-sample t-test, PLS-DA	–	–	1.57	2.37e-05	4.82e-05	–
Moreno et al. 2018	paired two-sample t-test, PLS-DA	–	–	1.35	6.75e-03	0.01	–
Jiang et al. 2021	Student’s t-test, PCA, Cluster analysis by Matlab. OPLS-DA	–	–	–	1.27e-04	3.90e-04	1.04

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Ro?-Mazurczyk et al. 2017	ROC curve	–	–	–	–	–
Chen et al. 2018	ROC curve	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Jiang et al. 2021	ROC analysis	–	0.986 (Combination)	97.2 (Combination)	92% (Combination)	–