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Showing information for HMDB0000517 ('L-arginine', 'Arg', 'arginine')

Metabolite information
HMDB ID	HMDB0000517
Synonyms	2-amino-5-Guanidinovalerate 2-amino-5-Guanidinovaleric acid 5-[[Aminoiminomethyl]amino]-L-norvaline Adipose Anephric patients Arg Arginase deficiency Arginine Arginine hydrochloride Arginine, L isomer Arginine, L-isomer Beautification product Body fat Chronic kidney failure Ckf Coffee Coffee bean Csf Cucurbits Cytoplasma DL Arginine acetate, monohydrate DL-Arginine acetate, monohydrate Dibasic aminoaciduria i Dietary supplement Digestion End-stage renal disease Eskd Esrd Essential mineral Extracellular region Faecal Faeces Familial protein intolerance Fat tissue Fauna Fecal Flora Gourds Gramineae Hf Hydrochloride, arginine Hyperdibasic aminoaciduria type 2 Kidneys L Arginine L-Arg L-Arginin L-Isomer arginine L-[+]-Arginine L-a-amino-D-Guanidinovalerate L-a-amino-D-Guanidinovaleric acid L-alpha-amino-delta-Guanidinovalerate L-alpha-amino-delta-Guanidinovaleric acid Legume Leukaemia Lpi Monohydrate DL-arginine acetate Myelin N5-[Aminoiminomethyl]-L-ornithine Neuron Nutraceutical Papilionoideae Pcp Personal hygiene Prostate gland R Semi-essential amino acid Soy Soya Soya bean Soybean Stage 5 chronic kidney disease Stage 5 ckd Stool Striated muscle Testes Testis Thrombocyte Toiletries Toiletry Trace mineral [2S]-2-amino-5-Guanidinopentanoate [2S]-2-amino-5-Guanidinopentanoic acid [2S]-2-amino-5-[carbamimidamido]Pentanoate [2S]-2-amino-5-[carbamimidamido]Pentanoic acid [S]-2-amino-5-Guanidinopentanoate [S]-2-amino-5-Guanidinopentanoic acid [S]-2-amino-5-Guanidinovalerate [S]-2-amino-5-Guanidinovaleric acid [S]-2-amino-5-[[Aminoiminomethyl]amino]-pentanoate [S]-2-amino-5-[[Aminoiminomethyl]amino]-pentanoic acid [S]-2-amino-5-[[Aminoiminomethyl]amino]pentanoate [S]-2-amino-5-[[Aminoiminomethyl]amino]pentanoic acid
Chemical formula	C6H14N4O2
IUPAC name	(2S)-2-amino-5-carbamimidamidopentanoic acid
CAS registry number	74-79-3
Monisotopic molecular weight	174.111675712
Chemical taxonomy
Super class	Organic acids and derivatives
Class	Carboxylic acids and derivatives
Sub class	Amino acids, peptides, and analogues
Biological properties
Pahtways	3-Phosphoglycerate dehydrogenase deficiency Arginine and Proline Metabolism Arginine: Glycine Amidinotransferase Deficiency [AGAT Deficiency] Argininemia Argininosuccinic Aciduria Aspartate Metabolism Canavan Disease Carbamoyl Phosphate Synthetase Deficiency Citrullinemia Type I Creatine deficiency, guanidinoacetate methyltransferase deficiency Dihydropyrimidine Dehydrogenase Deficiency [DHPD] Dimethylglycine Dehydrogenase Deficiency Dimethylglycine Dehydrogenase Deficiency Glycine and Serine Metabolism Guanidinoacetate Methyltransferase Deficiency [GAMT Deficiency] Hyperglycinemia, non-ketotic Hyperornithinemia with gyrate atrophy [HOGA] Hyperornithinemia-hyperammonemia-homocitrullinuria [HHH-syndrome] Hyperprolinemia Type I Hyperprolinemia Type II Hypoacetylaspartia L-arginine:glycine amidinotransferase deficiency Non Ketotic Hyperglycinemia Ornithine Aminotransferase Deficiency [OAT Deficiency] Ornithine Transcarbamylase Deficiency [OTC Deficiency] Prolidase Deficiency [PD] Prolinemia Type II Sarcosinemia Transcription/Translation Urea Cycle
Author-emphasized biomarker in the paper(s)	Ni et al. 2016 Ni et al. 2019 Maeda et al. 2010 Huang et al. 2016

Lung cancer metabolomics studies that identify HMDB0000517 ('L-arginine', 'Arg', 'arginine')

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
Huang et al. 2016	–	dried blood spot	diagnosis	lung cancer	–	222	94, 128	median: 57.47 (27-81)	–	healthy	96	30, 66	median: 56.07 (32-80)	–
Huang et al. 2016	–	dried blood spot	diagnosis	benign lung disease	–	118	55, 63	median: 59.61 (32-80)	–	healthy	96	30, 66	median: 56.07 (32-80)	–
Ni et al. 2019	–	serum	diagnosis	lung cancer	–	40	26, 14	66.7 (49-83)	–	healthy	100	65, 35	64.1 (41-90)	–
Ni et al. 2016	–	serum	diagnosis	lung cancer	–	40	14, 26	67	–	healthy	100	–	–	–
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	–
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	–
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	–
Maeda et al. 2010	–	plasma	–	NSCLC	I, II, III, IV	141	93, 48	62.7 ± 9.2	former, current, non-smoker	healthy	423	279, 144	61.1 ± 8.7	former, current, non-smoker
Callejon-Leblic et al. 2016	–	bronchoalveolar lavage fluid	diagnosis	lung cancer	–	24	16, 8	66 ± 11	–	noncancerous lung diseases	31	23, 8	56 ± 13	–
Ni et al. 2019	–	serum	diagnosis	NSCLC, SCLC	II, III, IV	17	13, 4	66.3 (53-77)	former, current, non-smoker	healthy	30	23, 7	62.8 (34-85)	former, current, non-smoker
Klupczynska et al. 2016a	–	serum	diagnosis	adenocarcinoma, squamous cell carcinoma	I, II, III	90	58, 32	64 (48-86)	current, non-smoker, unknown	healthy	63	41, 22	62 (43-78)	smoker, non-smoker, unknown
Yue et al. 2018	–	plasma	diagnosis	SCLC	–	20	–	–	–	healthy	20	–	–	–
Wen et al. 2013	–	plasma	diagnosis	adenocarcinoma	I	31	15, 16	median: 63 (40-81)	smoker, non-smoker	healthy	28	20, 8	median: 37 (29-50)	smoker, non-smoker
Yang et al. 2020	–	pleural effusion	diagnosis	adenocarcinoma	–	46	15, 31	63 ± 12	–	pulmonary tuberculosis, other pulmonary diseases	32	26, 6	49 ± 19	–

Reference	Chromatography	Ion source	Positive/Negative mode	Mass analyzer	Identification level
Huang et al. 2016	LC	ESI	positive	QTrap	MS/MS
Huang et al. 2016	LC	ESI	positive	QTrap	MS/MS
Ni et al. 2019	LC	ESI	positive	triple quadrupole	MS/MS
Ni et al. 2016	LC	ESI	positive	Triple quadrupole	MS/MS
Mazzone et al. 2016	LC	ESI	positive	linear ion-trap	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
Maeda et al. 2010	LC	ESI	positive	quadrupole	–
Callejon-Leblic et al. 2016	DI	ESI	positive	Q-TOF	MS/MS
Ni et al. 2019	LC	ESI	positive	triple quadrupole	MS/MS
Klupczynska et al. 2016a	LC	–	–	QTRAP	MS/MS
Yue et al. 2018	LC	ESI	positive, negative	QTRAP	MS/MS
Wen et al. 2013	LC	ESI	–	Q-TOF	MS/MS
Yang et al. 2020	LC	ESI	positive	Q-Orbitrap	MS/MS

Reference	Data processing software	Database search
Huang et al. 2016	Analyst software, ChemoView software	–
Huang et al. 2016	Analyst software, ChemoView software	–
Ni et al. 2019	–	HMDB, KEGG, SMPDB
Ni et al. 2016	–	–
Mazzone et al. 2016	Metabolon LIMS system	Metabolon LIMS system
Moreno et al. 2018	–	KEGG, HMDB
Moreno et al. 2018	–	KEGG, HMDB
Maeda et al. 2010	Xcalibur	–
Callejon-Leblic et al. 2016	Markerview	HMDB, METLIN
Ni et al. 2019	–	HMDB, KEGG, SMPDB
Klupczynska et al. 2016a	–	–
Yue et al. 2018	Analyst, MultiQuant	–
Wen et al. 2013	MassHunter, Mass Profiler Professional software (Agilent)	NIST 08, HMDB, METLIN, LIPID MAPS
Yang et al. 2020	XCMS	HMDB, METLIN, LipidSearch

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Huang et al. 2016	PLS-DA, ANOVA, student’s t-test	–	–	–	<0.001	–	–
Huang et al. 2016	PLS-DA, ANOVA, student’s t-test	–	–	–	<0.001	–	–
Ni et al. 2019	Mann-Whitney U test, Student's t-test, Welch's F test	224.94	119.51	–	<0.001	–	–
Ni et al. 2016	one‐way ANOVA	224.94 ± 72.52 μmol/L	123.76 ± 43.14 μmol/L	–	<0.0001	–	–
Mazzone et al. 2016	two- sample independent t test	1.025966± 0.2495975	1.025497± 0.232247	1.00045733922186	0.9875386	0.804216432	–
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	1.01312334003287	0.812289024660883	0.835317837215702	–
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	1.09817071824279	0.251250433741696	0.319888280293855	–
Maeda et al. 2010	Mann-Whitney U-test, PCA	101.3 ± 21.6 μM	98.1±17.8 μM	–	0.12	–	–
Callejon-Leblic et al. 2016	PLS-LDA, one-way ANOVA	–	–	0.8	0.036	–	1.43
Ni et al. 2019	Mann-Whitney U test, Student's t-test, Welch's F test	139.89	119.89	–	0.012	–	–
Klupczynska et al. 2016a	t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA	106.5±32.7 ?M	94.11±23.52 ?M	1.13	0.0058	–	–
Yue et al. 2018	OPLS-DA, student’s t-test	495.84±146.08 ng/mL	346.18±93.95 ng/mL	2.67585510957222	0.0000863	–	1.36
Wen et al. 2013	Mann–Whitney–Wilcoxon test, OPLS-DA	–	–	0.129408115480172	0.000000179	–	1.13
Yang et al. 2020	PLS-DA	–	–	1.38	–	0.00009	1.04

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Huang et al. 2016	–	–	–	–	–	–
Huang et al. 2016	–	–	–	–	–	–
Ni et al. 2019	ROC analysis	–	0.907	–	–	–
Ni et al. 2016	–	–	–	–	–	–
Mazzone et al. 2016	–	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Maeda et al. 2010	ROC curve	–	combination of 21 amino acid: 0.812	–	–	–
Callejon-Leblic et al. 2016	ROC curve analysis	–	0.53	–	–	–
Ni et al. 2019	ROC analysis	–	0.716	–	–	–
Klupczynska et al. 2016a	ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis	–	0.631	–	–	–
Yue et al. 2018	–	–	–	–	–	–
Wen et al. 2013	ROC curve analysis	–	0.89	–	–	–
Yang et al. 2020	ROC analysis	–	0.81	–	–	–