Showing information for HMDB0000064 ('creatine')

Metabolite information
HMDB ID HMDB0000064
Synonyms
((amino(imino)Methyl)(methyl)amino)acetate
((amino(imino)Methyl)(methyl)amino)acetic acid
(N-methylcarbamimidamido)Acetate
(N-methylcarbamimidamido)Acetic acid
(a-methylguanido)Acetate
(a-methylguanido)Acetic acid
(alpha-methylguanido)Acetate
(alpha-methylguanido)Acetic acid
(α-methylguanido)acetate
(α-methylguanido)acetic acid
Cosmocair C 100
Creatin
Creatine hydrate
Kreatin
Krebiozon
Methylglycocyamine
Methylguanidoacetate
Methylguanidoacetic acid
N-(Aminoiminomethyl)-N-methyl-glycine
N-(Aminoiminomethyl)-N-methylglycine
N-Amidinosarcosine
N-Carbamimidoyl-N-methylglycine
N-Methyl-N-guanylglycine
N-[(e)-amino(imino)METHYL]-N-methylglycine
Phosphagen
[[amino(imino)Methyl](methyl)amino]acetate
[[amino(imino)Methyl](methyl)amino]acetic acid
a-methylguanidino Acetate
a-methylguanidino Acetic acid
alpha-methylguanidino Acetate
alpha-methylguanidino Acetic acid
α-methylguanidino acetate
α-methylguanidino acetic acid
Chemical formula C4H9N3O2
IUPAC name
2-(N-methylcarbamimidamido)acetic acid
CAS registry number 57-00-1
Monoisotopic molecular weight 131.069476547

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 HMDB0000064 as a lung cancer biomarker

The studies that identify HMDB0000064 as a lung cancer-related metabolite

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 US 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 –
Callejon-Leblic et al. 2016 Spain bronchoalveolar lavage fluid diagnosis lung cancer – 24 16, 8 66 ± 11 – noncancerous lung diseases 31 23, 8 56 ± 13 –
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 –
Callej?n-Leblic et al. 2019 Spain 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
Qi et al. 2021 China blood diagnosis adenocarcinoma, squamous cell carcinoma, small cell lung cancer, other types, unknown types I, II, III, IV 98 51, 47 Median: 50 (32-69) – healthy 75 36, 39 Median: 50 (31-69) –
You et al. 2020 China tissue diagnosis AC I, II, III 45xa0 – – – SCC 18xa0 – – –
You et al. 2020 China Lung carcinoma tissue diagnosis NSCLC I, II, III 85 51, 34 59 ± 12 – benign lung tissues 85 51, 34 59 ± 12 –
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis adenocarcinoma (ADC) I, II, III 33 23, 10 64.77 ± 8.44 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – healthy control 20 13, 7 61.5 ± 12.06 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – adenocarcinoma (ADC) 33 23, 10 64.77 ± 8.44 –
Kowalczyk et al. 2021 Poland Tissue diagnosis squemous cell carcinoma (SCC) I, II, III 54 39, 15 64.45 ± 8.02 – adenocarcinoma (ADC) 33 23, 10 64.77 ± 8.44 –
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Mazzone et al. 2016 LC ESI positive linear ion-trap MS/MS
Callejon-Leblic et al. 2016 DI ESI positive Q-TOF MS/MS
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
Callej?n-Leblic et al. 2019 DI ESI positive Q-TOF MS/MS
Qi et al. 2021 LC ESI both Q-Orbitrap MS/MS
You et al. 2020 LC ESI both Q-TOF MS/MS
You et al. 2020 LC ESI both Q-TOF MS/MS
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Reference Data processing software Database search
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Callejon-Leblic et al. 2016 Markerview HMDB, METLIN
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callej?n-Leblic et al. 2019 – HMDB, Metlin
Qi et al. 2021 ProteoWizard, XCMS, Xcalibur, CAMERA mzCloud, ChemSpider, LipidBlast and Fiehn HILIC
You et al. 2020 MarkerView workstation, MultiQuant 3.0.3 OSI-SMMS
You et al. 2020 MarkerView workstation, MultiQuant 3.0.3 OSI-SMMS
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
Kowalczyk et al. 2021 Mass Hunter Qualitative Analysis Software, Mass Profiler Professional METLIN, KEGG, LIPIDMAPS, and HMDB
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 1.174984± 0.8189461 1.251814± 0.8039395 0.94 0.45 0.54 –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.76 0.03 – 1.53
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 2.61 7.43e-14 1.38e-12 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.44 4.71e-05 1.82e-04 –
Callej?n-Leblic et al. 2019 PCA, PLS-DA, one-way ANOVA – – 1.33 1.20e-03 – 1.89
Qi et al. 2021 PCA, OPLS-DA, Student’s t test – – 1.20 0.05 – 1.58
You et al. 2020 PCA, PLS-DA, non-parametric test – – 0.34 <0.001 <0.001 –
You et al. 2020 PCA, PLS-DA, non-parametric test – – 0.60 0.03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 1.62e-04 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 3.53e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 4.68e-04 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 5.07e-06 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 9.83e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 4.07e-03 – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Mazzone et al. 2016 – – – – – –
Callejon-Leblic et al. 2016 ROC curve analysis – 0.6 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Callej?n-Leblic et al. 2019 ROC curve – 0.7 – – –
Qi et al. 2021 – – – – – –
You et al. 2020 ROC analysis – 0.941 (Creatine + myoinositol + LPE 16:0 ) 0.844 (Creatine + myoinositol + LPE 16:0 ) 0.944 (Creatine + myoinositol + LPE 16:0 ) –
You et al. 2020 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –