Showing information for HMDB0000574 ('L-cysteine', 'cysteine')

Metabolite information
HMDB ID HMDB0000574
Synonyms
(+)-2-amino-3-Mercaptopropionic acid
(2R)-2-amino-3-Mercaptopropanoate
(2R)-2-amino-3-Mercaptopropanoic acid
(2R)-2-amino-3-Sulfanylpropanoate
(2R)-2-amino-3-Sulfanylpropanoic acid
(2R)-2-amino-3-Sulphanylpropanoate
(2R)-2-amino-3-Sulphanylpropanoic acid
(R)-(+)-Cysteine
(R)-2-amino-3-Mercaptopropanoate
(R)-2-amino-3-Mercaptopropanoic acid
(R)-2-amino-3-mercapto-Propanoate
(R)-2-amino-3-mercapto-Propanoic acid
(R)-Cysteine
2-amino-3-Mercaptopropanoate
2-amino-3-Mercaptopropanoic acid
2-amino-3-Mercaptopropionate
2-amino-3-Mercaptopropionic acid
3-mercapto-L-Alanine
Acetylcysteine
C
CYSTEINE
Carbocysteine
Cisteina
Cisteinum
Cys
Cystein
Cysteine hydrochloride
Cysteinum
FREE cysteine
Half cystine
Half-cystine
L Cysteine
L-(+)-Cysteine
L-2-amino-3-Mercaptopropanoate
L-2-amino-3-Mercaptopropanoic acid
L-2-amino-3-Mercaptopropionate
L-2-amino-3-Mercaptopropionic acid
L-Cystein
L-Zystein
Polycysteine
Thioserine
Zinc cysteinate
alpha-amino-beta-Thiolpropionic acid
b-Mercaptoalanine
beta-Mercaptoalanine
e 920
e-920
e920
Chemical formula C3H7NO2S
IUPAC name
(2R)-2-amino-3-sulfanylpropanoic acid
CAS registry number 52-90-4
Monoisotopic molecular weight 121.019749163

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 HMDB0000574 as a lung cancer biomarker


The studies that identify HMDB0000574 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
Miyamoto et al. 2015 US blood diagnosis adenocarcinoma unknown (mostly late stage) 18 10, 8 67 (50-85) / 62 (53-72) former, current healthy 20 8, 12 64 (49-80) / 66 (58-82) former, current
Miyamoto et al. 2015 US blood diagnosis NSCLC, SCLC, mesothelioma, secondary metastasis to lung I, II, III, IV 11 4, 7 67 (61-73) / 67 (47-76) smoker, non-smoker healthy 11 5, 6 69 (61-83) / 54 (44-61) unknown
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 –
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 –
Fahrmann et al. 2015 US plasma diagnosis adenocarcinoma I, II, III, IV 52 17, 35 65.9 ± 9.66 – healthy 31 11, 20 64.1 ± 8.97 –
Fahrmann et al. 2015 US serum diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Fahrmann et al. 2015 US serum diagnosis adenocarcinoma I, II, III, IV 49 17, 32 65.9 ± 9.87 – healthy 31 11, 20 64.1 ± 8.97 –
Fahrmann et al. 2015 US plasma diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Hori et al. 2011 Japan 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
Wikoff et al. 2015b US 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
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 –
Callejon-Leblic et al. 2019 Spain serum diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, non-smoker
Callejon-Leblic et al. 2019 Spain serum diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 former, non-smoker
Mu et al. 2019 China serum diagnosis NSCLC I, II, III, IV 30 0, 30 60.4 ± 9.7 non-smoker healthy 30 0, 30 54.7 ± 14.3 non-smoker
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Miyamoto et al. 2015 GC EI – TOF MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Wikoff et al. 2015b 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
Callejon-Leblic et al. 2019 GC EI – ion trap –
Callejon-Leblic et al. 2019 GC EI – ion trap –
Mu et al. 2019 GC – – – –
Reference Data processing software Database search
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Wikoff et al. 2015b BinBase NIST11, BinBase
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Mu et al. 2019 – –
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 7816.22222222222 9046.7 0.86 0.25 – –
Miyamoto et al. 2015 Analysis of Covariance 8400.90909090909 8526.72727272727 0.99 0.55 – –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.54811 ± 0.28307 0.77687 ± 0.89885 0.71 0.31 0.65 –
Mazzone et al. 2016 two- sample independent t test 0.8982138± 0.2592517 1.0673047± 0.2545761 0.84 3.16e-07 0.02 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1230 ± 597 1507 ± 1050 0.82 0.56 0.83 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1378 ± 604 1406 ± 533 0.98 0.66 0.89 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1659 ± 783 1843 ± 970 0.90 0.45 0.72 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 714 ± 337 790 ± 398 0.90 0.36 0.63 –
Hori et al. 2011 student’s t-test, PLS-DA – – 3.26 0.02 – –
Wikoff et al. 2015b OPLS-DA – – 1.60 – 3.00e-03 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 3.37 5.89e-10 2.95e-09 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 2.01 1.12e-06 8.31e-06 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 1.90 0.04 – 1.28
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 0.91 9.00e-03 – 1.48
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.65 1.00e-03 1.00e-03 1.90
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Mazzone et al. 2016 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Wikoff et al. 2015b – – – – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.64 – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.54 – – –
Mu et al. 2019 – – – – – –