Showing information for HMDB0000943 ('isothreonic acid', 'threonic acid', 'L-threonic acid/hypoxanthine')

Metabolite information
HMDB ID HMDB0000943
Synonyms
(R*,s*)-2,3,4-trihydroxy-butanoate
(R*,s*)-2,3,4-trihydroxy-butanoic acid
2,3,4-Trihydroxy-(threo)-butanoic acid
Calcium L-threonate
Calcium threonate
Magnesium threonate
Threonate
Threonic acid, (R-(r*,s*))-isomer
Threonic acid, (r*,r*)-isomer
threo-2,3,4-Trihydroxybutyrate
threo-2,3,4-Trihydroxybutyric acid
Chemical formula C4H8O5
IUPAC name
(2S,3R)-2,3,4-trihydroxybutanoic acid
CAS registry number 3909-12-4
Monoisotopic molecular weight 136.037173366

Chemical taxonomy
Super class Organic oxygen compounds
Class Organooxygen compounds
Sub class Carbohydrates and carbohydrate conjugates

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000943 as a lung cancer biomarker

The studies that identify HMDB0000943 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
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
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 plasma 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 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 plasma 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 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 49 17, 32 65.9 ± 9.87 – healthy 31 11, 20 64.1 ± 8.97 –
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
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
Callejon-Leblic et al. 2019 Spain urine diagnosis NSCLC, SCLC – 32 22, 8 66 ± 12 former, current, non-smoker healthy 29 18, 11 56 ± 13 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) –
Zheng et al. 2021 China Serum diagnosis lung cancer I, II, III, IV 57 38, 19 Median: 62 (52-69) smoker, non-smoker healthy 59 48, 11 Median: 60 (59-62) smoker, non-smoker
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 – adenocarcinoma (ADC) 33 23, 10 64.77 ± 8.44 –
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
Miyamoto et al. 2015 GC EI – TOF MS/MS
Miyamoto et al. 2015 GC EI – TOF 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 –
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 –
Wikoff et al. 2015b GC EI – TOF –
Wikoff et al. 2015b GC EI – TOF –
Callejon-Leblic et al. 2019 GC EI – ion trap –
Qi et al. 2021 LC ESI both Q-Orbitrap MS/MS
Zheng et al. 2021 GC EI – quadrupole –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
Kowalczyk et al. 2021 LC ESI both Q-TOF –
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
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Miyamoto et al. 2015 ChromaTOF software (Leco) 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
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
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Wikoff et al. 2015b BinBase NIST11, BinBase
Wikoff et al. 2015b BinBase NIST11, BinBase
Callejon-Leblic et al. 2019 XCMS NIST Mass Spectral Library
Qi et al. 2021 ProteoWizard, XCMS, Xcalibur, CAMERA mzCloud, ChemSpider, LipidBlast and Fiehn HILIC
Zheng et al. 2021 MassHunter Workstation software, Mass Profiler Professional software NIST14, HMDB, Golm Metabolome Database
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
Miyamoto et al. 2015 Analysis of Covariance 3336.72222222222 3483.7 0.96 0.78 – –
Miyamoto et al. 2015 Analysis of Covariance 3438.72727272727 3389.36363636364 1.01 0.73 – –
Miyamoto et al. 2015 Analysis of Covariance 13104.7222222222 18193.75 0.72 0.06 – –
Miyamoto et al. 2015 Analysis of Covariance 13327.0909090909 18239.2727272727 0.73 0.18 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1000 ± 624 1211 ± 686 0.83 0.26 0.63 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1109 ± 717 1377 ± 645 0.81 6.00e-03 0.07 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1222 ± 722 2000 ± 1341 0.61 2.00e-03 0.03 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 1994 ± 1279 2371 ± 1581 0.84 0.34 0.65 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 3196 ± 2751 4221 ± 2967 0.76 0.04 0.24 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 584 ± 339 720 ± 347 0.81 7.00e-03 0.06 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 604 ± 803 525 ± 146 1.15 0.76 0.90 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 640 ± 716 592 ± 162 1.08 0.51 0.74 –
Wikoff et al. 2015b OPLS-DA – – 1.30 – 8.00e-03 –
Wikoff et al. 2015b OPLS-DA – – 1.20 – 0.65 –
Callejon-Leblic et al. 2019 PLS-LDA, one-way ANOVA – – 2.03 9.00e-03 – 1.93
Qi et al. 2021 PCA, OPLS-DA, Student’s t test – – 1.38 5.77e-07 – 2.10
Zheng et al. 2021 Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA – – 0.89 3.61e-18 1.62e-17 1.20
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 3.21e-03 – –
Kowalczyk et al. 2021 Mann–Whitney U-test and Benjamini–Hochberg false discovery rate, partial least squares discriminant analysis (PLS-DA) – – – 0.03 – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Wikoff et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Callejon-Leblic et al. 2019 ROC curve analysis – 0.7 – – –
Qi et al. 2021 – – – – – –
Zheng et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –
Kowalczyk et al. 2021 – – – – – –