Showing information for HMDB0034263 ('triethyl citrate')


Metabolite information

HMDB ID HMDB0034263
Synonyms
1,2,3-Propanetricarboxylic acid, 2-hydroxy-, triethyl ester
2-Hydroxy-1,2,3-propanetricarboxylic acid, triethyl ester
Citric acid, triethyl ester
Citroflex 2
Crodamol TC
Ethyl citrate
Ethyl citrate, citric acid triethyl ester
Eudraflex
FEMA 3083
Hydagen c.a.t
Hydragen cat
TEC
Triaethylcitrat
Triethyl 2-hydroxy-1,2,3-propanetricarboxylate
Triethyl citrate (NF)
Triethyl citric acid
Triethylester kyseliny citronove
Uniflex tec
Uniplex 80
e1505
Chemical formula C12H20O7
IUPAC name
1,2,3-triethyl 2-hydroxypropane-1,2,3-tricarboxylate
CAS registry number 77-93-0
Monoisotopic molecular weight 276.120902994

Chemical taxonomy

Super class Organic acids and derivatives
Class Carboxylic acids and derivatives
Sub class Tricarboxylic acids and derivatives

Biological properties

Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0034263 as a lung cancer biomarker

The studies that identify HMDB0034263 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
Peralbo-Molina et al. 2016 Spain exhaled breath diagnosis adenocarcinoma, squamous cell carcinoma, large cell carcinoma, NSCLC, SCLC 48 45, 3 63 ± 7 healthy 61 53, 8 60 ± 9 non-smoker
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Peralbo-Molina et al. 2016 GC EI Q-TOF
Reference Data processing software Database search
Peralbo-Molina et al. 2016 Mass Profiler Professional (Angilent), Quantitative Analysis (Angilent) NIST 11, KEGG
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Peralbo-Molina et al. 2016 unpaired t-test, Fisher's exact, Mann–Whitney tests 4.70 9.73e-03
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Peralbo-Molina et al. 2016 Support-vector machine, ROC curve analysis