Showing information for HMDB0000067 ('cholesterol')

Metabolite information
HMDB ID HMDB0000067
Synonyms
(-)-Cholesterol
(3b,14b,17a)-Cholest-5-en-3-ol
(3beta)-Cholest-5-en-3-ol
(3beta,14beta,17alpha)-Cholest-5-en-3-ol
(3β)-Cholest-5-en-3-ol
(3β,14β,17α)-cholest-5-en-3-ol
3beta-Hydroxycholest-5-ene
3β-Hydroxycholest-5-ene
5:6-Cholesten-3beta-ol
5:6-Cholesten-3β-ol
Cholest-5-en-3b-ol
Cholest-5-en-3beta-ol
Cholest-5-en-3β-ol
Cholesterin
Cholesterine
Cholesterol
Cholesterol base H
Cholesteryl alcohol
Cholestrin
Cholestrol
Cordulan
Dastar
Dusoline
Dusoran
Dythol
Fancol CH
Hydrocerin
Kathro
Lanol
Super hartolan
Tegolan
Chemical formula C27H46O
IUPAC name
(1S,2R,5S,10S,11S,14R,15R)-2,15-dimethyl-14-[(2R)-6-methylheptan-2-yl]tetracyclo[8.7.0.0^{2,7}.0^{11,15}]heptadec-7-en-5-ol
CAS registry number 57-88-5
Monoisotopic molecular weight 386.354866094

Chemical taxonomy
Super class Lipids and lipid-like molecules
Class Steroids and steroid derivatives
Sub class Cholestane steroids

Biological properties
Pathways (Pathway Details in HMDB)

The paper(s) that report HMDB0000067 as a lung cancer biomarker

The studies that identify HMDB0000067 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
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 –
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 –
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 –
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 –
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – before vs. after treatment (operation) 30 – 61.58 ± 10.67 –
Chen et al. 2015b China serum – lung cancer (postoperative) – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
Chen et al. 2015b China serum – lung cancer – 30 – 61.58 ± 10.67 – healthy 30 – 60.35 ± 12.48 –
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
Musharraf et al. 2015 Pakistan plasma – adenocarcinoma, squamous cell carcinoma, NSCLC, SCLC, lung cancer I, II, III 96 – – – healthy 96 – – non-smoker
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 –
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
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
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
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Ro?-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
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 –
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Chen et al. 2015b GC EI – quadrupole –
Wikoff et al. 2015b GC EI – TOF –
Musharraf et al. 2015 GC EI – triple quadrupole –
Callejon-Leblic et al. 2016 GC EI – ion trap –
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
Mu et al. 2019 GC – – – –
Zheng et al. 2021 GC EI – quadrupole –
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
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Ro?-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
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
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Chen et al. 2015b ChemStation NIST
Wikoff et al. 2015b BinBase NIST11, BinBase
Musharraf et al. 2015 Agilent Mass Hunter Qualitative Analysis, Mass Hunter Wiley registry NIST 11, Fiehn RTL libraries
Callejon-Leblic et al. 2016 XCMS NIST Mass Spectral Library
Moreno et al. 2018 – KEGG, HMDB
Moreno et al. 2018 – KEGG, HMDB
Mu et al. 2019 – –
Zheng et al. 2021 MassHunter Workstation software, Mass Profiler Professional software NIST14, HMDB, Golm Metabolome Database
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Miyamoto et al. 2015 Analysis of Covariance 294290.444444444 263529.35 1.12 0.28 – –
Miyamoto et al. 2015 Analysis of Covariance 296451 259749.636363636 1.14 0.07 – –
Mazzone et al. 2016 two- sample independent t test 0.9487085± 0.1522138 1.0097426± 0.1682058 0.94 3.26e-03 0.02 –
Ro?-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 365.71 ± 139.16 400.39 ± 186.85 0.91 0.36 0.69 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 42629 ± 23502 45116 ± 26006 0.95 0.96 0.98 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 53193 ± 12343 54802 ± 13674 0.97 0.87 0.95 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 56451 ± 41418 51016 ± 38967 1.11 0.21 0.58 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 74293 ± 15808 84288 ± 15250 0.88 6.00e-03 0.07 –
Chen et al. 2015b PCA, PLS-DA, independent t test – – 1.42 1.00e-03 – 1.62
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.71 1.00e-03 – 1.32
Chen et al. 2015b PCA, PLS-DA, independent t test – – 0.49 1.00e-03 – 2.14
Wikoff et al. 2015b OPLS-DA – – 1.00 – 0.66 –
Musharraf et al. 2015 one way ANOVA, Turkey’s honest Significance Difference – – 175648.29 1.00e-03 – –
Callejon-Leblic et al. 2016 PLS-LDA, one-way ANOVA – – 0.47 0.02 – 1.55
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.32 3.70e-05 7.26e-05 –
Moreno et al. 2018 paired two-sample t-test, PLS-DA – – 1.07 0.15 0.20 –
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.84 1.00e-03 1.00e-03 1.51
Zheng et al. 2021 Student’s t-test, Mann–Whitney U test, PCA, PLS-DA, and OPLS-DA – – 0.84 9.76e-31 1.76e-29 1.34
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Miyamoto et al. 2015 – – – – – –
Miyamoto et al. 2015 – – – – – –
Mazzone et al. 2016 – – – – – –
Ro?-Mazurczyk et al. 2017 ROC curve – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Fahrmann et al. 2015 random forest – – – – –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Chen et al. 2015b – – – – – –
Wikoff et al. 2015b – – – – – –
Musharraf et al. 2015 Hierarchical clustering, Partial Least Square Discrimination – – 96.2 92 93.1
Callejon-Leblic et al. 2016 ROC curve analysis – 0.72 – – –
Moreno et al. 2018 – – – – – –
Moreno et al. 2018 – – – – – –
Mu et al. 2019 – – – – – –
Zheng et al. 2021 ROC analysis – 0.993 (Combination of cholesterol, oleic acid, 4-hydroxybutyric acid, myo-inositol, and 2-hydroxybutyric acid) – – –