Metabolite page

Showing information for HMDB0001858 ('p-cresol')

Metabolite information
HMDB ID	HMDB0001858
Synonyms	1-Hydroxy-4-methylbenzene 1-Methyl-4-hydroxybenzene 4-Cresol 4-Cresol, aluminum salt 4-Cresol, potassium salt 4-Cresol, sodium salt 4-Hydroxytoluene 4-Methyl phenol 4-Methyl-phenol 4-Methylphenol 4-[Pentafluorosulfanyl]phenol Adipose Body fat Cellular membrane Cephalalgia Coffee Coffee bean Contaminant Cutaneous [related but nor necessarily exact synonym] Digestion Faecal Faeces Fat tissue Fauna Fecal Flora Gramineae Haemodialysis High blood pressure Hpn Htn Hypoglycaemia Nasal P-Cresylate P-Cresylic acid P-Hydroxytoluene P-Kresol P-Methyl phenol P-Methylhydroxybenzene P-Methylphenol P-Oxytoluene P-Toluol P-Tolyl alcohol Para-cresol Paracresol Paramethyl phenol Pollutant Pulmonary Skin contact Stool Topical Transdermal Uraemic toxin, uremic solute m-Cresol
Chemical formula	C7H8O
IUPAC name	4-methylphenol
CAS registry number	106-44-5
Monisotopic molecular weight	108.057514878
Chemical taxonomy
Super class	Benzenoids
Class	Phenols
Sub class	Cresols
Biological properties
Pahtways
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0001858 ('p-cresol')

Reference	Country	Specimen	Marker function	Participants (Case)						Participants (Control)
Reference	Country	Specimen	Marker function	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
Fahrmann et al. 2015	–	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	–	serum	diagnosis	adenocarcinoma	I, II, III, IV	49	17, 32	65.9 ± 9.87	–	healthy	31	11, 20	64.1 ± 8.97	–
Peralbo-Molina et al. 2016	–	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
Fahrmann et al. 2015	–	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	–	plasma	diagnosis	adenocarcinoma	I, II, III, IV	43	21, 22	67.3 ± 10.10	–	healthy	43	21, 22	65.9 ± 8.05	–
Chen et al. 2018	–	serum	diagnosis	NSCLC	I, II	90	40, 50	58.1 ± 9.0	–	healthy	90	42, 48	53.0 ± 11.8	–

Reference	Chromatography	Ion source	Positive/Negative mode	Mass analyzer	Identification level
Fahrmann et al. 2015	GC	EI	–	TOF	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Peralbo-Molina et al. 2016	GC	EI	–	Q-TOF	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Chen et al. 2018	GC	EI	–	TOF	–

Reference	Data processing software	Database search
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Peralbo-Molina et al. 2016	Mass Profiler Professional (Angilent), Quantitative Analysis (Angilent)	NIST 11, KEGG
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Chen et al. 2018	Chroma TOF	LECO-Fiehn Rtx 5

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	878 ± 747	877 ± 480	1	0.483	0.781	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	1857 ± 2263	1945 ± 1700	0.96	0.161	0.502	–
Peralbo-Molina et al. 2016	unpaired t-test, Fisher's exact, Mann–Whitney tests	–	–	0.116253011950669	0.0009	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	–	–	–	–	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	–	–	–	–	–	–
Chen et al. 2018	PCA, OPLS-DA	–	–	0.6	–	–	1.17

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Fahrmann et al. 2015	random forest	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Peralbo-Molina et al. 2016	Support-vector machine, ROC curve analysis	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Chen et al. 2018	ROC curve	–	–	–	–	–