Dataset Information
(1)H-NMR urinary metabolomic profiling for diagnosis of gastric cancer.
Ontology highlight
ABSTRACT: Background
Metabolomics has shown promise in gastric cancer (GC) detection. This research sought to identify whether GC has a unique urinary metabolomic profile compared with benign gastric disease (BN) and healthy (HE) patients.Methods
Urine from 43 GC, 40 BN, and 40 matched HE patients was analysed using (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy, generating 77 reproducible metabolites (QC-RSD <25%). Univariate and multivariate (MVA) statistics were employed. A parsimonious biomarker profile of GC vs HE was investigated using LASSO regularised logistic regression (LASSO-LR). Model performance was assessed using Receiver Operating Characteristic (ROC) curves.Results
GC displayed a clear discriminatory biomarker profile; the BN profile overlapped with GC and HE. LASSO-LR identified three discriminatory metabolites: 2-hydroxyisobutyrate, 3-indoxylsulfate, and alanine, which produced a discriminatory model with an area under the ROC of 0.95.Conclusions
GC patients have a distinct urinary metabolite profile. This study shows clinical potential for metabolic profiling for early GC diagnosis.
SUBMITTER: Chan AW
PROVIDER: S-EPMC4716538 | biostudies-literature | 2016 Jan
REPOSITORIES: biostudies-literature
Publications
(1)H-NMR urinary metabolomic profiling for diagnosis of gastric cancer.
British journal of cancer 20151208 1
<h4>Background</h4>Metabolomics has shown promise in gastric cancer (GC) detection. This research sought to identify whether GC has a unique urinary metabolomic profile compared with benign gastric disease (BN) and healthy (HE) patients.<h4>Methods</h4>Urine from 43 GC, 40 BN, and 40 matched HE patients was analysed using (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy, generating 77 reproducible metabolites (QC-RSD <25%). Univariate and multivariate (MVA) statistics were employed. A par ...[more]
