A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the...

Full description

Saved in:
Bibliographic Details
Published in:International journal of analytical chemistry Vol. 2020; no. 2020; pp. 1 - 9
Main Authors: Guo, Lan, Wu, Liqing, Huang, Lingling, Liu, Fan, Fang, Mengchan, Wan, Yiqun
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 21-09-2020
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Subjects:
Algorithms
Analytical chemistry
Biomarkers
Bladder
Bladder cancer
Cancer
Chromatography
Classification
Decomposition
Galactose
Gas chromatography
Glycine
Instrument industry
Mass spectrometry
Metabolism
Metabolites
Multivariate statistical analysis
NMR
Nuclear magnetic resonance
Oncology, Experimental
Scientific imaging
Spectroscopy
Statistical analysis
Statistical methods
Sucrose
Tumors
Urea
Urine
United States
China
St Louis Missouri
United States > US
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Academic Editor: David M. Lubman
ISSN:1687-8760
1687-8779
DOI:10.1155/2020/8839215