Abstract
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.
Highlights
Bladder cancer (BC) is a common malignant tumor disease of the urinary tract, and its incidence and mortality have always occupied the first place in the urinary reproductive system tumors
Due to the easy relapse characteristic, BC has been the focus of researchers to search tumor markers for the early diagnosis and postoperative evaluation to improve the survival rate of bladder cancer patients [1]
One is the metabonomics technology basing on nuclear magnetic resonance (NMR) [8,9,10], and the other is chromatography-mass spectrometry [11,12,13]
Summary
Bladder cancer (BC) is a common malignant tumor disease of the urinary tract, and its incidence and mortality have always occupied the first place in the urinary reproductive system tumors. Due to the easy relapse characteristic, BC has been the focus of researchers to search tumor markers for the early diagnosis and postoperative evaluation to improve the survival rate of bladder cancer patients [1]. Chromatography-mass spectrometry technology mainly contains gas chromatography-mass spectrometry (GC-MS) [15, 16], liquid chromatography-mass spectrometry (LC-MS) [17,18,19], and capillary electrophoresis-mass spectrometry (CE-MS) [20,21,22] Among these technologies, GC-MS has been widely used in metabonomics studies owing to its high sensitivity, strong analysis ability, and possessing more mature commercial mass spectrum library [23]. Is method was used to screen potential biomarkers from urine samples for bladder cancer diagnosis. Erefore, in this study, the rat bladder cancer model was established, and the urine metabonomics was studied with GC-MS technology. Erefore, in this study, the rat bladder cancer model was established, and the urine metabonomics was studied with GC-MS technology. e rat urine samples of the advanced stage, medium stage, and early stage of the bladder cancer model group and healthy group had been detected to establish the bladder cancer urine metabolic fingerprint, and the experimental data were analyzed with algorithm of random forests [41, 42] and used for the preliminary exploration of the tumor markers of BC
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