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

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.

Figure 1

The total ion chromatograms (TICs) of rat bladder cancer urine sample.

Figure 2

Histopathological specimen examination of the rat bladder of the control (a), 15th week (b), 25th week (c), and 35th week (d). Note. (a) The epithelial tissue was composed of 2–3 layers, and the cells were extremely obvious, without abnormality, and arranged in an orderly manner. (b)There were papillary hyperplasia in the partial region, the epithelial cell had 4–6 layers, polarity had a little disorder, and the cell's morphology and size had a certain atypia. (c) The layer number of tumor cell increased significantly with ball-shaped distribution, the sizes of tumor cells were different, the nucleus was deeply dyed and showed polymorphism, the atypia was obvious, and some tumor cells showed the characteristics of squamous cell tumor differentiation. (d) The nucleus was deeply stained, the nuclear membrane was thickened, the nucleoli were obvious and showed pathological karyokinesis, and the muscularis was deeply infiltrated.

Figure 3

Analysis results of four group samples with random forest.

Figure 4

The variable importance of metabolites. (a) Healthy control vs. early- and middle-stage groups, (b) healthy control vs. advanced-stage group, and (c) early- and middle-stage groups vs. advanced stage group.

Figure 5

Pathway analysis based on significant metabolites.