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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Analytical Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2020/8839215 |
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| author | Mengchan Fang Fan Liu Lingling Huang Liqing Wu Lan Guo Yiqun Wan |
| author_facet | Mengchan Fang Fan Liu Lingling Huang Liqing Wu Lan Guo Yiqun Wan |
| author_sort | Mengchan Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c50c01985f249128cad6c12c6063788 |
| institution | Kabale University |
| issn | 1687-8760 1687-8779 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Analytical Chemistry |
| spelling | doaj-art-4c50c01985f249128cad6c12c60637882025-02-03T06:46:38ZengWileyInternational Journal of Analytical Chemistry1687-87601687-87792020-01-01202010.1155/2020/88392158839215A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest AlgorithmMengchan Fang0Fan Liu1Lingling Huang2Liqing Wu3Lan Guo4Yiqun Wan5College of Chemistry, Nanchang University, Nanchang 330031, ChinaJiangxi Province Key Laboratory of Modern Analytical Science, Nanchang University, Nanchang 330031, ChinaCollege of Chemistry, Nanchang University, Nanchang 330031, ChinaDepartment of Pathology, 3rd Affiliated Hospital of Nanchang University, Nanchang 330008, ChinaCollege of Chemistry, Nanchang University, Nanchang 330031, ChinaCollege of Chemistry, Nanchang University, Nanchang 330031, ChinaA 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.http://dx.doi.org/10.1155/2020/8839215 |
| spellingShingle | Mengchan Fang Fan Liu Lingling Huang Liqing Wu Lan Guo Yiqun Wan A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm International Journal of Analytical Chemistry |
| title | A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm |
| title_full | A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm |
| title_fullStr | A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm |
| title_full_unstemmed | A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm |
| title_short | A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm |
| title_sort | urine metabonomics study of rat bladder cancer by combining gas chromatography mass spectrometry with random forest algorithm |
| url | http://dx.doi.org/10.1155/2020/8839215 |
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