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Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma

文献类型：期刊论文

作者Li, Na1,4,5; Yang, Chenxi1,5; Zhou, Sicheng1,5; Song, Siyu1,5; Jin, Yuyao1,3,5; Wang, Ding1,4,5; Liu, Junping1,5; Gao, Yun1,5; Yang, Haining2; Mao, Weimin1,5
刊名DIAGNOSTICS
出版日期2021-07-01
卷号11
关键词malignant mesothelioma metabolomics machine learning diagnosis
DOI10.3390/diagnostics11071281
通讯作者Yang, Haining(hyang@cc.hawaii.edu) ; Mao, Weimin(maowm@zjcc.org.cn) ; Chen, Zhongjian(chenzj@zjcc.org.cn)
英文摘要Background: Malignant mesothelioma (MM) is an aggressive and incurable carcinoma that is primarily caused by asbestos exposure. However, the current diagnostic tool for MM is still under-developed. Therefore, the aim of this study is to explore the diagnostic significance of a strategy that combined plasma-based metabolomics with machine learning algorithms for MM. Methods: Plasma samples collected from 25 MM patients and 32 healthy controls (HCs) were randomly divided into train set and test set, after which analyzation was performed by liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were screened out from the samples of the train set. Subsequently, metabolite-based diagnostic models, including receiver operating characteristic (ROC) curves and Random Forest model (RF), were established, and their prediction accuracies were calculated for the test set samples. Results: Twenty differential plasma metabolites were annotated in the train set; 10 of these metabolites were validated in the test set. The seven metabolites with most significant diagnostic values were taurocholic acid (accuracy = 0.6429), uracil (accuracy = 0.7143), biliverdin (accuracy = 0.7143), tauroursodeoxycholic acid (accuracy = 0.5000), histidine (accuracy = 0.8571), pyrroline hydroxycarboxylic acid (accuracy = 0.8571), and phenylalanine (accuracy = 0.7857). Furthermore, RF based on 20 annotated metabolites showed a prediction accuracy of 0.9286, and its optimized version achieved 1.0000 in the test set. Moreover, the comparison between the samples of peritoneal MM (n = 8) and pleural MM (n = 17) illustrated a significant increase in levels of taurocholic acid and tauroursodeoxycholic acid, as well as an evident decrease in biliverdin. Conclusions: Our results revealed the potential diagnostic value of plasma-based metabolomics combined with machine learning for MM. Further research with large sample size is worthy conducting. Moreover, our data demonstrated dysregulated metabolism pathways in MM, which aids in better understanding of molecular mechanisms related to the initiation and development of MM.
WOS关键词ACID-INDUCED APOPTOSIS ; CANCER ; METABOLISM ; EPIDEMIOLOGY
资助项目National Natural Science Foundation of China[81672315] ; National Natural Science Foundation of China[81302840] ; Key R&D Program Projects in Zhejiang Province[2018C04009] ; 1022 Talent Training Program of Zhejiang CancerHospital ; Zhejiang ProvincialNatural Science Fund[LY20H280001] ; International Cooperation Project of Zhejiang Basic Public Technology Research Program[LGJ20H010001] ; Projects of Zhejiang Province Medical and Health Science and Technology Plan[2017KY256] ; Zhejiang Provincial Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology
WOS研究方向General & Internal Medicine
语种英语
出版者MDPI
WOS记录号WOS:000676686500001
资助机构National Natural Science Foundation of China ; Key R&D Program Projects in Zhejiang Province ; 1022 Talent Training Program of Zhejiang CancerHospital ; Zhejiang ProvincialNatural Science Fund ; International Cooperation Project of Zhejiang Basic Public Technology Research Program ; Projects of Zhejiang Province Medical and Health Science and Technology Plan ; Zhejiang Provincial Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/123248]  
专题中国科学院合肥物质科学研究院
通讯作者Yang, Haining; Mao, Weimin; Chen, Zhongjian
作者单位1.Chinese Acad Sci, Inst Canc & Basic Med IBMC, Hangzhou 310000, Peoples R China
2.Univ Hawaii, Thorac Oncol, Canc Ctr, Honolulu, HI 96813 USA
3.Hangzhou Med Coll, Dept Pharmaceut Sci, Hangzhou 310013, Peoples R China
4.Zhejiang Chinese Med Univ, Clin Med Coll 2, Hangzhou 310053, Peoples R China
5.Univ Chinese Acad Sci, Zhejiang Canc Res Inst, Canc Hosp, Zhejiang Canc Hosp, Hangzhou 310022, Peoples R China
推荐引用方式
GB/T 7714		 Li, Na,Yang, Chenxi,Zhou, Sicheng,et al. Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma[J]. DIAGNOSTICS,2021,11.
APA Li, Na.,Yang, Chenxi.,Zhou, Sicheng.,Song, Siyu.,Jin, Yuyao.,...&Chen, Zhongjian.(2021).Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma.DIAGNOSTICS,11.
MLA Li, Na,et al."Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma".DIAGNOSTICS 11(2021).

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来源：合肥物质科学研究院

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