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Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients

文献类型：期刊论文

作者Yang, Chenxi1,2; Zhou, Sicheng1,2; Zhu, Jing1,2; Sheng, Huaying1,2; Mao, Weimin1,2,3; Fu, Zhixuan1,2,4; Chen, Zhongjian1,2,3,4
刊名CLINICA CHIMICA ACTA
出版日期2022-11-01
卷号536
关键词Lipidomics Diagnosis Prognosis ROC Machine learning
ISSN号0009-8981
DOI10.1016/j.cca.2022.09.002
通讯作者Fu, Zhixuan(fuzx@zjcc.org.cn) ; Chen, Zhongjian(chenzj@zjcc.org.cn)
英文摘要Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening method for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic method for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivar-iate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modeling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP (d15:0_22:0 + O) showed fine predictive accuracy in single-lipid-based ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95 % CI: 0.8806, 1.0000), that can be reached by modeling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC (17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that have potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modeling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid in clinical diagnosis of colorectal cancer.
WOS关键词BIOMARKERS ; METABOLISM ; STRATEGY ; RISK
资助项目Projects of Zhejiang Province Medical and Health Science and Technology Plan[2022KY622] ; Projects of Zhejiang Province Medical and Health Science and Technology Plan[2018KY300] ; Projects of Zhejiang Province Medical and Health Science and Technology Plan[2018KY316] ; Key R&D Program Projects in Zhejiang Province[2018C04009] ; National Natural Science Foundation of China[83172210]
WOS研究方向Medical Laboratory Technology
语种英语
WOS记录号WOS:000875703900001
出版者ELSEVIER
资助机构Projects of Zhejiang Province Medical and Health Science and Technology Plan ; Key R&D Program Projects in Zhejiang Province ; National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/129805]  
专题中国科学院合肥物质科学研究院
通讯作者Fu, Zhixuan; Chen, Zhongjian
作者单位1.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp, Hangzhou, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Basic Med & Canc IBMC, Hangzhou, Zhejiang, Peoples R China
3.Zhejiang Key Lab Diag & Treatment Technol Thorac O, Hangzhou, Zhejiang, Peoples R China
4.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Chenxi,Zhou, Sicheng,Zhu, Jing,et al. Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients[J]. CLINICA CHIMICA ACTA,2022,536.
APA Yang, Chenxi.,Zhou, Sicheng.,Zhu, Jing.,Sheng, Huaying.,Mao, Weimin.,...&Chen, Zhongjian.(2022).Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients.CLINICA CHIMICA ACTA,536.
MLA Yang, Chenxi,et al."Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients".CLINICA CHIMICA ACTA 536(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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