Knowledge Management System of Hefei Institute of Physical Science,CAS
Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients | |
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 | |
2022-11-01 | |
发表期刊 | CLINICA CHIMICA ACTA |
ISSN | 0009-8981 |
通讯作者 | Fu, Zhixuan(fuzx@zjcc.org.cn) ; Chen, Zhongjian(chenzj@zjcc.org.cn) |
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. |
关键词 | Lipidomics Diagnosis Prognosis ROC Machine learning |
DOI | 10.1016/j.cca.2022.09.002 |
关键词[WOS] | BIOMARKERS ; METABOLISM ; STRATEGY ; RISK |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Medical Laboratory Technology |
WOS类目 | Medical Laboratory Technology |
WOS记录号 | WOS:000875703900001 |
出版者 | ELSEVIER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.hfcas.ac.cn:8080/handle/334002/129806 |
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). |
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