| Chen Qi,Zhao Hua,Cai Tianjing,et al.Exploration of models of radiosensitive lipid metabolites of human plasma based on multiple machine learning algorithms[J].Chinese Journal of Radiological Medicine and Protection,2024,44(6):457-463 |
| Exploration of models of radiosensitive lipid metabolites of human plasma based on multiple machine learning algorithms |
| Received:December 28, 2023 |
| DOI：10.3760/cma.j.cn112271-20231228-00225 |
| KeyWords:Ionizing radiation Lipidomics Machine learning Random forest |
| FundProject:国家自然科学基金(82173463,82003393) |
| Author Name | Affiliation | E-mail | | Chen Qi | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China
Department of Acute Infectious Disease Prevention and Control, Institute of Infectious Disease Control and Prevention, Hubei Center for Disease Control and Prevention, Wuhan 430079, China | | | Zhao Hua | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Cai Tianjing | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Gao Yizhe | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Gao Ling | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Liu Qingjie | Key Laboratory of Radiological Protection and Nuclear Emergency, China CDC, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | liuqingjie@nirp.chinacdc.cn |
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| Abstract:: |
| Objective To explore classification models for radiosensitive lipid metabolites in human peripheral blood by combining lipidomics with multiple machine learning (ML) algorithms. Methods Totally 97 peripheral blood samples were collected from 25 leukemia cases admitted to a general hospital in Beijing from March to September 2023 who were ready to undergo bone marrow transplantation, including 0 Gy blood samples before irradiation in the control group (n=24), and 73 blood samples after irradiation at doses of 4, 8 and 12 Gy in the radiation group (n=73), and the targeted lipidomic based on the ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) platform method to analyze the differences of different lipids between control and radiation groups. Then, lipids responsive to radiation doses of 0-12 Gy were identified using linear regression. Finally, classification models were constructed using five ML algorithms based on the training set, followed by the validation and evaluation of these models using the validation set. Results Compared with the control group, the differences in the concentration changes of 62 lipids in 9 classes of lipid metabolites sensitive to radiation group were statistically significant (t=-4. 91 to 4. 74, P<0. 05), including sphingomyelins(SMs), cholesteryl esters (CEs), ceramides(Cers), phosphatidylinositols(PIs), hexosylceramides(HexCers), lysophosphatidylcholines (LysoPCs), phosphatidylcholines (PCOs), phosphatidylethanolamines (PEs), and lysophosphatidylethanolamines (LysoPEs). Twenty lipids responsive to radiation doses of 0-12 Gy were identified, namely 11 SMs, 7 CEs, 1 Cer, and 1 PI. The five models based on ML algorithms of decision tree (DT), support vector machine (SVM), light gradient boosting machine (Light GBM), random forest (RF), and K-nearest neighbors (KNN) all exhibited high goodness of fit (F1=0. 69-1. 00) and high sensitivity. The evaluation and validation metrics revealed that the RF-based model yielded the optimal radiation classification discrimination (sensitivity: 1. 00; accuracy: 0. 72; F1 score: 0. 80). Conclusions Lipid metabolites responsive to radiation and lipids responsive to radiation dose in human samples were identified using targeted lipidomics. The RF-based model can provide new ideas for exploring models of human radiosensitive lipid metabolites. |
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