Exploration of models of radiosensitive lipid metabolites of human plasma based on multiple machine learning algorithms
10.3760/cma.j.cn112271-20231228-00225
- VernacularTitle:基于多种机器学习算法的人血浆辐射敏感脂质代谢物模型探索
- Author:
Qi CHEN
1
;
Hua ZHAO
;
Tianjing CAI
;
Yizhe GAO
;
Ling GAO
;
Qingjie LIU
Author Information
1. 中国疾病预防控制中心辐射防护与核安全医学所 辐射防护与核应急中国疾病预防控制中心重点实验室，北京 100088
- Keywords:
Ionizing radiation;
Lipidomics;
Machine learning;
Random forest
- From:
Chinese Journal of Radiological Medicine and Protection
2024;44(6):457-463
- CountryChina
- Language:Chinese
-
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.
