Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine ma...

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Autores principales:	Runbin Sun, Haokai Zhao, Shuzhen Huang, Ran Zhang, Zhenyao Lu, Sijia Li, Guangji Wang, Jiye Aa, Yuan Xie
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	liver regeneration partial hepatectomy metabolomics machine learning GC/MS Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/f5c1f39b61f74368ac8f76932d7e4c6c
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spelling	oai:doaj.org-article:f5c1f39b61f74368ac8f76932d7e4c6c2021-12-01T18:46:19ZPrediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy1663-981210.3389/fphar.2021.760474https://doaj.org/article/f5c1f39b61f74368ac8f76932d7e4c6c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphar.2021.760474/fullhttps://doaj.org/toc/1663-9812Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R2) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.Runbin SunRunbin SunHaokai ZhaoShuzhen HuangRan ZhangZhenyao LuSijia LiGuangji WangJiye AaYuan XieFrontiers Media S.A.articleliver regenerationpartial hepatectomymetabolomicsmachine learningGC/MSTherapeutics. PharmacologyRM1-950ENFrontiers in Pharmacology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	liver regeneration partial hepatectomy metabolomics machine learning GC/MS Therapeutics. Pharmacology RM1-950
spellingShingle	liver regeneration partial hepatectomy metabolomics machine learning GC/MS Therapeutics. Pharmacology RM1-950 Runbin Sun Runbin Sun Haokai Zhao Shuzhen Huang Ran Zhang Zhenyao Lu Sijia Li Guangji Wang Jiye Aa Yuan Xie Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
description	Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R2) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.
format	article
author	Runbin Sun Runbin Sun Haokai Zhao Shuzhen Huang Ran Zhang Zhenyao Lu Sijia Li Guangji Wang Jiye Aa Yuan Xie
author_facet	Runbin Sun Runbin Sun Haokai Zhao Shuzhen Huang Ran Zhang Zhenyao Lu Sijia Li Guangji Wang Jiye Aa Yuan Xie
author_sort	Runbin Sun
title	Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
title_short	Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
title_full	Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
title_fullStr	Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
title_full_unstemmed	Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy
title_sort	prediction of liver weight recovery by an integrated metabolomics and machine learning approach after 2/3 partial hepatectomy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f5c1f39b61f74368ac8f76932d7e4c6c
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Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy

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