DataSheet_1_Plasma Metabolomics Reveals Diagnostic Biomarkers and Risk Factors for Esophageal Squamous Cell Carcinoma.pdf

Esophageal squamous carcinoma (ESCC) has a high morbidity and mortality rate. Identifying risk metabolites associated with its progression is essential for the early prevention and treatment of ESCC. A total of 373 ESCC, 40 esophageal squamous dysplasia (ESD), and 218 healthy controls (HC) subjects were enrolled in this study. Gas chromatography-mass spectrometry (GC/MS) was used to acquire plasma metabolic profiles. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate the potential diagnosis and prediction ability markers. The levels of alpha-tocopherol and cysteine were progressively decreased, while the levels of aminomalonic acid were progressively increased during the various stages (from precancerous lesions to advanced-stage) of exacerbation in ESCC patients. Alpha-tocopherol performed well for the differential diagnosis of HC and ESD/ESCC (AUROC>0.90). OR calculations showed that a high level of aminomalonic acid was not only a risk factor for further development of ESD to ESCC (OR>13.0) but also a risk factor for lymphatic metastasis in ESCC patients (OR>3.0). A low level of alpha-tocopherol was a distinguished independent risk factor of ESCC (OR0.95). In conclusion, the oxidative stress function was impaired in ESCC patients, and improving the body’s antioxidant function may help reduce the early occurrence of ESCC.</p

fon-2021-0025 - Supplementary Figure 1

Detection and verification of co-existing diagnostic markers in plasma and serum of patients with non-small cell lung cancer PCA score plots, PLS-DA score plots and validation plots for the metabolic profiling results of NSCLC and HC using plasma samples (A-C) and serum samples (D-F). (A) PCA score plot of NSCLC and HC using plasma samples. (B) PLS-DA score plot of NSCLC and HC using plasma samples. (C) Permutation test of NSCLC and HC using plasma samples. (D) PCA score plot of NSCLC and HC using serum samples. (E) PLS-DA score plot of NSCLC and HC using serum samples. (F) Permutation test of NSCLC and HC using serum samples.</div

fon-2021-0025 - Supplementary Tables

Tables - Detection and verification of co-existing diagnostic markers in plasma and serum of patients with non-small cell lung cancer Table 1. Plasma differential metabolites between NSCLC and HC groups Table 2. Serum differential metabolites between NSCLC and HC groups. Table 3. ROC analysis results of co-existing metabolites in HC and NSCLC. Table 4. Plasma differential metabolites in HC, AdC and SqCC Table 5. Serum differential metabolites in HC, AdC and SqCC. </div

Metabolomic Profiling Reveals That Reprogramming of Cerebral Glucose Metabolism Is Involved in Ischemic Preconditioning-Induced Neuroprotection in a Rodent Model of Ischemic Stroke

Ischemic tolerance renders the brain resistant to ischemia-reperfusion (I/R) injury as a result of the activation of endogenous adaptive responses triggered by various types of preconditioning. The complex underlying metabolic mechanisms responsible for the neuroprotection of cerebral ischemic preconditioning (IPC) remain elusive. Herein, gas chromatography–mass spectrometry (GC-MS) technique was applied to delineate the dynamic changes of brain metabolome in a rodent model of ischemic stroke (transient occlusion of the middle cerebral artery, tMCAO), alone or after pretreatment with nonlethal ischemic tolerance induction (transient occlusion of the bilateral common carotid arteries, tBCCAO). Metabolomic analysis showed that accumulation of glucose (concentration increased more than 4 fold) and glycolytic intermediates is the prominent feature of brain I/R-induced metabolic disturbance. IPC attenuated brain I/R damage by subduing postischemic hyperglycolysis, increasing the pentose phosphate pathway (PPP) flux and promoting the utilization of β-hydroxybutyrate. The expression analysis of pivotal genes and proteins involved in relevant metabolic pathways revealed that the downregulation of AMP-activated protein kinase (AMPK)-mediated glucose transporter-1 (GLUT-1) and 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) and reduced mRNA levels of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) subunits were associated with IPC-induced metabolic flexibility, which allows the brain to be more capable of withstanding severe I/R insults. The present study provided mechanistic insights into the metabolic signature of IPC and indicated that adaptively modulating brain glucose metabolism could be an effective approach for the therapeutic intervention of ischemic stroke

DataSheet_1_Plasma metabolomics reveals risk factors for lung adenocarcinoma.pdf

BackgroundMetabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD.MethodsA total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN.ResultsHigh levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, PConclusionsThis study reveals risk factors associated with LUAD.</p

DataSheet2_Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.docx

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.</p

Image2_Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.TIF

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.</p

Image4_Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.TIF

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.</p

Image3_Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.TIF

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.</p

DataSheet1_Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.xlsx

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.</p