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Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients

In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396)....

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Autores principales: Chao-yang, Gong, Rong, Tang, Yong-qiang, Shi, Tai-cong, Liu, Kai-sheng, Zhou, Wei, Nan, Hai-hong, Zhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940372/
https://www.ncbi.nlm.nih.gov/pubmed/33708772
http://dx.doi.org/10.3389/fcell.2021.644220
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author Chao-yang, Gong
Rong, Tang
Yong-qiang, Shi
Tai-cong, Liu
Kai-sheng, Zhou
Wei, Nan
Hai-hong, Zhang
author_facet Chao-yang, Gong
Rong, Tang
Yong-qiang, Shi
Tai-cong, Liu
Kai-sheng, Zhou
Wei, Nan
Hai-hong, Zhang
author_sort Chao-yang, Gong
collection PubMed
description In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396). We also constructed a six metabolic gene signature to predict the overall survival of osteosarcoma (OS) patients using least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Our results show that the six metabolic gene signature showed good performance in predicting survival of OS patients and was also an independent prognostic factor. Stratified correlation analysis showed that the metabolic gene signature accurately predicted survival outcomes in high-risk and low-risk OS patients. The six metabolic gene signature was also verified to perform well in predicting survival of OS patients in an independent cohort (GSE21257). Then, using univariate Cox regression and Lasso Cox regression analyses, we identified an eight metabolism-related long noncoding RNA (lncRNA) signature that accurately predicts overall survival of OS patients. Gene set variation analysis showed that the apical surface and bile acid metabolism, epithelial mesenchymal transition, and P53 pathway were activated in the high-risk group based on the eight metabolism-related lncRNA signature. Furthermore, we constructed a competing endogenous RNA (ceRNA) network and conducted immunization score analysis based on the eight metabolism-related lncRNA signature. These results showed that the six metabolic gene signature and eight metabolism-related lncRNA signature have good performance in predicting the survival outcomes of OS patients.
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spelling pubmed-79403722021-03-10 Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients Chao-yang, Gong Rong, Tang Yong-qiang, Shi Tai-cong, Liu Kai-sheng, Zhou Wei, Nan Hai-hong, Zhang Front Cell Dev Biol Cell and Developmental Biology In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396). We also constructed a six metabolic gene signature to predict the overall survival of osteosarcoma (OS) patients using least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Our results show that the six metabolic gene signature showed good performance in predicting survival of OS patients and was also an independent prognostic factor. Stratified correlation analysis showed that the metabolic gene signature accurately predicted survival outcomes in high-risk and low-risk OS patients. The six metabolic gene signature was also verified to perform well in predicting survival of OS patients in an independent cohort (GSE21257). Then, using univariate Cox regression and Lasso Cox regression analyses, we identified an eight metabolism-related long noncoding RNA (lncRNA) signature that accurately predicts overall survival of OS patients. Gene set variation analysis showed that the apical surface and bile acid metabolism, epithelial mesenchymal transition, and P53 pathway were activated in the high-risk group based on the eight metabolism-related lncRNA signature. Furthermore, we constructed a competing endogenous RNA (ceRNA) network and conducted immunization score analysis based on the eight metabolism-related lncRNA signature. These results showed that the six metabolic gene signature and eight metabolism-related lncRNA signature have good performance in predicting the survival outcomes of OS patients. Frontiers Media S.A. 2021-02-23 /pmc/articles/PMC7940372/ /pubmed/33708772 http://dx.doi.org/10.3389/fcell.2021.644220 Text en Copyright © 2021 Chao-yang, Rong, Yong-qiang, Tai-cong, Kai-sheng, Wei and Hai-hong. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Chao-yang, Gong
Rong, Tang
Yong-qiang, Shi
Tai-cong, Liu
Kai-sheng, Zhou
Wei, Nan
Hai-hong, Zhang
Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title_full Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title_fullStr Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title_full_unstemmed Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title_short Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients
title_sort prognostic signatures of metabolic genes and metabolism-related long non-coding rnas accurately predict overall survival for osteosarcoma patients
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940372/
https://www.ncbi.nlm.nih.gov/pubmed/33708772
http://dx.doi.org/10.3389/fcell.2021.644220
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