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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)....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-7940372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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