AUTHOR=Chao-yang Gong , Rong Tang , Yong-qiang Shi , Tai-cong Liu , Kai-sheng Zhou , Wei Nan , Hai-hong Zhang 

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

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.644220

DOI=10.3389/fcell.2021.644220

ISSN=2296-634X

ABSTRACT=In this study, data of osteosarcoma (OS) tissue and normal muscle tissue from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) (n =84) and GTEx database (n =396), eight survival‐related metabolic genes were identified in differentially expressed metabolic genes by univariate Cox regression analysis. Six metabolic genes were screened to prognostic signature by LASSO regression analysis. The metabolism gene signature has a good performance in predicting survival of OS patients and is also an independent prognostic factor. Next, eight metabolism‐related long non‐coding RNAs (lncRNAs) were also identified the metabolism‐related lncRNA signature and can accurately predict overall survival for. Gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) showed that multiple metabolism processes and signalling pathways are enriched in the high‐risk and low‐risk group. Immunization scores analysis showed that there is lower score in high-risk group than low-risk groups. These results showed that Six metabolic genes and eight metabolism‐related prognostic lncRNAs signature have good performance in predicting the survival outcomes of OS patients.