Ewing sarcoma (EwS) is a malignant sarcoma which occurs in bone and soft tissues commonly happening in children with poor survival rates. Changes in cell metabolism, such as glycolysis, may provide the environment for the transformation and progression of tumors. We aimed to build a model to predict prognosis of EwS patients based on glycolysis and metabolism genes. Candidate genes were obtained by differential gene expression analysis based on GSE17679, GSE17674 and ICGC datasets. We performed GO and KEGG pathway enrichment analysis on candidate genes. Univariate Cox and LASSO Cox regression analyses were conducted to construct a model to calculate the Risk Score. GSEA was done between high-risk and low-risk groups. CIBERSORT was applied to analyze the immune landscape. We got 295 candidate glycolysis-metabolism-related genes which were enriched in 620 GO terms and 18 KEGG pathways. 12 Genes were selected by univariate Cox model and 5 of them were determined by LASSO Cox regression analysis to be used in the construction of the Risk Score model. The Risk Score could be considered as an independent prognosis factor. The immune landscape and immune checkpoints' expression significantly differed between high- and low-risk groups. Our research constructed a new glycolysis-metabolism-related genes (FABP5, EMILIN1, GLCE, PHF11 and PALM3) based prognostic signature for EwS patients and assisted in gaining insight into prognosis to improve therapies further.
Keywords: Ewing sarcoma; Glycolysis; Metabolism; Prognosis.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.