Research Paper Volume 13, Issue 13 pp 17516—17535
Glycolysis- and immune-related novel prognostic biomarkers of Ewing's sarcoma: glucuronic acid epimerase and triosephosphate isomerase 1
- 1 The First Clinical Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
Received: December 21, 2020 Accepted: May 13, 2021 Published: July 7, 2021https://doi.org/10.18632/aging.203242
How to Cite
Copyright: © 2021 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Introduction: Owing to the poor prognosis of Ewing's sarcoma, reliable prognostic biomarkers are highly warranted for clinical diagnosis of the disease.
Materials and Methods: A combination of the weighted correlation network analysis and differentially expression analysis was used for initial screening; glycolysis-related genes were extracted and subjected to univariate Cox, LASSO regression, and multivariate Cox analyses to construct prognostic models. The immune cell composition of each sample was analysed using CIBERSORT software. Immunohistochemical analysis was performed for assessing the differential expression of modelled genes in Ewing's sarcoma and paraneoplastic tissues.
Results: A logistic regression model constructed for the prognosis of Ewing's sarcoma exhibited that the patient survival rate in the high-risk group is much lower than in the low-risk group. CIBERSORT analysis exhibited a strong correlation of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages (P < 0.05). Immunohistochemical analysis confirmed the study findings.
Conclusions: GLCE and TPI1 can be used as prognostic biomarkers to predict the prognosis of Ewing's sarcoma, and a close association of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages provides a novel approach to the disease immunotherapy.
DEGs: differentially expressed genes; WGCNA: weighted gene co-expression network analysis; FDR: false discovery rate; GO: Gene Ontology; GSEA: Gene Set Enrichment Analysis; KEGG: KEGG enrichment analysis; LASSO: least absolute shrinkage and selection operator.