Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
