Abstract
Objective The purpose of this study is to identify novel biomarkers for the prognosis of Ewing's sarcoma based on bioinformatics analysis. Methods The GSE63157 and GSE17679 datasets contain patient and healthy control microarray data that were downloaded from the Gene Expression Omnibus (GEO) database and analyzed through R language software to obtain differentially expressed genes (DEGs). Firstly, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, protein-protein interaction (PPI) networks, and Cytoscape Molecular Complex Detection (MCODE) plug-in were then used to compute the highest scores of the module. After survival analysis, the hub genes were lastly obtained from the two module genes. Results A total of 1181 DEGs were identified from the two GSEs. Through MCODE and survival analysis, we obtain 53 DEGs from the module and 29 overall survival- (OS-) related genes. ZBTB16 was the only downregulated gene after Venn diagrams. Survival analysis indicates that there was a significant correlation between the high expression of ZBTB16 and the OS of Ewing's sarcoma (ES), and the low expression group had an unfavorable OS when compared to the high expression group. Conclusions High expression of ZBTB16 may serve as a predictor biomarker of poor prognosis in ES patients.
Highlights
Ewing’s sarcoma (ES) is the second most common sarcoma of bone and soft tissue, usually occurring in children and adolescents [1]
Genes and Genomes (KEGG) functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction of pivot genes, and identification of key genes that are significantly related to overall survival (OS) in ES are aimed at providing therapeutic strategies for the prognosis of ES
Univariate and multivariate Cox regression analyses were performed through using the R “Survival” package to identify the differentially expressed genes (DEGs) associated with the OS time and clinical characteristics of ES, and the hub genes that could independently guide the prognosis were found by taking the overlap with module genes
Summary
The purpose of this study is to identify novel biomarkers for the prognosis of Ewing’s sarcoma based on bioinformatics analysis. The hub genes were lastly obtained from the two module genes. A total of 1181 DEGs were identified from the two GSEs. Through MCODE and survival analysis, we obtain 53 DEGs from the module and 29 overall survival- (OS-) related genes. ZBTB16 was the only downregulated gene after Venn diagrams. Survival analysis indicates that there was a significant correlation between the high expression of ZBTB16 and the OS of Ewing’s sarcoma (ES), and the low expression group had an unfavorable OS when compared to the high expression group. High expression of ZBTB16 may serve as a predictor biomarker of poor prognosis in ES patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
