Abstract
BACKGROUND: Ewing's sarcoma (ES) is one of the most prevalent malignant bone tumors worldwide. However, the molecular mechanisms of the genes and signaling pathways of ES are still not well sufficiently comprehended. To identify candidate genes involved in the development and progression of ES, the study screened for key genes and biological pathways related to ES using bioinformatics methods. METHODS: The GSE45544 and GSE17618 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. A protein-protein interaction (PPI) network was built, and key module analysis was performed using STRING and Cytoscape. A core-gene was gained and was validated by the validation dataset GSE67886 and immunohistochemistry (IHC). The diagnostic value and prognosis evaluation of ES were executed using, respectively, the ROC approach and Cox Regression. RESULTS: A total of 187 DEGs, consisting of 56 downregulated genes and 131 upregulated genes, were identified by comparing the tumor samples to normal samples. The enriched functions and pathways of the DEGs, including cell division, mitotic nuclear division, cell proliferation, cell cycle, oocyte meiosis, and progesterone-mediated oocyte maturation, were analyzed. There were 149 nodes and 1246 edges in the PPI network, and 15 hub genes were identified according to the degree levels. The core gene (UBE2T) showed high expression in ES, validated by using GSE67886 and IHC. The ROC analysis revealed UBE2T had outstanding diagnostic value in ES (AUC = 0.75 in the training set, AUC = 0.90 in the validation set). Kaplan-Meier (analysis of survival rate) and Cox Regression analyses indicated that UBE2T was a sign of adverse results for sufferers with ES. CONLUSION: UBE2T was a significant value biomarker for diagnosis and treatment of ES, thereby presenting a novel potential therapeutic target for ES as well as a new perspective for assessing the effect of treatment and prognostic prediction.