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Identification of driver genes associated with chemotherapy resistance of Ewing’s sarcoma

Authors Liao H, Xie X, Xu Y, Huang G

Received 25 April 2018

Accepted for publication 3 August 2018

Published 15 October 2018 Volume 2018:11 Pages 6947—6956


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Carlos E Vigil

Hongyi Liao,1,* Xianbiao Xie,2,* Yuanyuan Xu,3 Gang Huang2

1Department of Orthopedic Surgery, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People’s Republic of China; 2Department of Orthopedic Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Pediatrics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China

*These authors contributed equally to this work

Background: The aim of this study was to identify the driver genes associated with chemotherapy resistance of Ewing’s sarcoma and potential targets for Ewing’s sarcoma treatment.
Methods: Two mRNA microarray datasets, GSE12102 and GSE17679, were downloaded from the Gene Expression Omnibus database, which contain 94 human Ewing’s sarcoma samples, including 65 from those who experienced a relapse and 29 from those with no evidence of disease. The differen­tially expressed genes (DEGs) were identified using LIMMA package R. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed for DEGs using Database for Annotation, Visualization and Integrated Analysis. The protein–protein interaction network was constructed using Cytoscape software, and module analysis was performed using Molecular Complex Detection.
Results: A total of 206 upregulated DEGs and 141 downregulated DEGs were identified. Upregulated DEGs were primarily enriched in DNA replication, nucleoplasm and protein kinase binding for biological processes, cellular component and molecular functions, respectively. Downregulated DEGs were predominantly involved in receptor clustering, membrane raft, and ligand-dependent nuclear receptor binding. The protein–protein interaction network of DEGs consisted of 150 nodes and 304 interactions. Thirteen hub genes were identified, and biological analysis revealed that these genes were primarily enriched in cell division, cell cycle, and mitosis. Furthermore, based on closeness centrality, betweenness centrality, and degree centrality, the three most significant genes were identified as GAPDH, AURKA, and EHMT2. Furthermore, the significant network module was composed of nine genes. These genes were primarily enriched in mitotic nuclear division, mitotic chromosome condensation, and nucleoplasm.
Conclusion: These hub genes, especially GAPDH, AURKA, and EHMT2, may be closely associated with the progression of Ewing’s sarcoma chemotherapy resistance, and further experiments are needed for confirmation.

Keywords: Ewing’s sarcoma, chemotherapy resistance, differentially expressed gene, enrichment analysis, network

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