Dataset Information

Identification of driver genes associated with chemotherapy resistance of Ewing's sarcoma.

ABSTRACT: 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 differen tially 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.

SUBMITTER: Liao H

PROVIDER: S-EPMC6199211 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of driver genes associated with chemotherapy resistance of Ewing's sarcoma.

Liao Hongyi H Xie Xianbiao X Xu Yuanyuan Y Huang Gang G

OncoTargets and therapy 20181015

<h4>Background</h4>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.<h4>Methods</h4>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 differen tially expressed genes (DEGs) ...[more]

PMID: 30410352

Similar Datasets

Project description:Background: Ewing sarcoma (ES) is the second commonest primary malignant bone neoplasm. Metastatic status at diagnosis strongly predicted poor prognosis of Ewing sarcoma patients. Yet little was known about the underlying mechanism of ES metastasis. Purpose:This study intended to identify the relationship between key genes/pathways and metastasis/poor prognosis in Ewing's sarcoma patients by using bioinformatic method. Methods: In this study, multi-center sequencing data were obtained from the GEO database, including gene and miRNA expression profile and prognosis information of ES patients. Differentially expressed genes (DEGs) were identified between primary and metastasis ES samples by the GEO2R online tool. Gene ontology (Go) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses of DEGs were performed. And PPI network analyses were conducted. The ES patient's prognostic information was employed for survival analysis, and the potential relationship between miRNAs and key genes was analyzed. Results: The results showed that a total of 298 and 428 DEGs were screened out in metastasis samples based on GSE17618 and GSE12102 dataset compared to primary samples respectively. The most significantly enriched KEGG pathway was the mismatch repair (MMR) pathway. MSH2, MSH6, RPA2, and RFC2 that belong to the MMR pathway were identified as key genes. Moreover, the expression of key genes was increased in metastasis samples compared with primary ones and was associated with poor event-free and overall survival of ES patients. The negative correlation of the expression level of the key genes with patients prognosis also supported by TCGA sarcoma database. Furthermore, knockdown of EWSR/FLI1 fusion in ES cell line A673 down-regulates the expression of the 4 key genes was revealed by GDS4962. Conclusion: In conclusion, the present study indicated that the key genes promote our understanding of the molecular mechanisms underlying the development of ES metastasis, and might be used as molecular targets and diagnostic biomarkers for the treatment of ES.

Project description:IGF system contributes significantly to many human malignancies. Targeting IGF-I receptor (IGF-IR) has been reported to be active against several tumors, but particular efficacy was observed only against a minority of Ewing's sarcoma patients. Identification of mechanisms of acquired resistance to anti-IGF-IR agents is mandatory to individualize their use in clinics and optimize cure costs. In this study, we compared gene expression profiles of cells made resistant with three different anti-IGF-IR drugs (human antibodies AVE1642, Figitumumab, or tyrosine kinase inhibitor NVP-AEW541) to highlight common and distinctive mechanisms of resistance. Among common mechanisms, we identified two molecular signatures that distinguish sensitive from resistant cells. Annotation analysis indicated some common altered pathways, such as insulin signaling, MAPK pathway, endocytosis, and modulation of some members of the interferon-induced transmembrane protein family. Among distinctive pathways/processes, resistance to human antibodies involves mainly genes regulating neural differentiation and angiogenesis, whereas resistance to NVP-AEW541 is mainly associated with alterations in genes concerning inflammation and antigen presentation. Evaluation of the common altered pathways indicated that resistant cells seem to maintain intact the IGF-IR internalization/degradation route of sensitive cells but constantly down-regulated its expression. In resistant cells, the loss of proliferative stimulus, normally sustained by IGF-I/IGF-IR autocrine loop in Ewing's sarcoma cells, is compensated by transcriptional up-regulation of IGF-II and insulin receptor-A; this signaling seems to favor the MAPK pathway over the v-akt murine thymoma viral oncogene homolog 1 pathway. Overall, complexity of IGF system requires analytical evaluation of its components to select those patients that may really benefit from this targeted therapy and support the idea of cotargeting IGF-IR and insulin receptor-A to increase the efficacy.

Dataset Information

Identification of driver genes associated with chemotherapy resistance of Ewing's sarcoma.

Publications

Identification of driver genes associated with chemotherapy resistance of Ewing's sarcoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets