Dataset Information

Identification of key genes and pathways in Ewing's sarcoma patients associated with metastasis and poor prognosis.

ABSTRACT: 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.

SUBMITTER: Li G

PROVIDER: S-EPMC6549663 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of key genes and pathways in Ewing's sarcoma patients associated with metastasis and poor prognosis.

Li Guoqi G Zhang Piao P Zhang Wenkan W Lei Zhong Z He Jiaming J Meng Jiahong J Di Tuoyu T Yan Weiqi W

OncoTargets and therapy 20190527

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 wer ...[more]

PMID: 31213834

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Project description: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.

Project description:Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. "Gene ontology (GO)" and "Kyoto Encyclopedia of Genes and Genomes (KEGG)" data were analyzed by the "DAVID" software. The online search tool "STRING" was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using "GEPIA" with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.

Project description:BackgroundGenomic profiling can be used to identify the predictive effect of genomic subsets for determining prognosis in bladder urothelial carcinoma (BUC) after radical cystectomy. This study aimed to investigate potential gene and pathway markers associated with prognosis in BUC.MethodsA microarray dataset of BUC was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified by DESeq of the R platform. Kaplan-Meier analysis was applied for prognostic markers. Key pathways and genes were identified using bioinformatics tools, such as gene set enrichment analysis, gene ontology, the Kyoto Encyclopedia of Genes and Genomes, gene multiple association network integration algorithm (GeneMANIA), Search Tool for the Retrieval of Interacting Genes/Proteins, and Molecular Complex Detection.ResultsA comparative gene set enrichment analysis of tumor and adjacent normal tissues suggested BUC tumorigenesis resulted mainly from enrichment of cell cycle and DNA damage and repair-related biological processes and pathways, including TP53 and mitotic recombination. Two hundred and fifty-six genes were identified as potential prognosis-related DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the potential prognosis-related DEGs were enriched in angiogenesis, including the cyclic adenosine monophosphate biosynthetic process, cyclic guanosine monophosphate-protein kinase G, mitogen-activated protein kinase, Rap1, and phosphoinositide-3-kinase-AKT signaling pathway. Nine hub genes, TAGLN, ACTA2, MYH11, CALD1, MYLK, GEM, PRELP, TPM2, and OGN, were identified from the intersection of protein-protein interaction and GeneMANIA networks. Module analysis of protein-protein interaction and GeneMANIA networks mainly showed enrichment of the cyclic guanosine monophosphate-protein kinase G signaling pathway, angiogenesis, cell proliferation, and differentiation, which are associated with tumor angiogenesis and cancer prognosis.ConclusionGenes and pathways related to cell cycle and DNA damage and repair may play a crucial role in BUC pathogenesis, whereas those pertaining to tumor angiogenesis may be key factors in influencing BUC prognosis, especially in advanced disease stages.

Project description:Ewing's sarcoma is a high-grade malignancy bone and soft tissue tumor that most commonly occurs in children and adolescents. Although the overall prognosis of Ewing's sarcoma has improved, the 5-year survival rate has not improved significantly. The study aimed to determine the risk factors independently associated with the prognosis of Ewing's sarcoma and to construct a nomogram to predict patient survival. Patients diagnosed with Ewing's sarcoma were collected from the Surveillance, Epidemiology, and End Results program database between 2004 and 2015 and further divided into training and validation cohort. Univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors. The nomogram was used to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). Finally, the nomogram was verified internally and externally through the training and validation cohorts, and the predictive capability was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve and compared with that of the 7th TNM stage. A total of 1120 patients were divided into training (n = 713) and validation (n = 407) cohorts. Based on the multivariate analysis of the training cohort, a nomogram that integrated age, tumor size, primary site, N stage, and M stage was constructed (P < 0.05). The predicted C-indexes of OS and CSS of the training cohort were 0.744 (95% CI 0.717-0.771) and 0.743 (95% CI 0.715-0.770), respectively. However, the TNM stage had a C-index of 0.695 (95% CI 0.666-0.724) and 0.698 (95% CI 0.669-0.727) for predicting OS and CSS, respectively. The nomogram showed higher C-indexes than those in the TNM stage. Furthermore, the internal and external calibration curves showed good consistency between the predicted and observed values. Age, tumor size, primary site, N stage, and M stage are independent risk factors affecting the OS and CSS in Ewing's sarcoma patients. Compared with the 7th TNM staging, the nomogram consisting of these factors was more accurate for risk assessment and survival prediction in patients with Ewing's sarcoma, thus providing a novel reliable tool for risk assessment and survival prediction in Ewing's sarcoma patients.

Project description:BackgroundCuproptosis is copper-induced cell death. Copper metabolism related genes (CMRGs) were demonstrated that used to assess the prognosis out of tumors. In the study, CMRGs were tested for their effect on TME cell infiltration in Ewing's sarcoma (ES).MethodsThe GEO and ICGC databases provided the mRNA expression profiles and clinical features for downloading. In the GSE17674 dataset, 22prognostic-related copper metabolism related genes (PR-CMRGs) was identified by using univariate regression analysis. Subsequently, in order to compare the survival rates of groups with high and low expression of these PR-CMRGs,Kaplan-Meier analysis was implemented. Additionally, correlations among them were examined. The study employed functional enrichment analysis to investigate probable underlying pathways, while GSVA was applied to evaluate enriched pathways in the ES (Expression Set). Through an unsupervised clustering algorithm, samples were classified into two clusters, revealing significant differences in survival rates and levels of immune infiltration.ResultsUsing Lasso and step regression methods, five genes (TFRC, SORD, SLC11A2, FKBP4, and AANAT) were selected as risk signatures. According to the Kaplan-Meier survival analysis, the high-risk group had considerably lower survival rates than the low-risk group(p=6.013e-09). The area under the curve (AUC) values for the receiver operating characteristic (ROC) curve were 0.876, 0.883, and 0.979 for 1, 3, and 5 years, respectively. The risk model was further validated in additional datasets, namely GSE63155, GSE63156, and the ICGC datasets. To aid in outcome prediction, a nomogram was developed that incorporated risk levels and clinical features. This nomogram's performance was effectively validated through calibration curves.Additionally, the study evaluated the variations in immune infiltration across different risk groups, as well as high-expression and low-expression groups. Importantly, several drugs were identified that displayed sensitivity, offering potential therapeutic options for ES.ConclusionThe findings above strongly indicate that CMRGs play crucial roles in predicting prognosis and immune status in ES.

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Dataset Information

Identification of key genes and pathways in Ewing's sarcoma patients associated with metastasis and poor prognosis.

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Identification of key genes and pathways in Ewing's sarcoma patients associated with metastasis and poor prognosis.

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