Prognostic Immune-Related Genes of Patients With Ewing's Sarcoma.
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ABSTRACT:
SUBMITTER: Zhou Y
PROVIDER: S-EPMC8194304 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveBy combining the expression profiles of metabolism-related genes (MRGS) with clinical information, the expression quantities of MRGS and the influence on development and prognosis were systematically analyzed, so as to provide a theoretical basis for the clinical study on the prognosis of Ewing's sarcoma.MethodsMRGs expression profiles of 64 patients with Ewing's sarcoma were obtained from GEO dataset. Univariate Cox regression analysis was used to identify metabolization-related differentially expressed genes (DEGs) related with prognosis in Ewing's sarcoma patients. Then, multivariate Cox analysis was used to calculate novel prognostic markers based on metabolism-related DEGs. Besides, We validate the model using ICGC datasets. Finally, the new prognostic index was verified on the basis of the prognostic models.ResultsMultivariate Cox regression analysis identified 74 metabolization-related DEGs, 25 of which were associated with Ewing's sarcoma patients' overall survival. Subsequently, we used 25 DEGs to construct metabolism-related prognostic signature for patients with Ewing's sarcoma. Based on the 18 DEGs regression coefficient, we propose the formula of each patient's risk score, and then divided the patients into high-risk group and low-risk group. The results indicated that the survival rate and survival time were higher in the low-risk group and lower in the high-risk group. Multivariate Cox analysis showed that risk score index was an independent prognostic factor for Ewing's sarcoma.ConclusionThe experimental results suggest that the 18 metabolism-related DEGs marker may be effective in predicting the prognosis of Ewing's sarcoma to some extent, helping to individualize treatment of patients at different risks.
| S-EPMC8461378 | biostudies-literature
Project description:IntroductionOwing to the poor prognosis of Ewing's sarcoma, reliable prognostic biomarkers are highly warranted for clinical diagnosis of the disease.Materials and methodsA combination of the weighted correlation network analysis and differentially expression analysis was used for initial screening; glycolysis-related genes were extracted and subjected to univariate Cox, LASSO regression, and multivariate Cox analyses to construct prognostic models. The immune cell composition of each sample was analysed using CIBERSORT software. Immunohistochemical analysis was performed for assessing the differential expression of modelled genes in Ewing's sarcoma and paraneoplastic tissues.ResultsA logistic regression model constructed for the prognosis of Ewing's sarcoma exhibited that the patient survival rate in the high-risk group is much lower than in the low-risk group. CIBERSORT analysis exhibited a strong correlation of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages (P < 0.05). Immunohistochemical analysis confirmed the study findings.ConclusionsGLCE and TPI1 can be used as prognostic biomarkers to predict the prognosis of Ewing's sarcoma, and a close association of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages provides a novel approach to the disease immunotherapy.
| S-EPMC8312448 | biostudies-literature
Project description:BackgroundImmune-related genes (IRGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of IRGs and their clinical significance in soft tissue sarcoma (STS) patients is lacking.MethodsGene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed immune-related genes (DEIRGs) were determined by matching the DEG and ImmPort gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEIRGs was conducted, and associations with prognosis, the tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for progression free survival (PFS), were established and validated in an independent set. Finally, two transcription factor (TF)-IRG regulatory networks were constructed, and a crucial regulatory axis was validated.ResultsIn total, 364 DEIRGs and four clusters were identified. OS, TME scores, five immune checkpoints, and 12 types of immune cells were found to be significantly different among the four clusters. The two prognostic signatures incorporating 20 DEIRGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.879 (95%CI 0.832 ~ 0.926) and 0.825 (95%CI 0.776 ~ 0.874) for the OS and PFS signatures, respectively. Finally, TF-IRG regulatory networks were established, and the MYH11-ADM regulatory axis was verified in three independent datasets.ConclusionThis comprehensive analysis of the IRG landscape in soft tissue sarcoma revealed novel IRGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.
| S-EPMC7465445 | biostudies-literature
Project description:ObjectiveThe purpose of this study is to identify novel biomarkers for the prognosis of Ewing's sarcoma based on bioinformatics analysis.MethodsThe 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.ResultsA 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.ConclusionsHigh expression of ZBTB16 may serve as a predictor biomarker of poor prognosis in ES patients.
| S-EPMC8514890 | biostudies-literature
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.
| S-EPMC11267503 | biostudies-literature
Project description:AimImmune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).MethodsImmune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.ResultsWe selected 255 patients with SARC for our analysis. The Kaplan-Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.ConclusionThe current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.
| S-EPMC7882740 | biostudies-literature
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.
| S-EPMC6199211 | biostudies-literature
Project description:Ewing's sarcoma (ES) is the second most common bone tumor among children and adolescents worldwide. However, the genes and signaling pathways involved in ES tumorigenesis and progression remain unclear. The present study used two gene-expression profile datasets (GSE17674 and GSE31215) to elucidate key potential candidate genes and pathways in ES. Differentially expressed genes (DEGs) were identified and a functional enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed, and the most significant module in the PPI network was selected from the Search Tool for the Retrieval of Interacting Genes/Proteins database. A total of 278 genes were identified by comparing the tumor samples with non-cancerous samples; these included 272 upregulated and 6 downregulated genes. The pathway analysis demonstrated significant enrichment in the positive regulation of transcription in the DEGs coding for RNA polymerase II promoter, plasma membrane and chromatin binding pathways in cancer in general. There were 269 nodes and 292 edges in the PPI network. Finally, MYC, IGF1, OAS1, EZH2 and ISG15 were identified as the hub genes according to the degree levels. The survival analysis revealed that EZH2 is associated with a poor prognosis in patients with ES. In conclusion, the DEGs, associated pathways and hub genes identified in the present study help elucidate the underlying molecular mechanisms of ES carcinogenesis and progression, and provide potential molecular targets and biomarkers for ES.
| S-EPMC6865160 | biostudies-literature
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.
| S-EPMC6549663 | biostudies-literature
Project description:Ewing's sarcoma has a poor prognosis and high metastasis rate; thus, it is critical to explore prognostic biomarkers of m6A-related genes. Two datasets were downloaded from the Gene Expression Omnibus database, m6A-related genes were extracted, and prognostic models were constructed using the least absolute shrinkage and selection operator and multivariate COX regression analyses. Immune cell composition and drug sensitivity analyses were performed, and our analysis was validated using laboratory methods of immunohistochemical specific staining and qRT-PCR. Ewing's sarcoma prognostic model demonstrated that the survival rate of cases in the high-risk group was much lower than that of the low-risk group. Naïve B cells, macrophages M0, macrophages M1, and resting mast cells are closely associated with Ewing's sarcoma. METTL14 and YTHDF2 are strongly associated with multiple drug sensitivity. Immunohistochemical specific staining revealed higher expression of both METTL14 and YTHDF2 in Ewing's sarcoma than in the paraneoplastic tissues. The results of qRT-PCR showed that METTL14 expression was significantly higher in both ES cell lines than in the control cell line. The prognostic model constructed using m6A-related genes METTL14 and TYHDF2, can be a potential prognostic biomarker for Ewing's sarcoma, with the survival rate of cases in the high-risk group being much lower than that of the low-risk group.
| S-EPMC9054816 | biostudies-literature