A Prognostic Model for the Overall Survival of Patients with Ewing's Sarcoma

A Prognostic Model for the Overall Survival of Patients with Ewing's Sarcoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research A Prognostic Model for the Overall Survival of Patients with Ewing's Sarcoma Xiaofei Feng, Yao Ma, Hai Lei, Wenji Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-117483/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Ewing's sarcoma (ES) is the second most common primary malignant bone tumor. Although the disease has been studied on a molecular basis, its prognosis has not improved. Therefore, the goal of this study is to screen effective biomarkers for predicting the prognosis and progression of ES. Methods: In this study, the gene expression profile of Ewing's sarcoma was downloaded from public databases. Candidate genes were screened by two methods of weighted gene co-expression network analysis (WGCNA) and differential genes expression analysis, and the Hub gene were determined by the protein–protein interaction (PPI) network. The univariate cox regression and least absolute shrinkage and the selection operator (LASSO) Cox regression were performed on the Hub gene to construct a prognostic model. The receiver operating characteristic (ROC) curve tests the predictive power of the prognostic model. Use the clinical data in the International Cancer Genome Consortium (ICGC) database for verification. Finally, Gene Set Enrichment Analysis (GSEA) was performed to obtain the biological role of prognostic genes in ES. Results: A total of 542 differentially expressed genes（DEGs） were obtained. Through WGCNA, the obtained turquoise module is significantly related to ES. PPI network analysis of candidate genes identified 10 Hub genes. The univariate cox regression results showed that CACNB1, IDH2, ATP1B4, PRKAG3, STAC3 are risk factors affecting the prognosis of ES. A prognostic model was constructed using IDH2 and PRKAG3, and patients were divided into two risk groups. The survival time of patients in the high-risk group was significantly shorter than that in the low-risk group. The ROC curve confirms that this model has certain accuracy. IGCG cohort verification yielded consistent results. GSEA results showed that IDH2 in the development of ES may promote the progression of the disease through G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING pathways, while PRKAG3 through MYOGENESIS pathway. Conclusions: This study shows that IDH2 and PRKAG3 may play important roles in the progression of ES, and can be used as therapeutic targets and prognostic evaluation biomarkers. Translational Medicine Ewing's sarcoma WGCNA prognosis biomarker Figures Figure 1 Figure 1 Figure 1 Figure 2 Figure 2 Figure 2 Figure 3 Figure 3 Figure 3 Figure 4 Figure 4 Figure 4 Figure 5 Figure 5 Figure 5 Figure 6 Figure 6 Figure 6 Figure 7 Figure 7 Figure 7 Figure 8 Figure 8 Figure 8 Background ES is an aggressive tumor originating from mesenchymal stem cells, with the highest incidence in adolescents and young adults[ 1 – 3 ]. In the past 30 years, chemotherapy, surgery, and/or radiotherapy have had significant effects on improving the survival of patients[ 1 , 4 ], but at this stage, even if intensive treatment has been taken to improve the prognosis of patients, the effect was still not significantly improved[ 5 , 6 ]. ES has a certain background in molecular research, and its therapeutic and prognostic effects are still worth expecting. Studies have shown that ES is characterized by balanced chromosomal translocation, which leads to the expression of fusion oncoproteins. The most common one is EWS/FLI to drive tumor occurrence and development[ 7 , 8 ]. However, studies have pointed out that targeted inhibitors of EWS/FLI are not clinically feasible[ 9 ]. Also, insulin-like growth factor 1 and its receptor play key roles in promoting the progression of Ewing's sarcoma[ 10 , 11 ]. However, the results of clinical trials of inhibitors of this pathway have been disappointing[ 12 , 13 ]. Nevertheless, the results still have important guiding significance for us to research the treatment of the disease on a molecular basis. Therefore, the identification of new genes and pathways related to ES occurrence and patient prognosis is crucial. In recent years, sequencing technology and bioinformatics have shown indispensable roles in the study of disease molecular mechanisms and specific biomarkers. WGCNA is a systematic biological method that can obtain gene function and gene association from the expression of the whole genome. The identification of genes that promote key roles in disease provides profound insights[ 14 ]. This method has been used to explore the molecular pathology of diseases, such as liver cancer, Laryngeal Squamous Cell Carcinoma, etc. [ 15 , 16 ]. However, there is no report on using the WGCNA method to study Ewing's sarcoma related genes and discover new prognostic markers. In this study, we constructed a prognostic model in the GSE17679 cohort and verified it in the IGCG cohort. We further performed a functional enrichment analysis of IDH2 and PRKAG3 to explore the underlying mechanism. This study provides an explanation for the pathogenesis and potential molecular biological processes of ES, and the identified genes are expected to become potential biomarkers and targets for diagnosis and treatment. Materials And Methods Data source In this study, gene expression data sets (GSE12102, GSE17679, and GSE34620) were obtained from the GEO (https://www.ncbi.nlm.nih.gov/geo/), and these data sets are all based on the GPL570 platform. The GSE12102 data set includes 37 cases of ES. The GSE17679 data set contains 88 cases of ES tissue and 18 cases of non-tumor tissue, and obtains the survival information of 88 patients. The GSE34620 data set contains 117 cases of ES tissue. The gene expression profile and survival information of 57 patients with ES were obtained from the ICGC (https://icgc.org/). Data processing and differential expression analysis Use the affy package to read in the raw data of the three data sets for background processing, quality control, and normalization processing[17]. The probe is annotated through the platform annotation file. Combine the three data sets, use the Sva package to remove the batch effects between the three data sets[18], and then use the limma package to analyze differentially expressed genes[19], the false discovery rate (FDR) 3 is used as the cut-off criterion. Weighted gene co-expression network analysis Select genes with mutation rate> 15% from the expression profile data for analysis. Use WGCNA R software package to cluster analysis of samples. Calculate the Pearson correlation coefficient of any two genes, and determine the soft threshold ability β value to screen the co-expression module. To test whether the β value conforms to the scale-free network, the logarithm of nodes with k connectivity (log(k)) should be negatively related to the logarithm of the occurrence probability of a specific node (log(P(k))), and the correlation coefficient should be greater than 0.85. The number of genes in the gene network module is set to at least 50. ES candidate genes and their function analysis Select the module with the highest correlation with the disease, take cor.geneModuleMembership> 0.8 (correlation between genes and certain clinical phenotypes) as the cut-off criterion, and the overlapping genes between the acquired genes and DEGs as ES Related candidate genes. In order to clarify the potential biological process of candidate genes, the candidate genes were analyzed by GO enrichment and KEGG pathways through clusterProfiler software package[20]. Construction of PPI and selection of Hub gene Search tool used to search for interacting genes (STRING) Online tools can be used to predict the PPI and construct a PPI network of genes with a confidence level of ≥0.7[21]. The Cytoscape software visualizes the PPI network[22], and the cytoHubba plug-in obtains the top 10 genes, which are regarded as Hub genes. Prognosis analysis In order to verify the prognostic risk of the hub gene, the hub gene was analyzed by univariate Cox regression, LASSO COX regression, multivariate Cox regression and Kaplan-Meier survival curve. Validation is done through the ICGC database. The above analysis is through the R language survival package[23], the survminer package (https://CRAN.R-project.org/package=survminer), and the glmnet package[24]. P<0.05 was considered statistically significant. Gene Set Enrichment Analysis In order to explore the biological processes involved in prognostic genes, according to the median value of each gene expression, it is divided into two groups for GSEA analysis[25], and the GSEA software (version 4.1) is used for analysis, MsigDB gene set "h.all.v7.2.symbols.gmt" as a reference[26], the number of random combinations is set to 1000, and p<0.05 is considered statistically significant. Results Differentially expressed genes in ES After preprocessing the data and removing the batch effect, the principal component analysis (PCA) method was used to display the results before and after processing (Figure 1A, B). The results show that the distribution pattern between tumor samples and normal samples is different, which can be used for further analysis. Compared with normal tissues, tumor tissues have 119 up-regulated genes and 423 down-regulated genes. The volcano map (Figure 1C) and heat map (Figure 1D) show the DEGs. Figure 1. Data processing and DEGs identification. (A) before batch effect removal; (B) after batch effect removal; (C) Volcanogram of DEGs; (D) Heat map of DEGs; Construction of co-expression network and screening of candidate genes Cluster analysis included 18 normal tissues and 242 ES samples, did not exclude any samples (Figure 2A). Choose β = 3 as the optimal soft threshold (Figure 2B, C), and scale-free R2 = 0.88 (Figure 2D, E) to ensure a scale-free network. 5 modules were identified, namely turquoise, blue, brown, green, and yellow modules (Figure 2F). We mapped the correlation between the module and the clinic. Among these modules, the turquoise module has the highest correlation with ES (correlation coefficient =-0.97, p 0.8 in the turquoise module, there are 381 in total. Then take the intersection with the DEGs. There are 305 overlapping genes as candidate genes for ES (Figure 2H). Figure 2. Construction of weighted co-expression network and screening of candidate genes. (A) Clustering dendrogram of 250 samples; (B) Determination of soft thresholds（β）=3; (C) Average connectivity of soft threshold powers; (D) When（β）= 3, the histogram of the connection distribution; (E) When（β）= 3, Check the scale-free topology. (F) The Cluster dendrogram of co-expression network modules was ordered by a hierarchical clustering of genes based on the 1-TOM matrix; (G) Heatmap of the correlation between module eigengenes and clinical traits of ES; (H) Venn diagram of candidate genes. Functional enrichment analysis of candidate genes In order to further explore the function of the candidate genes, we conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The GO analysis results show (Figure 3A) that candidate genes include muscle system process, muscle contraction, muscle organ development, muscle cell differentiation and other aspects in the biological process (BP), and the cellular component (CC) includes contractile fiber, myofibril, sarcomere, I band, etc., molecular function (MF) includes actin binding, actin filament binding, structural constituent of muscle, etc.. The results of the KEGG enrichment pathway show (Figure 3B) that candidate genes function mainly through the following pathways, such as Calcium signaling pathway, Oxytocin signaling pathway, cGMP−PKG signaling pathway, cAMP signaling pathway, Glucagon signaling pathway, etc. Figure 3. GO and KEGG enrichment analyses. (A) Top 10 GO term enrichment analysis of candidate genes, including BP, CC, and MF; (B)KEGG pathway enrichment analysis of candidate genes. PPI network construction and Hub gene identification The PPI network between candidate genes is established using the STRING database. The clusteringcoefficient algorithm of the CytoHubba plug-in is used to select the top 10 genes from the PPI network as Hub genes, including STAC3, ATP1B4, TAS2R38, SSTR4, SORBS1, TNNC1, FBXO27, CACNB1, IDH2, PRKAG3 (Figure 4). Figure 4. PPI network and hub genes. The GSE17679 cohort is used to build a prognostic model Using the clinical data of GSE17679, the univariate cox regression analysis was performed on 10 Hub genes (Figure 5A), and 5 genes were obtained through the threshold of P<0.05. 5 genes were subjected to LASSO Cox regression analysis, and the penalty parameter lambda was selected by the cross-validation method to obtain 2 relatively independent characteristic genes for constructing a prognostic model (Figure 5B, C). The results showed that high expression of IDH2 and high expression of PRKAG3 are associated with poor prognosis of ES. Kaplan-Meier curve also showed that the high expression of IDH2 (P<0.001) and PRKAG3 (P<0.004) were associated with poor prognosis (Figure 6A, B). Risk Score = (0.749 × IDH2) + (1.499 × PRKAG3), according to the median value of risk score, patients are divided into high-risk group and low-risk group. PCA analysis in the GSE17679 cohort showed that the distribution patterns of patients in different risk groups were different (Figure 6C). The Kaplan-Meier curve shows that the higher-risk group has a worse prognosis than the low-risk group (log-rank p<0.001) (Figure 6D). The ROC was used to curve to evaluate the performance of the risk score, and the area under the curve (AUC) for 3 years, 5 years, and 10 years are 0.811, 0.838, and 0.844, respectively (Figure 6E). Figure 5. Identification of prognostic genes in ES patients. (A) Univariate Cox regression analysis of 10 Hub genes; (B) LASSO coefficients; (C) LASSO regression with tenfold cross-validation obtained 14 prognostic genes using minimum lambda value. Figure 6. Survival curves of prognostic genes and prognostic models in GEO cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the GEO cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO cohort. Validation of the prognostic model in the ICGC cohort The 57 samples from the International Cancer Genome Consortium (ICGC) database were verified, and the results showed that the high expression of IDH2 (P=0.005) and PRKAG3 (P<0.034) was associated with poor prognosis (Figure 7A, B). In order to test the prognosis model constructed by the GSE17679 cohort, patients in the ICGC cohort were divided into a high-risk group or a low-risk group after the median risk score calculated by the same formula as the GSE17679 cohort. Similar to the results obtained in the GSE17679 cohort, PCA analysis confirmed that the distribution patterns of patients in the high-risk group and the low-risk group were different (Figure 7C). Similarly, the high-risk group had a shorter survival time than the low-risk group (log-rank p<0.019) (Figure 8D). In addition, the area under the curve (AUC) of the prognostic model at 3, 5, and 10 years are 0.659, 0.662, and 0.607, respectively (Figure 7E). Figure 7. Validation of prognostic genes and prognostic models in the ICGC cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the ICGC cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the ICGC cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the ICGC cohort. Analysis of the role of prognostic genes in diseases Finally, GSEA is used to further determine the potential biological processes and pathways of IDH2 and PRKAG3. The results show that the high expression of IDH2 may be related to the following pathways, such as G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING, UV RESPONSE UP (Figure 8A). Highly expressed PRKAG3 is related to the MYOGENESIS pathway (Figure 8B). Figure 8. GSEA of prognostic genes. (A) IDH2 high expression enrichment pathway; (B) PRKAG3 high expression enrichment pathway. Discussion ES seriously harms the health of children and young people. Although the treatment of ES has improved in the past few decades, the ability to treat the disease is still limited due to the lack of precise molecular targets for ES. Many studies have shown that genetic abnormal changes promote the occurrence and development of the disease, but the molecular mechanism is still unclear[ 27 ]. Therefore, further exploration of molecular markers and potential mechanisms related to the progression and prognosis of ES can help the diagnosis and treatment of the disease. In this study, 542 DEGs between tumor and normal tissues were identified based on 3 ES microarray data of Gene Expression Omnibus (GEO) database. The turquoise module was found to be significantly related to ES, and candidate genes were screened out. Construct a PPI network to detect the interaction between the proteins encoded by candidate genes, and select Hub genes. Univariate COX regression, LASSO COX regression, multivariate COX regression, and Kaplan-Meier curve were used to analyze the survival of the Hub gene. The results showed that the high expression levels of IDH2 and PRKAG3 in ES are closely related to the poor prognosis. Isocitrate dehydrogenase 2 (IDH2) catalyzes the oxidative decarboxylation of isocitrate to α-ketoglutarate in the mitochondria. Recent advances in cancer genetics have determined that the IDH2 gene is mutated in glioma, acute myeloid leukemia and other cancers[ 28 ]. Current studies have shown that inhibitors of IDH2 gene mutations may be clinically effective[ 29 ]. However, IDH2 has very few mutations in ES[ 30 , 31 ]. Wild-type IDH2 is down-regulated in liver cancer and gastric cancer, and low-expression IDH2 has a low five-year survival rate[ 32 , 33 ]. Studies have found that high expression of IDH2 is associated with poor prognosis in esophageal squamous cell carcinoma[ 34 ], colon cancer[ 35 ], lung cancer[ 36 ], and non-small cell lung cancer[ 37 ]. The clinical correlation between wild-type IDH2 and ES has not been fully studied. Our results show that IDH2 is associated with the development and poor prognosis of ES. PRKAG3, a protein kinase, is activated by AMP, the non-catalytic subunit of γ3, which may play a role in regulating energy metabolism of skeletal muscle[ 38 , 39 ]. PRKAG3 and overall breast cancer risk are considered as potential targets for cancer treatment[ 40 , 41 ]. Another study showed that PRKAG3 is associated with the recurrence of triple-negative breast cancer in the Chinese population[ 42 ]. We believe that PRKAG3 promotes the development of ES and serves as a new molecular marker. Our research has constructed a new prognostic model, which has been well validated in the GSE17679 cohort and the IGCG cohort. IDH2 and PRKAG3 may be potential prognostic molecular markers for poor survival of ES, and provide potential targets for pathogenesis and treatment. In addition, G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING, etc. may be the key pathways of IDH2 regulation in ES. PRKAG3 may promote the development of ES through the MYOGENESIS pathway. In future research, further experimental verification is needed to prove the biological role of IDH2 and PRKAG3 in ES. Conclusion In summary, we have studied the gene expression profiles of Ewing's sarcoma in GEO and ICGC databases through bioinformatics methods. We provide a prognostic model that has a good predictive effect on the prognosis of patients with Ewing's sarcoma. The model includes two genes (IDH2 and PRKAG3), and using GESA to find that these genes may play important roles through some signaling pathways. These results may serve as potential prognostic molecular biomarkers in Ewing's sarcoma, and may contribute to the molecular targeted therapy of Ewing's sarcoma. Abbreviations ES ewing's sarcoma WGCNA weighted gene co-expression network analysis PPI protein–protein interaction LASSO least absolute shrinkage and the selection operator ROC receiver operating characteristic ICGC International Cancer Genome Consortium GSEA Gene Set Enrichment Analysis DEGs differentially expressed genes STRING Search tool used to search for interacting genes PCA principal component analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes BP biological process CC cellular component MF molecular function AUC area under the curve OS overall survival Declarations Ethics declarations Ethics approval and consent to participate: Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by Gansu Provincial Youth Science and Technology Fund Program (Grant No. 17JR5RA196), The First Hospital of Lanzhou University Project (Grant No. ldyyyn2018‐40), Lanzhou Talent Innovation and Entrepreneurship Project (Grant No. 2016‐RC‐57), Science and Technology Development Project of Chengguan (Grant No. 2017/7/5), and Natural Science Foundation of Gansu Province (Grant No. 1606RJZA126). Acknowledgments None. Contributions Conception and design: Xiaofei Feng; Administrative support: Wenji Wang; Collection and assembly of data: Xiaofei Feng, Hai Lei; Data analysis and interpretation: Xiaofei Feng, Yao Ma, Hai Lei; Manuscript writing: All authors. All authors read and approved the final manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-117483","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":5473155,"identity":"8e00fa5b-4a19-4848-a8bf-1acb2d373e97","order_by":0,"name":"Xiaofei Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBAC9gYQaSNR388O5jMT1sJzAESmWTDObCZRSwXjhsNEa2HvPfyaJ0GC2fgw+zMJhgrrxAb2swfwa+E5l2Y5I0GCzewwQ5oEw5n0xAaevAS8WuwlcswMPv6Q4AFqOSbB2HY4sUGCxwC/LfJvzAwSEiQkjJsZ2yQY/xGjRYLH+MGHBAkDA2ZmNgnGBmK08OSYMQL9kiBxmI3ZIuFYunEbTw4BLexnjD/zJNQl8Le3P7zxocZatp/9DH4tQMAmAWcmgLiE1AMB8wciFI2CUTAKRsFIBgCKOTurMk9uIwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1322-4102","institution":"Lanzhou University First Affiliated Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Feng","suffix":""},{"id":5473156,"identity":"f5994ba0-4af3-409f-a5f2-b9c7827ead3d","order_by":1,"name":"Yao Ma","email":"","orcid":"","institution":"Clinical Laboratory Center, Gansu Provincial Maternity and Child-care Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Ma","suffix":""},{"id":5473157,"identity":"200e9b52-0e5b-4cca-8875-9875c423a4ad","order_by":2,"name":"Hai Lei","email":"","orcid":"","institution":"Tanzhou Hospital of Zhongshan City","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Lei","suffix":""},{"id":5473158,"identity":"7dc7317b-25e7-4a7c-a994-bac9851fcbee","order_by":3,"name":"Wenji Wang","email":"","orcid":"","institution":"Lanzhou University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenji","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2020-11-27 22:22:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-117483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-117483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3959867,"identity":"5b33c1b3-d3ab-4e3a-9543-cbd6fb8421a7","added_by":"auto","created_at":"2020-12-02 18:18:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2085096,"visible":true,"origin":"","legend":"Data processing and DEGs identification. (A) before batch effect removal; (B) after batch effect removal; (C) Volcanogram of DEGs; (D) Heat map of DEGs;","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/0a400bdf70ed991123d247d6.Png"},{"id":3959858,"identity":"1c259873-dcfe-44ea-9789-4c5c3f1046a2","added_by":"auto","created_at":"2020-12-02 18:18:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2085096,"visible":true,"origin":"","legend":"Data processing and DEGs identification. (A) before batch effect removal; (B) after batch effect removal; (C) Volcanogram of DEGs; (D) Heat map of DEGs;","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/c9017b6aa5ec34a5d9298e0c.Png"},{"id":3959849,"identity":"27a45c2a-e9b7-4566-a1d2-64678ae9e5cc","added_by":"auto","created_at":"2020-12-02 18:18:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2085096,"visible":true,"origin":"","legend":"Data processing and DEGs identification. (A) before batch effect removal; (B) after batch effect removal; (C) Volcanogram of DEGs; (D) Heat map of DEGs;","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/b54359916516c9ce2e146f95.Png"},{"id":3959868,"identity":"eb4d0824-e220-4930-9fa4-634693ce7f7d","added_by":"auto","created_at":"2020-12-02 18:18:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1792896,"visible":true,"origin":"","legend":"Construction of weighted co-expression network and screening of candidate genes. (A) Clustering dendrogram of 250 samples; (B) Determination of soft thresholds（β）=3; (C) Average connectivity of soft threshold powers; (D) When（β）= 3, the histogram of the connection distribution; (E) When（β）= 3, Check the scale-free topology. (F) The Cluster dendrogram of co-expression network modules was ordered by a hierarchical clustering of genes based on the 1-TOM matrix; (G) Heatmap of the correlation between module eigengenes and clinical traits of ES; (H) Venn diagram of candidate genes.","description":"","filename":"Onlinefloatimage2.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/5bc861a58df6ef57cf343623.Png"},{"id":3959859,"identity":"98c536d1-e65b-43df-85c1-d71ee69c53b8","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1792896,"visible":true,"origin":"","legend":"Construction of weighted co-expression network and screening of candidate genes. (A) Clustering dendrogram of 250 samples; (B) Determination of soft thresholds（β）=3; (C) Average connectivity of soft threshold powers; (D) When（β）= 3, the histogram of the connection distribution; (E) When（β）= 3, Check the scale-free topology. (F) The Cluster dendrogram of co-expression network modules was ordered by a hierarchical clustering of genes based on the 1-TOM matrix; (G) Heatmap of the correlation between module eigengenes and clinical traits of ES; (H) Venn diagram of candidate genes.","description":"","filename":"Onlinefloatimage2.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/de4bc2ec0756f87cdcda2134.Png"},{"id":3959850,"identity":"4062eb69-7569-4071-988c-a3fbc6a6c915","added_by":"auto","created_at":"2020-12-02 18:18:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1792896,"visible":true,"origin":"","legend":"Construction of weighted co-expression network and screening of candidate genes. (A) Clustering dendrogram of 250 samples; (B) Determination of soft thresholds（β）=3; (C) Average connectivity of soft threshold powers; (D) When（β）= 3, the histogram of the connection distribution; (E) When（β）= 3, Check the scale-free topology. (F) The Cluster dendrogram of co-expression network modules was ordered by a hierarchical clustering of genes based on the 1-TOM matrix; (G) Heatmap of the correlation between module eigengenes and clinical traits of ES; (H) Venn diagram of candidate genes.","description":"","filename":"Onlinefloatimage2.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/00bb5c1b2faabc1750595aba.Png"},{"id":3959869,"identity":"624ffd59-e9a3-4da0-9632-1af81712b91c","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1087190,"visible":true,"origin":"","legend":"GO and KEGG enrichment analyses. (A) Top 10 GO term enrichment analysis of candidate genes, including BP, CC, and MF; (B)KEGG pathway enrichment analysis of candidate genes.","description":"","filename":"Onlinefloatimage3.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/d636b820bcf00d4e79af23d9.Png"},{"id":3959860,"identity":"ac2dbd6f-f17b-4e65-b6f2-0250c3879f6d","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1087190,"visible":true,"origin":"","legend":"GO and KEGG enrichment analyses. (A) Top 10 GO term enrichment analysis of candidate genes, including BP, CC, and MF; (B)KEGG pathway enrichment analysis of candidate genes.","description":"","filename":"Onlinefloatimage3.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/937d145edd48294e6a3d6f40.Png"},{"id":3959851,"identity":"782e0daf-cc3d-4043-8df5-2ba86be3b93b","added_by":"auto","created_at":"2020-12-02 18:18:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1087190,"visible":true,"origin":"","legend":"GO and KEGG enrichment analyses. (A) Top 10 GO term enrichment analysis of candidate genes, including BP, CC, and MF; (B)KEGG pathway enrichment analysis of candidate genes.","description":"","filename":"Onlinefloatimage3.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/2c688dcd01b43ed5f5db3c59.Png"},{"id":3959870,"identity":"6d1a6f1c-57fc-4c47-bc23-2006cdbddc34","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586367,"visible":true,"origin":"","legend":"PPI network and hub genes.","description":"","filename":"Onlinefloatimage4.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/4e341c131b850b4c79e30ce3.Png"},{"id":3959861,"identity":"17987164-5bc0-43f6-852b-af4d4faee3bc","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586367,"visible":true,"origin":"","legend":"PPI network and hub genes.","description":"","filename":"Onlinefloatimage4.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/dc9d61ee99fa68e34b090b0a.Png"},{"id":3959852,"identity":"288efcd6-e8b2-4711-9fe0-61d851b41bc7","added_by":"auto","created_at":"2020-12-02 18:18:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586367,"visible":true,"origin":"","legend":"PPI network and hub genes.","description":"","filename":"Onlinefloatimage4.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/900fcbb7579c3661eea022f9.Png"},{"id":3959871,"identity":"07d5d8cc-6b64-413d-b599-8b7155f365b3","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":567455,"visible":true,"origin":"","legend":"Identification of prognostic genes in ES patients. (A) Univariate Cox regression analysis of 10 Hub genes; (B) LASSO coefficients; (C) LASSO regression with tenfold cross-validation obtained 14 prognostic genes using minimum lambda value. ","description":"","filename":"Onlinefloatimage5.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/0915335a56a919e022575b77.Png"},{"id":3959862,"identity":"a9d4d80a-8ec6-478c-9ab0-46d86fdcd125","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":567455,"visible":true,"origin":"","legend":"Identification of prognostic genes in ES patients. (A) Univariate Cox regression analysis of 10 Hub genes; (B) LASSO coefficients; (C) LASSO regression with tenfold cross-validation obtained 14 prognostic genes using minimum lambda value. ","description":"","filename":"Onlinefloatimage5.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/4addb62985a16d959761e4d5.Png"},{"id":3959853,"identity":"8d28eef7-7b5c-4311-b9fd-960b8b790b1f","added_by":"auto","created_at":"2020-12-02 18:18:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":567455,"visible":true,"origin":"","legend":"Identification of prognostic genes in ES patients. (A) Univariate Cox regression analysis of 10 Hub genes; (B) LASSO coefficients; (C) LASSO regression with tenfold cross-validation obtained 14 prognostic genes using minimum lambda value. ","description":"","filename":"Onlinefloatimage5.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/60986e594f1c4f90267f22e2.Png"},{"id":3959872,"identity":"c9e5b79a-0a62-4062-ab5a-354917dee140","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502568,"visible":true,"origin":"","legend":"Survival curves of prognostic genes and prognostic models in GEO cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the GEO cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO cohort.","description":"","filename":"Onlinefloatimage6.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/96a4432b174cf3ec8cd0c382.Png"},{"id":3959863,"identity":"4cb70341-373c-40ee-b2ab-b3a56a77932f","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502568,"visible":true,"origin":"","legend":"Survival curves of prognostic genes and prognostic models in GEO cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the GEO cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO cohort.","description":"","filename":"Onlinefloatimage6.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/623c5fa4f538afb2be46d1a3.Png"},{"id":3959854,"identity":"df3793d8-72f5-4f64-baf5-a5f4ff2a70f6","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502568,"visible":true,"origin":"","legend":"Survival curves of prognostic genes and prognostic models in GEO cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the GEO cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO cohort.","description":"","filename":"Onlinefloatimage6.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/786bbdb6ccc99b3b4b86fd6c.Png"},{"id":3959873,"identity":"22ded48a-b967-4640-ab7d-53c862509517","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":515216,"visible":true,"origin":"","legend":"Validation of prognostic genes and prognostic models in the ICGC cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the ICGC cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the ICGC cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the ICGC cohort.","description":"","filename":"Onlinefloatimage7.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/702f84d84431a4e1494f39fb.Png"},{"id":3959864,"identity":"5e859158-5a13-48f1-968f-586806a9b612","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":515216,"visible":true,"origin":"","legend":"Validation of prognostic genes and prognostic models in the ICGC cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the ICGC cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the ICGC cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the ICGC cohort.","description":"","filename":"Onlinefloatimage7.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/7ea8eaedd43750d63d74c77f.Png"},{"id":3959855,"identity":"6d8c5423-37a8-438f-a2e4-d5593db4a124","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":515216,"visible":true,"origin":"","legend":"Validation of prognostic genes and prognostic models in the ICGC cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the ICGC cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the ICGC cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the ICGC cohort.","description":"","filename":"Onlinefloatimage7.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/7e7f28ee62b60477f9f3f7eb.Png"},{"id":3959874,"identity":"144f0f25-bb8f-4155-bb3b-62c6379b6b74","added_by":"auto","created_at":"2020-12-02 18:18:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":672581,"visible":true,"origin":"","legend":"GSEA of prognostic genes. (A) IDH2 high expression enrichment pathway; (B) PRKAG3 high expression enrichment pathway.","description":"","filename":"Onlinefloatimage8.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/1afe367b055c4498ca2dea92.Png"},{"id":3959865,"identity":"34393209-eb3a-4438-ae69-fc3fcf97a07d","added_by":"auto","created_at":"2020-12-02 18:18:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":672581,"visible":true,"origin":"","legend":"GSEA of prognostic genes. (A) IDH2 high expression enrichment pathway; (B) PRKAG3 high expression enrichment pathway.","description":"","filename":"Onlinefloatimage8.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/56acd767fe0ddf2c0d2fbc2c.Png"},{"id":3959856,"identity":"6ebf6b2c-7ca6-456c-8d7f-8c0f807f9a29","added_by":"auto","created_at":"2020-12-02 18:18:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":672581,"visible":true,"origin":"","legend":"GSEA of prognostic genes. (A) IDH2 high expression enrichment pathway; (B) PRKAG3 high expression enrichment pathway.","description":"","filename":"Onlinefloatimage8.Png","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/06ead948b0b436965efc2f09.Png"},{"id":13621301,"identity":"31e54bda-aa9d-4ae2-973a-c800d6e81046","added_by":"auto","created_at":"2021-09-17 07:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6072249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-117483/v1/67866b08-a495-40c8-b880-ec0558754595.pdf"}],"financialInterests":"","formattedTitle":"A Prognostic Model for the Overall Survival of Patients with Ewing's Sarcoma","fulltext":[{"header":"Background","content":" \u003cp\u003eES is an aggressive tumor originating from mesenchymal stem cells, with the highest incidence in adolescents and young adults[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the past 30 years, chemotherapy, surgery, and/or radiotherapy have had significant effects on improving the survival of patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], but at this stage, even if intensive treatment has been taken to improve the prognosis of patients, the effect was still not significantly improved[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. ES has a certain background in molecular research, and its therapeutic and prognostic effects are still worth expecting. Studies have shown that ES is characterized by balanced chromosomal translocation, which leads to the expression of fusion oncoproteins. The most common one is EWS/FLI to drive tumor occurrence and development[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, studies have pointed out that targeted inhibitors of EWS/FLI are not clinically feasible[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Also, insulin-like growth factor 1 and its receptor play key roles in promoting the progression of Ewing's sarcoma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the results of clinical trials of inhibitors of this pathway have been disappointing[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, the results still have important guiding significance for us to research the treatment of the disease on a molecular basis. Therefore, the identification of new genes and pathways related to ES occurrence and patient prognosis is crucial.\u003c/p\u003e \u003cp\u003eIn recent years, sequencing technology and bioinformatics have shown indispensable roles in the study of disease molecular mechanisms and specific biomarkers. WGCNA is a systematic biological method that can obtain gene function and gene association from the expression of the whole genome. The identification of genes that promote key roles in disease provides profound insights[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This method has been used to explore the molecular pathology of diseases, such as liver cancer, Laryngeal Squamous Cell Carcinoma, etc. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there is no report on using the WGCNA method to study Ewing's sarcoma related genes and discover new prognostic markers.\u003c/p\u003e \u003cp\u003eIn this study, we constructed a prognostic model in the GSE17679 cohort and verified it in the IGCG cohort. We further performed a functional enrichment analysis of IDH2 and PRKAG3 to explore the underlying mechanism. This study provides an explanation for the pathogenesis and potential molecular biological processes of ES, and the identified genes are expected to become potential biomarkers and targets for diagnosis and treatment.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, gene expression data sets (GSE12102, GSE17679, and GSE34620) were obtained from the GEO (https://www.ncbi.nlm.nih.gov/geo/), and these data sets are all based on the GPL570 platform. The GSE12102 data set includes 37 cases of ES. The GSE17679 data set contains 88 cases of ES tissue and 18 cases of non-tumor tissue, and obtains the survival information of 88 patients. The GSE34620 data set contains 117 cases of ES tissue. The gene expression profile and survival information of 57 patients with ES were obtained from the ICGC (https://icgc.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and differential expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the affy package to read in the raw data of the three data sets for background processing, quality control, and normalization processing[17]. The probe is annotated through the platform annotation file. Combine the three data sets, use the Sva package to remove the batch effects between the three data sets[18], and then use the limma package to analyze differentially expressed genes[19], the false discovery rate (FDR) \u0026lt;0.05 and |log 2 FC| \u0026gt; 3 is used as the cut-off criterion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelect genes with mutation rate\u0026gt; 15% from the expression profile data for analysis. Use WGCNA R software package to cluster analysis of samples. Calculate the Pearson correlation coefficient of any two genes, and determine the soft threshold ability \u0026beta; value to screen the co-expression module. To test whether the \u0026beta; value conforms to the scale-free network, the logarithm of nodes with k connectivity (log(k)) should be negatively related to the logarithm of the occurrence probability of a specific node (log(P(k))), and the correlation coefficient should be greater than 0.85. The number of genes in the gene network module is set to at least 50.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eES candidate genes and their function analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelect the module with the highest correlation with the disease, take cor.geneModuleMembership\u0026gt; 0.8 (correlation between genes and certain clinical phenotypes) as the cut-off criterion, and the overlapping genes between the acquired genes and DEGs as ES Related candidate genes. In order to clarify the potential biological process of candidate genes, the candidate genes were analyzed by GO enrichment and KEGG pathways through clusterProfiler software package[20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of PPI and selection of Hub gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSearch tool used to search for interacting genes (STRING) Online tools can be used to predict the PPI and construct a PPI network of genes with a confidence level of \u0026ge;0.7[21]. The Cytoscape software visualizes the PPI network[22], and the cytoHubba plug-in obtains the top 10 genes, which are regarded as Hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognosis analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to verify the prognostic risk of the hub gene, the hub gene was analyzed by univariate Cox regression, LASSO COX regression, multivariate Cox regression and Kaplan-Meier survival curve. Validation is done through the ICGC database. The above analysis is through the R language survival package[23], the survminer package (https://CRAN.R-project.org/package=survminer), and the glmnet package[24]. P\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the biological processes involved in prognostic genes, according to the median value of each gene expression, it is divided into two groups for GSEA analysis[25], and the GSEA software (version 4.1) is used for analysis, MsigDB gene set \"h.all.v7.2.symbols.gmt\" as a reference[26], the number of random combinations is set to 1000, and p\u0026lt;0.05 is considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes in ES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter preprocessing the data and removing the batch effect, the principal component analysis (PCA) method was used to display the results before and after processing (Figure 1A, B). The results show that the distribution pattern between tumor samples and normal samples is different, which can be used for further analysis. Compared with normal tissues, tumor tissues have 119 up-regulated genes and 423 down-regulated genes. The volcano map (Figure 1C) and heat map (Figure 1D) show the DEGs.\u003c/p\u003e\n\u003cp\u003eFigure 1. Data processing and DEGs identification. (A) before batch effect removal; (B) after batch effect removal; (C) Volcanogram of DEGs; (D) Heat map of DEGs;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of co-expression network and screening of candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster analysis included 18 normal tissues and 242 ES samples, did not exclude any samples (Figure 2A). Choose \u0026beta; = 3 as the optimal soft threshold (Figure 2B, C), and scale-free R2 = 0.88 (Figure 2D, E) to ensure a scale-free network. 5 modules were identified, namely turquoise, blue, brown, green, and yellow modules (Figure 2F). We mapped the correlation between the module and the clinic. Among these modules, the turquoise module has the highest correlation with ES (correlation coefficient =-0.97, p\u0026lt;0.001) (Figure 2G). Select the genes with cor.geneModuleMembership\u0026gt; 0.8 in the turquoise module, there are 381 in total. Then take the intersection with the DEGs. There are 305 overlapping genes as candidate genes for ES (Figure 2H).\u003c/p\u003e\n\u003cp\u003eFigure 2. Construction of weighted co-expression network and screening of candidate genes. (A) Clustering dendrogram of 250 samples; (B) Determination of soft thresholds（\u0026beta;）=3; (C) Average connectivity of soft threshold powers; (D) When（\u0026beta;）= 3, the histogram of the connection distribution; (E) When（\u0026beta;）= 3, Check the scale-free topology. (F) The Cluster dendrogram of co-expression network modules was ordered by a hierarchical clustering of genes based on the 1-TOM matrix; (G) Heatmap of the correlation between module eigengenes and clinical traits of ES; (H) Venn diagram of candidate genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further explore the function of the candidate genes, we conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The GO analysis results show (Figure 3A) that candidate genes include muscle system process, muscle contraction, muscle organ development, muscle cell differentiation and other aspects in the biological process (BP), and the cellular component (CC) includes contractile fiber, myofibril, sarcomere, I band, etc., molecular function (MF) includes actin binding, actin filament binding, structural constituent of muscle, etc.. The results of the KEGG enrichment pathway show (Figure 3B) that candidate genes function mainly through the following pathways, such as Calcium signaling pathway, Oxytocin signaling pathway, cGMP\u0026minus;PKG signaling pathway, cAMP signaling pathway, Glucagon signaling pathway, etc.\u003c/p\u003e\n\u003cp\u003eFigure 3. GO and KEGG enrichment analyses. (A) Top 10 GO term enrichment analysis of candidate genes, including BP, CC, and MF; (B)KEGG pathway enrichment analysis of candidate genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and Hub gene identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPI network between candidate genes is established using the STRING database. The clusteringcoefficient algorithm of the CytoHubba plug-in is used to select the top 10 genes from the PPI network as Hub genes, including STAC3, ATP1B4, TAS2R38, SSTR4, SORBS1, TNNC1, FBXO27, CACNB1, IDH2, PRKAG3 (Figure 4).\u003c/p\u003e\n\u003cp\u003eFigure 4. PPI network and hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe GSE17679 cohort is used to build a prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the clinical data of GSE17679, the univariate cox regression analysis was performed on 10 Hub genes (Figure 5A), and 5 genes were obtained through the threshold of P\u0026lt;0.05. 5 genes were subjected to LASSO Cox regression analysis, and the penalty parameter lambda was selected by the cross-validation method to obtain 2 relatively independent characteristic genes for constructing a prognostic model (Figure 5B, C). The results showed that high expression of IDH2 and high expression of PRKAG3 are associated with poor prognosis of ES. Kaplan-Meier curve also showed that the high expression of IDH2 (P\u0026lt;0.001) and PRKAG3 (P\u0026lt;0.004) were associated with poor prognosis (Figure 6A, B). Risk Score = (0.749 \u0026times; IDH2) + (1.499 \u0026times; PRKAG3), according to the median value of risk score, patients are divided into high-risk group and low-risk group. PCA analysis in the GSE17679 cohort showed that the distribution patterns of patients in different risk groups were different (Figure 6C). The Kaplan-Meier curve shows that the higher-risk group has a worse prognosis than the low-risk group (log-rank p\u0026lt;0.001) (Figure 6D). The ROC was used to curve to evaluate the performance of the risk score, and the area under the curve (AUC) for 3 years, 5 years, and 10 years are 0.811, 0.838, and 0.844, respectively (Figure 6E).\u003c/p\u003e\n\u003cp\u003eFigure 5. Identification of prognostic genes in ES patients. (A) Univariate Cox regression analysis of 10 Hub genes; (B) LASSO coefficients; (C) LASSO regression with tenfold cross-validation obtained 14 prognostic genes using minimum lambda value.\u003c/p\u003e\n\u003cp\u003eFigure 6. Survival curves of prognostic genes and prognostic models in GEO cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the GEO cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the GEO cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the GEO cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the prognostic model in the ICGC cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 57 samples from the International Cancer Genome Consortium (ICGC) database were verified, and the results showed that the high expression of IDH2 (P=0.005) and PRKAG3 (P\u0026lt;0.034) was associated with poor prognosis (Figure 7A, B). In order to test the prognosis model constructed by the GSE17679 cohort, patients in the ICGC cohort were divided into a high-risk group or a low-risk group after the median risk score calculated by the same formula as the GSE17679 cohort. Similar to the results obtained in the GSE17679 cohort, PCA analysis confirmed that the distribution patterns of patients in the high-risk group and the low-risk group were different (Figure 7C). Similarly, the high-risk group had a shorter survival time than the low-risk group (log-rank p\u0026lt;0.019) (Figure 8D). In addition, the area under the curve (AUC) of the prognostic model at 3, 5, and 10 years are 0.659, 0.662, and 0.607, respectively (Figure 7E).\u003c/p\u003e\n\u003cp\u003eFigure 7. Validation of prognostic genes and prognostic models in the ICGC cohort. (A) OS curves of IDH2; (B) OS curves of PRKAG3; (C) PCA plot of the ICGC cohort; (D) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the ICGC cohort; (E) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the ICGC cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the role of prognostic genes in diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, GSEA is used to further determine the potential biological processes and pathways of IDH2 and PRKAG3. The results show that the high expression of IDH2 may be related to the following pathways, such as G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING, UV RESPONSE UP (Figure 8A). Highly expressed PRKAG3 is related to the MYOGENESIS pathway (Figure 8B).\u003c/p\u003e\n\u003cp\u003eFigure 8. GSEA of prognostic genes. (A) IDH2 high expression enrichment pathway; (B) PRKAG3 high expression enrichment pathway.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eES seriously harms the health of children and young people. Although the treatment of ES has improved in the past few decades, the ability to treat the disease is still limited due to the lack of precise molecular targets for ES. Many studies have shown that genetic abnormal changes promote the occurrence and development of the disease, but the molecular mechanism is still unclear[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, further exploration of molecular markers and potential mechanisms related to the progression and prognosis of ES can help the diagnosis and treatment of the disease. In this study, 542 DEGs between tumor and normal tissues were identified based on 3 ES microarray data of Gene Expression Omnibus (GEO) database. The turquoise module was found to be significantly related to ES, and candidate genes were screened out. Construct a PPI network to detect the interaction between the proteins encoded by candidate genes, and select Hub genes. Univariate COX regression, LASSO COX regression, multivariate COX regression, and Kaplan-Meier curve were used to analyze the survival of the Hub gene. The results showed that the high expression levels of IDH2 and PRKAG3 in ES are closely related to the poor prognosis.\u003c/p\u003e \u003cp\u003eIsocitrate dehydrogenase 2 (IDH2) catalyzes the oxidative decarboxylation of isocitrate to α-ketoglutarate in the mitochondria. Recent advances in cancer genetics have determined that the IDH2 gene is mutated in glioma, acute myeloid leukemia and other cancers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Current studies have shown that inhibitors of IDH2 gene mutations may be clinically effective[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, IDH2 has very few mutations in ES[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Wild-type IDH2 is down-regulated in liver cancer and gastric cancer, and low-expression IDH2 has a low five-year survival rate[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Studies have found that high expression of IDH2 is associated with poor prognosis in esophageal squamous cell carcinoma[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], colon cancer[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], lung cancer[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and non-small cell lung cancer[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The clinical correlation between wild-type IDH2 and ES has not been fully studied. Our results show that IDH2 is associated with the development and poor prognosis of ES.\u003c/p\u003e \u003cp\u003ePRKAG3, a protein kinase, is activated by AMP, the non-catalytic subunit of γ3, which may play a role in regulating energy metabolism of skeletal muscle[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. PRKAG3 and overall breast cancer risk are considered as potential targets for cancer treatment[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Another study showed that PRKAG3 is associated with the recurrence of triple-negative breast cancer in the Chinese population[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We believe that PRKAG3 promotes the development of ES and serves as a new molecular marker.\u003c/p\u003e \u003cp\u003eOur research has constructed a new prognostic model, which has been well validated in the GSE17679 cohort and the IGCG cohort. IDH2 and PRKAG3 may be potential prognostic molecular markers for poor survival of ES, and provide potential targets for pathogenesis and treatment. In addition, G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING, etc. may be the key pathways of IDH2 regulation in ES. PRKAG3 may promote the development of ES through the MYOGENESIS pathway. In future research, further experimental verification is needed to prove the biological role of IDH2 and PRKAG3 in ES.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn summary, we have studied the gene expression profiles of Ewing's sarcoma in GEO and ICGC databases through bioinformatics methods. We provide a prognostic model that has a good predictive effect on the prognosis of patients with Ewing's sarcoma. The model includes two genes (IDH2 and PRKAG3), and using GESA to find that these genes may play important roles through some signaling pathways. These results may serve as potential prognostic molecular biomarkers in Ewing's sarcoma, and may contribute to the molecular targeted therapy of Ewing's sarcoma.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eewing's sarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eweighted gene co-expression network analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein\u0026ndash;protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and the selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Cancer Genome Consortium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch tool used to search for interacting genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecellular component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Gansu Provincial Youth Science and Technology Fund Program (Grant No. 17JR5RA196), The First Hospital of Lanzhou University Project (Grant No. ldyyyn2018‐40), Lanzhou Talent Innovation and Entrepreneurship Project (Grant No. 2016‐RC‐57), Science and Technology Development Project of Chengguan (Grant No. 2017/7/5), and Natural Science Foundation of Gansu Province (Grant No. 1606RJZA126).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Xiaofei Feng; Administrative support: Wenji Wang; Collection and assembly of data: Xiaofei Feng, Hai Lei; Data analysis and interpretation: Xiaofei Feng, Yao Ma, Hai Lei; Manuscript writing: All authors. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGaspar N, Hawkins DS, Dirksen U, Lewis IJ, Ferrari S, Le Deley MC, et al. Ewing Sarcoma: Current Management and Future Approaches Through Collaboration. J Clin Oncol. 2015;33:3036\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGr\u0026uuml;newald TGP, Cidre-Aranaz F, Surdez D, Tomazou EM, de \u0026Aacute;lava E, Kovar H, et al. Ewing sarcoma. Nat Rev Dis Primers. 2018;4:5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTirode F, Laud-Duval K, Prieur A, Delorme B, Charbord P, Delattre O. Mesenchymal stem cell features of Ewing tumors. Cancer Cell. 2007;11:421\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamron TA, Ward WG, Stewart A. Osteosarcoma, chondrosarcoma, and Ewing's sarcoma: National Cancer Data Base Report. 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PLoS One. 2011;6:e28222.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ewing's sarcoma, WGCNA, prognosis, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-117483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-117483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Ewing's sarcoma (ES) is the second most common primary malignant bone tumor. Although the disease has been studied on a molecular basis, its prognosis has not improved. Therefore, the goal of this study is to screen effective biomarkers for predicting the prognosis and progression of ES.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this study, the gene expression profile of Ewing's sarcoma was downloaded from public databases. Candidate genes were screened by two methods of weighted gene co-expression network analysis (WGCNA) and differential genes expression analysis, and the Hub gene were determined by the protein–protein interaction (PPI) network. The univariate cox regression and least absolute shrinkage and the selection operator (LASSO) Cox regression were performed on the Hub gene to construct a prognostic model. The receiver operating characteristic (ROC) curve tests the predictive power of the prognostic model. Use the clinical data in the International Cancer Genome Consortium (ICGC) database for verification. Finally, Gene Set Enrichment Analysis (GSEA) was performed to obtain the biological role of prognostic genes in ES.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 542 differentially expressed genes（DEGs） were obtained. Through WGCNA, the obtained turquoise module is significantly related to ES. PPI network analysis of candidate genes identified 10 Hub genes. The univariate cox regression results showed that CACNB1, IDH2, ATP1B4, PRKAG3, STAC3 are risk factors affecting the prognosis of ES. A prognostic model was constructed using IDH2 and PRKAG3, and patients were divided into two risk groups. The survival time of patients in the high-risk group was significantly shorter than that in the low-risk group. The ROC curve confirms that this model has certain accuracy. IGCG cohort verification yielded consistent results. GSEA results showed that IDH2 in the development of ES may promote the progression of the disease through G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING pathways, while PRKAG3 through MYOGENESIS pathway.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study shows that IDH2 and PRKAG3 may play important roles in the progression of ES, and can be used as therapeutic targets and prognostic evaluation biomarkers.\u003c/p\u003e","manuscriptTitle":"A Prognostic Model for the Overall Survival of Patients with Ewing's Sarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-02 18:18:11","doi":"10.21203/rs.3.rs-117483/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23904d62-494e-416f-9c15-2c95b5802882","owner":[],"postedDate":"December 2nd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1294337,"name":"Translational Medicine"}],"tags":[],"updatedAt":"2020-12-02T18:18:13+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-02 18:18:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-117483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-117483","identity":"rs-117483","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}