Construction of a Prognosis-Related Gene Signature by Weighted Gene Coexpression Network Analysis in Ewing Sarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35126643", "kind": "science-record", "revision": 4, "content_hash": "sha256:dc2f4ed20ce7371b4c037a1c474179d4117394aa5adecc370528ae7400f530eb", "created_at": "2026-11-01T17:36:52Z", "updated_at": "2026-11-01T22:05:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a Prognosis-Related Gene Signature by Weighted Gene Coexpression Network Analysis in Ewing Sarcoma.", "doi": "10.1155/2022/8798624", "pmid": "35126643", "authors": ["Runhan Zhao", "Chuang Xiong", "Chao Zhang", "Lin Wang", "Hao Liang", "Xiaoji Luo"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "BACKGROUND: Ewing sarcoma (ES) is the second most common pediatric bone tumor with a high rate of metastasis, high recurrence, and low survival rate. Therefore, the identification of new biomarkers which can improve the prognosis of ES patients is urgently needed.\nMETHODS: Here, GSE17679 dataset was downloaded from GEO databases. WGCNA method was used to identify one module associating with OVS (overall vital survival) and event. cytoHubba was used to screen out 50 hub genes from the module genes. Then, GSE17679 dataset was randomly divided into train cohort and test cohort. Next, univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were conducted on 50 hub genes combined with train cohort data to select pivotal genes. Finally, an optimal 7-gene-based risk assessment model was established, which was verified by test cohort, entire GSE17679, and two independent datasets (GSE63157 and TCGA-SARC).\nRESULTS: The results of the functional enrichment analysis revealed that the OVS and event-associated module were mainly enriched in the protein transcription, cell proliferation, and cell-cycle control. And the train cohort was divided into high-risk and low-risk subgroups based on the median risk score; the results showed that the survival of the low-risk subgroup was significantly longer than high-risk. ROC analysis revealed that AUC values of 1, 3, and 5-year survival were 0.85, 0.94, and 0.88, and Kaplan-Meier analysis also revealed that P value < 0.0001, indicating that this model was accurate, which was also verified in the test, entire cohort, and two independent datasets (GSE63157 and TCGA-SARC). Then, we performed a comprehensive analysis (differential expression analysis, correlation analysis and survival analysis) of seven pivotal genes, and found that four genes (NCAPG, KIF4A, NUF2 and CDC20) plays a more crucial role in the prognosis of ES.\nCONCLUSION: Taken together, this study established an optimal 7-gene-based risk assessment model and identified 4 potential therapeutic targets, to improve the prognosis of ES patients.", "claims": [{"id": "claim-method", "statement": "WGCNA method was used to identify one module associating with OVS (overall vital survival) and event.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35126643", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "WGCNA method was used to identify one module associating with OVS (overall vital survival) and event."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data are available at Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/ and The Cancer Genome Atlas Program (TCGA): https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35126643", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The data are available at Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/ and The Cancer Genome Atlas Program (TCGA): https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga."}], "state": "complete"}, {"id": "claim-target-result", "statement": "According to the module-trait relationship heatmap (Figure 2), the turquoise module was significantly correlated with the event (r2 = 0.46; P = 8e − 06), OVS (r2 = −0.53; P = 1e − 07), and EFS (r2 = −0.41; P = 7e − 05).", "kind": "quantitative", "target": {"value": 2.0, "unit": "correlation coefficient", "tolerance": null, "source": "According to the module-trait relationship heatmap (Figure 2), the turquoise module was significantly correlated with the event (r2 = 0.46; P = 8e − 06), OVS (r2 = −0.53; P = 1e − 07), and EFS (r2 = −0.41; P = 7e − 05)."}, "evidence": [{"asset": "source:pmid:35126643", "locator": "sections[13] \"3.2. Identification of Prognosis-Related and OVS-Related Coexpression Modules\" paragraph 3", "quote": "According to the module-trait relationship heatmap (Figure 2), the turquoise module was significantly correlated with the event (r2 = 0.46; P = 8e − 06), OVS (r2 = −0.53; P = 1e − 07), and EFS (r2 = −0.41; P = 7e − 05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "According to the module-trait relationship heatmap (Figure 2), the turquoise module was significantly correlated with the event (r2 = 0.46; P = 8e − 06), OVS (r2 = −0.53; P = 1e − 07), and EFS (r2 = −0.41; P = 7e − 05).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35126643"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17679"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"p_value": "ysis also revealed that P value < 0"}, "inputs": ["data:geo:GSE17679"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35126643", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/8798624", "sha256": 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