An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32968877", "kind": "science-record", "revision": 4, "content_hash": "sha256:bc9e59ef78ea69084d4401a1cd4f542d0dd17bfbcc8f1ab56ca9a90dd751f201", "created_at": "2026-11-01T17:34:07Z", "updated_at": "2026-11-01T22:02:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.", "doi": "10.1007/s00432-020-03396-3", "pmid": "32968877", "authors": ["En-Hui Ren", "Ya-Jun Deng", "Wen-Hua Yuan", "Zuo-Long Wu", "Guang-Zhi Zhang", "Qi-Qi Xie"], "journal": "Journal of cancer research and clinical oncology", "year": 2021, "abstract": "PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs).\nMETHODS: We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein-protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells.\nRESULTS: A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation.\nCONCLUSION: Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.", "claims": [{"id": "claim-method", "statement": "Then, we conducted functional correlation and protein–protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32968877", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, we conducted functional correlation and protein–protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32968877", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The IRG-related 11-gene signature was found to be a reliable prognostic biomarker for Ewing sarcoma.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:32968877", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The IRG-related 11-gene signature was found to be a reliable prognostic biomarker for Ewing sarcoma.", "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:32968877"], "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. 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