Identification of a prognostic signature based on five ferroptosis-related genes for diffuse large B-cell lymphoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38517778", "kind": "science-record", "revision": 2, "content_hash": "sha256:e096edc872ec76131d2e733994268b45f11efc96dd51d07d840ec0a2a731b3dd", "created_at": "2026-11-01T19:20:27Z", "updated_at": "2026-11-01T21:32:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a prognostic signature based on five ferroptosis-related genes for diffuse large B-cell lymphoma.", "doi": "10.3233/CBM-230325", "pmid": "38517778", "authors": ["Wuping Li", "Ruizhe Yao", "Nasha Yu", "Weiming Zhang"], "journal": "Cancer biomarkers : section A of Disease markers", "year": 2024, "abstract": "BACKGROUND: Therapies for diffuse large B-cell lymphoma (DLBCL) are limited due to the diverse gene expression profiles and complicated immune microenvironments, making it an aggressive lymphoma. Beyond this, researches have shown that ferroptosis contributes to tumorigenesis, progression, and metastasis. We thus are interested to dissect the connection between ferroptosis and disease status of DLBCL. We aim at generating a valuable prognosis gene signature for predicting the status of patients of DLBCL, with focus on ferroptosis-related genes (FRGs).\nOBJECTIVE: To examine the connection between ferroptosis-related genes (FRGs) and clinical outcomes in DLBCL patients based on public datasets.\nMETHODS: An expression profile dataset for DLBCL was downloaded from GSE32918 (https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=gse32918), and a ferroptosis-related gene cluster was obtained from the FerrDb database (http://www. zhounan.org/ferrdb/). A prognostic signature was developed from this gene cluster by applying a least absolute shrinkage and selection operator (LASSO) Cox regression analysis to GSE32918, followed by external validation. Its effectiveness as a biomarker and the prognostic value was determined by a receiver operator characteristic curve mono factor analysis. Finally, functional enrichment was evaluated by the package Cluster Profiler of R.\nRESULTS: Five ferroptosis-related genes (FRGs) (GOP1, GPX2, SLC7A5, ATF4, and CXCL2) associated with DLBCL were obtained by a multivariate analysis. The prognostic power of these five FRGs was verified by TCGA (https://xenabrowser.net/datapages/?dataset=TCGA.DLBC.sampleMap%2FHiSeqV2_PANCAN&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A44) and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse 32918) datasets, with ROC analyses. KEGG and GO analyses revealed that upregulated genes in the high-risk group based on the gene signature were enriched in receptor interactions and other cancer-related pathways, including pathways related to abnormal metabolism and cell differentiation.\nCONCLUSION: The newly developed signature involving GOP1, GPX2, SLC7A5, ATF4, and CXCL2 has the potential to serve as a prognostic biomarker. Furthermore, our results provide additional support for the contribution of ferroptosis to DLBCL.", "claims": [{"id": "claim-method", "statement": "This gene cluster was then employed to further refine the gene sets and build a FRG signature by applying the least absolute shrinkage and selection operator (LASSO) Cox regression analysis using the glmnet package [25] in R, which effectively selected the most predictive features.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38517778", "locator": "sections[5] \"Building a prognostic gene signature\" paragraph 2", "quote": "This gene cluster was then employed to further refine the gene sets and build a FRG signature by applying the least absolute shrinkage and selection operator (LASSO) Cox regression analysis using the glmnet package [25] in R, which effectively selected the most predictive features."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The public datasets used in the study were obtained from FerrDb, http://www.zhounan.org/ferrdb, TCGA repository, https://portal.gdc/cancer.gov/ and GEO repository: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38517778", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The public datasets used in the study were obtained from FerrDb, http://www.zhounan.org/ferrdb, TCGA repository, https://portal.gdc/cancer.gov/ and GEO repository: https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed and validated a five-gene ferroptosis-related prognostic signature comprising GOP1, GPX2, SLC7A5, ATF4, and CXCL2 for DLBCL.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38517778", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Five ferroptosis-related genes (FRGs) (GOP1, GPX2, SLC7A5, ATF4, and CXCL2) associated with DLBCL were obtained by a multivariate analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study developed and validated a five-gene ferroptosis-related prognostic signature comprising GOP1, GPX2, SLC7A5, ATF4, and CXCL2 for DLBCL.", "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:38517778"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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