Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related genes and its association with immune infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34920339", "kind": "science-record", "revision": 3, "content_hash": "sha256:153b2c0f26b9df239c4edd78988141ce4c9f72d9a5df4083ffd86a2d6d8b5e1c", "created_at": "2026-11-01T17:36:38Z", "updated_at": "2026-11-01T21:26:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related genes and its association with immune infiltration.", "doi": "10.1016/j.tranon.2021.101314", "pmid": "34920339", "authors": ["Dan Xiong", "Mojuan Li", "Chong Zeng"], "journal": "Translational oncology", "year": 2022, "abstract": "BACKGROUNDS: The prognostic significance of ferroptosis-related genes is well known. However, survival- and ferroptosis-related genes are not currently considered in risk scoring models for diffuse large B-cell lymphoma (DLBCL).\nMATERIALS AND METHODS: Ferroptosis regulators and markers were downloaded from the FerrDb database. The transcriptome profiling data were collected from the cancer genome atlas (TCGA). Transcriptome data and corresponding clinical information of DLBCL were downloaded from the gene expression omnibus (GEO). The validation data were downloaded using the UCSC Xena browser. ConsensusClusterPlus was used to categorize DLBCL samples according to gene expression profiles. The survival function was plotted with the Kaplan-Meier plots. The nomogram was built using multivariate logistic regression analysis and the Cox proportional hazards regression model.\nRESULTS: Based on the GSE11318 dataset of 203 samples and 267 ferroptosis-related gene expression profiles, we identified four clusters. A total of 19 survival-related genes were found associated with ferroptosis. The prognostic risk scoring model was constructed based on the regression coefficients. The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively. DLBCL samples with cluster 2 or cancer stage IV have shorter survival. Correlations between the immune infiltration and risk scores of the 12 immune cells were demonstrated. The response of DLBCL to doxorubicin was effectively validated by the risk scoring model.\nCONCLUSIONS: In this study, a ferroptosis-based risk scoring model for patients with DLBCL was constructed and validated in an independent dataset. This risk score model has a better efficacy in predicting survival compared to clinical characteristics.", "claims": [{"id": "claim-method", "statement": "Univariable Cox regression analysis was carried out to screen the survival- and ferroptosis- related factors. A least absolute shrinkage and selection operator (LASSO)-type regularization method was used to penalize the factor data. The obtained model shrank the coefficients for non-informative factors toward zero, the reduced variance was acquired, and clustering performance was improved. Prognostic risk scoring models associated with ferroptosis-related factors in DLBCL were constructed based on the regression coefficients of these factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34920339", "locator": "sections[10] \"COX-LASSO regression analysis\" paragraph 2", "quote": "Univariable Cox regression analysis was carried out to screen the survival- and ferroptosis- related factors. A least absolute shrinkage and selection operator (LASSO)-type regularization method was used to penalize the factor data. The obtained model shrank the coefficients for non-informative factors toward zero, the reduced variance was acquired, and clustering performance was improved. Prognostic risk scoring models associated with ferroptosis-related factors in DLBCL were constructed based on the regression coefficients of these factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets used and/or analyzed in this study can be obtained from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34920339", "locator": "sections[24] \"ack\" paragraph 1", "quote": "Datasets used and/or analyzed in this study can be obtained from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed and externally validated a ferroptosis-based risk scoring model for DLBCL, with AUC values of 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival in the development dataset and 0.673, 0.708, and 0.716, respectively, in the independent dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34920339", "locator": "sections[15] \"Identification of ferroptosis and survival-related genes and construction of prognostic risk scoring model\" paragraph 2", "quote": "The prognostic risk scoring model was constructed based on the regression coefficients and calculated as follows, risk Score = -0.006906*DRD4-0.1427*TFAP2C-0.03086*AKR1C3+0.08295*CHAC1+0.2237*ULK2+0.1026*CXCL2-0.2478*GABARAPL1+0.07055*TRIB3-0.01005*CYBB+0.4483*IREB2-0.02139*EPAS1+0.1521*MT1G+0.2654*ATG3-0.06966*CAPG+0.4052*ATF4+0.03599*UBC."}, {"asset": "source:pmid:34920339", "locator": "sections[0] \"Abstract\" paragraph 7", "quote": "The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively."}, {"asset": "source:pmid:34920339", "locator": "sections[17] \"Validation of the prognostic scoring model for DLBCL and external confirmation of the nomogram\" paragraph 2", "quote": "The GSE10846 expression profile was input into the model, and the risk scores were obtained."}, {"asset": "source:pmid:34920339", "locator": "sections[17] \"Validation of the prognostic scoring model for DLBCL and external confirmation of the nomogram\" paragraph 2", "quote": "The predictive validity of risk scores for 1, 3, and 5 -year survival probability is presented in Fig. 5B, with obtained AUC values of 0.673, 0.708 and 0.716, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed and externally validated a ferroptosis-based risk scoring model for DLBCL, with AUC values of 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival in the development dataset and 0.673, 0.708, and 0.716, respectively, in the independent dataset.", "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:34920339"], "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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"missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Univariable Cox regression analysis was carried out to screen the survival- and ferroptosis- related factors. A least absolute shrinkage and selection operator (LASSO)-type regularization method was used to penalize the factor data. The obtained model shrank the coefficients for non-informative factors toward zero, the reduced variance was acquired, and clustering performance was improved. Prognostic risk scoring models associated with ferroptosis-related factors in DLBCL were constructed based on the regression coefficients of these factors."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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