Construction of a Ferroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Prognosis Prediction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34992433", "kind": "science-record", "revision": 5, "content_hash": "sha256:e0de638b62867789df6fd355ebed47c25bf9402005d800a6cedc3ed071be044f", "created_at": "2026-11-01T17:36:42Z", "updated_at": "2026-11-01T22:48:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a Ferroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Prognosis Prediction.", "doi": "10.2147/IJGM.S343233", "pmid": "34992433", "authors": ["Qun Li", "Yangli Jin", "Zhisen Shen", "Huigao Liu", "Yi Shen", "Zhenhua Wu"], "journal": "International journal of general medicine", "year": 2021, "abstract": "BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers, and few studies have demonstrated the value of ferroptosis-related genes in prognosis.\nMETHODS: The original counts of RNA sequencing data and clinicopathological data were obtained from TCGA and GSE65858 datasets. Common ferroptosis-related genes related to prognosis were identified from the training set and were included in LASSO to determine the best prognosis. To evaluate the efficacy, time-dependent ROC and Kaplan-Meier (KM) survival analyses were applied. Moreover, univariate and multivariate Cox regression analyses were used to screen independent parameters of prognosis and build a nomogram. Eventually, possible biological pathways were proposed based on GSEA.\nRESULTS: Among 242 ferroptosis-related genes, we identified that the FLT3, IL6, Keap1, NQO1, SOCS1 and TRIB3 genes were significantly connected with HNSCC patient prognosis as a six-gene signature. After, the patients were divided into high- and low-risk groups based on the six-gene signature. The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset. Moreover, GSEA suggested that the epithelial mesenchymal transition pathway was abundant and that the mesenchymal status in the high-risk group was substantially higher than that in the low-risk group. Finally, the immune microenvironment and differences in the content of immune cell types were demonstrated.\nCONCLUSION: We established a six-ferroptosis-related-gene model crossing clinical prognostic parameters that can predict HNSCC patient prognosis and provide a reliable prognostic evaluation tool to assist clinical treatment decisions.", "claims": [{"id": "claim-method", "statement": "In the univariate Cox proportional hazard regression analysis, the expression data of log2 TPM transformation and the relevant clinical data were applied to screen prognostic genes (hazard ratio (HR) ≠ 1, P < 0.05). A group of candidate genes was obtained by crossing the prognosis-related genes with ferroptosis-related genes. By applying the “glmnet” software package of R software15 and the least absolute shrinkage and selection operator (LASSO) regression method, a polygenic signature of OCGS was constructed to predict HNSCC prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34992433", "locator": "sections[4] \"Selection and Verification of Prognosis-Related Genes\" paragraph 3", "quote": "In the univariate Cox proportional hazard regression analysis, the expression data of log2 TPM transformation and the relevant clinical data were applied to screen prognostic genes (hazard ratio (HR) ≠ 1, P < 0.05). A group of candidate genes was obtained by crossing the prognosis-related genes with ferroptosis-related genes. By applying the “glmnet” software package of R software15 and the least absolute shrinkage and selection operator (LASSO) regression method, a polygenic signature of OCGS was constructed to predict HNSCC prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34992433", "locator": "sections[23] \"Data Sharing Statement\" paragraph 2", "quote": "The data are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset.", "kind": "quantitative", "target": {"value": 0.0001, "unit": "p-value", "tolerance": null, "source": "The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset."}, "evidence": [{"asset": "source:pmid:34992433", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 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:34992433"], "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:GSE65858"], "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": {}, "inputs": ["data:geo:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34992433", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S343233", "sha256": 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expression data of log2 TPM transformation and the relevant clinical data were applied to screen prognostic genes (hazard ratio (HR) ≠ 1, P < 0.05). A group of candidate genes was obtained by crossing the prognosis-related genes with ferroptosis-related genes. By applying the “glmnet” software package of R software15 and the least absolute shrinkage and selection operator (LASSO) regression method, a polygenic signature of OCGS was constructed to predict HNSCC prognosis."}}, {"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": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "termine the risk ratio (hazard ratio, HR) of the P value and", "p_value": "azard ratio, HR) of the P value and the 95% confidence", "confidence_interval": "the P value and the 95% confidence interval (Cl)"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Log rank"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.0001, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. 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METHODS: The original counts of RNA sequencing data and clinicopathological data were obtai"], "order_basis": "paper_text_order:294", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In the univariate Cox proportional hazard regression analysis, the expression data of log2 TPM transformation and the relevant clinical data were applied to screen prognostic genes (hazard ratio (HR) ≠ 1, P < 0.05). A group of candidate genes was obtained by crossing the prognosis-related genes with ferroptosis-related genes. 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