HPV-ferroptosis related genes as biomarkers to predict the prognosis of cervical cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39302544", "kind": "science-record", "revision": 3, "content_hash": "sha256:6ddd576c250aed31dc53a2af434fb5f62510bff98d697feba7ccdbcefb8333cf", "created_at": "2026-11-01T20:29:29Z", "updated_at": "2026-11-01T22:12:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "HPV-ferroptosis related genes as biomarkers to predict the prognosis of cervical cancer.", "doi": "10.1007/s12672-024-01291-8", "pmid": "39302544", "authors": ["Songtao Han", "Senyu Wang", "Yuxia Li", "YuJiao He", "Jing Ma", "Yangchun Feng"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Ferroptosis can be used as a powerful predictor of cancer prognosis. HPV persistent infection is the main cause of cervical cancer, so it is very important to improve the prognosis of patients. Therefore, it is necessary to explore the value of HPV-ferroptosis related genes as prognostic biomarkers of cervical cancer patients.\nMETHODS: In this study, differentially expressed HPV-ferroptosis related genes were obtained from GSE7410, HPV gene set crossed with iron death genes. Five HPV-ferroptosis related genes with prognostic features were finally identified: CYBB, VEGFA, CKB, EFNA1 and HELLS. Multifactorial Cox regression was applied to establish and validate the prognostic model, and drug susceptibility and immune infiltration analyses were also performed.\nRESULTS: The prognostic model was validated in the training set (TCGA) and validation set (GSE44001). Kaplan-Meier curves reveal significant differences in overall survival (OS) between high-risk and low-risk groups. Receiver operating characteristic (ROC) curve reflects the stability and accuracy of the prognostic model established in this study. In terms of immune function, T cell costimulation was better in the low-risk group than in the high-risk group (P < 0.01). The therapeutic effects of cisplatin, paclitaxel, docetaxel and cyclophosphamide, commonly used chemotherapy drugs for cervical cancer, are better in the high-risk group than in the low-risk group (P < 0.001).\nCONCLUSION: HPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment.", "claims": [{"id": "claim-method", "statement": "The R package (glmnet version 4.1.1) executed LASSO regression on the differentially expressed HPV-ferroptosis related genes to filter out redundant factors. Subsequently, univariate/multivariate Cox regression analysis determined the prognostic genes and constructed a Risk score prognostic model (The majority of literature calculates Risk score based on the weighting of the product of gene expression and its coefficients. This study employed multivariate Cox regression to develop a model in which Risk score was determined as the weighting of the product of gene expression and its coefficients.).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39302544", "locator": "sections[5] \"Prognostic model establishment and prognostic analysis\" paragraph 2", "quote": "The R package (glmnet version 4.1.1) executed LASSO regression on the differentially expressed HPV-ferroptosis related genes to filter out redundant factors. Subsequently, univariate/multivariate Cox regression analysis determined the prognostic genes and constructed a Risk score prognostic model (The majority of literature calculates Risk score based on the weighting of the product of gene expression and its coefficients. This study employed multivariate Cox regression to develop a model in which Risk score was determined as the weighting of the product of gene expression and its coefficients.)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39302544", "locator": "sections[21] \"notes\" paragraph 1", "quote": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39302544", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "HPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment.", "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:39302544"], "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:GSE44001"], "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:GSE44001"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39302544", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01291-8", "sha256": "sha256:56341cd1726d664be18c3d677d6ac7287cb671fb33ea6f6a91bcaef0d0078323", "bytes": 35062, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01291-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11415325", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE44001", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44001", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE44001", "version": null, "note": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). 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"application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7410", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE7410", "version": null, "note": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM178828", "GSM178829", "GSM178830", "GSM178831", "GSM178832", "GSM178833", "GSM178834", "GSM178835", "GSM178836", "GSM178837", "GSM178838", "GSM178839", "GSM178840", "GSM178841", "GSM178842", "GSM178843", "GSM178844", "GSM178845", "GSM178846", "GSM178847", "GSM178848", "GSM178849", "GSM178850", "GSM178851", "GSM178852", "GSM178853", "GSM178854", "GSM178855", "GSM178856", "GSM178857", "GSM178858", "GSM178859", "GSM178860", "GSM178861", "GSM178862", "GSM178863", "GSM178864", "GSM178865", "GSM178866", "GSM178867", "GSM178868", "GSM178869", "GSM178870", "GSM178871", "GSM178872"], "sample_count": 45, "group_sizes": null, "group_sizes_basis": null, "platform": "1708", "taxon": "Homo sapiens", "title": "Gene expression in early stage cervical cancer", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE44001 not downloaded", "asset:data:geo:GSE44001 licence not read", "asset:data:geo:GSE7410 not downloaded", "asset:data:geo:GSE7410 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The R package (glmnet version 4.1.1) executed LASSO regression on the differentially expressed HPV-ferroptosis related genes to filter out redundant factors. Subsequently, univariate/multivariate Cox regression analysis determined the prognostic genes and constructed a Risk score prognostic model (The majority of literature calculates Risk score based on the weighting of the product of gene expression and its coefficients. This study employed multivariate Cox regression to develop a model in which Risk score was determined as the weighting of the product of gene expression and its coefficients.)."}}, {"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": "differential_expression", "specific": "edger", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {"fold_change": "ith a threshold of |Log(FC)|> 1 and p adj < 0"}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "edgeR"}}, {"id": "method-4", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}, {"id": "method-5", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}, {"id": "method-6", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.8, "band": "high", "quote": "Decision curve analysis"}}], "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE7410", "GSE44001"], "resolved_accessions": ["GSE7410", "GSE44001"], "statements": ["The datasets generated and/or analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/)). FerrDb (http://www.zhounan.org/ferrdb/), NCBI-gene (https://www.ncbi.nlm.nih.gov/gene), MSigDB (http://www.gsea-msigdb.org /gsea/msigdb/), Genecard (https://www.genecards.org/) four databases to download the Ferroptosis gene set."], "basis": "The paper names GSE7410, GSE44001 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39302544", "data:geo:GSE44001"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE44001"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:32:31Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10169, "completion_tokens": 2889, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39302544", "type": "paper", "label": "pmid:39302544"}, {"id": "source:pmid:39302544", "type": "manuscript", "label": "PMID 39302544 full text"}, {"id": "data:geo:GSE44001", "type": "dataset", "label": "GEO GSE44001"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39302544", "type": "paper", "label": "pmid:39302544"}, {"id": "source:pmid:39302544", "type": "manuscript", "label": "PMID 39302544 full text"}, {"id": "data:geo:GSE44001", "type": "dataset", "label": "GEO GSE44001"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39302544", "type": "extraction", "used": ["source:pmid:39302544"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE44001", "type": "accession_resolution", "used": ["data:geo:GSE44001"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39302544", "parent_hash": "sha256:04ff6119af3c51b2ec6b46a03b81edfeeb8e9383c58e55ad106b138aaad3b249", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE44001 licence not read", "asset:data:geo:GSE44001 not downloaded", "asset:data:geo:GSE7410 licence not read", "asset:data:geo:GSE7410 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["The R package (glmnet version 4.1.1) executed LASSO regression on the differentially expressed HPV-ferroptosis related genes to filter out redundant factors. Subsequently, univariate/multivariate Cox regression analysis determined the prognostic genes and constructed a Risk score prognostic model (The majority of literature calculates Risk score based on the weighting of the product of gene expression and its coefficients. This study employed multivariate Cox regression to develop a model in which Risk score was determined as the weighting of the product of gene expression and its coefficients.)."], "order_basis": "analysis_only", "id": "assay:1"}]}