Stratifying osteosarcoma patients using an epigenetic modification-related prognostic signature: implications for immunotherapy and chemotherapy selection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39145082", "kind": "science-record", "revision": 3, "content_hash": "sha256:f6747d513265f666dbeecdbbf8b6dc3a9c9a788fd5ad97634e4ed84345420a8b", "created_at": "2026-11-01T20:29:16Z", "updated_at": "2026-11-01T22:12:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Stratifying osteosarcoma patients using an epigenetic modification-related prognostic signature: implications for immunotherapy and chemotherapy selection.", "doi": "10.21037/tcr-23-2300", "pmid": "39145082", "authors": ["Zhichao Li", "Yong Xue", "Xianxing Huang", "Gang Xiao"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Osteosarcoma (OS) poses significant challenges in treatment and lacks reliable prognostic markers. Epigenetic alterations play a crucial role in disease progression. This study aimed to develop an accurate prognostic signature for OS using epigenetic modification genes (EMGs).\nMethods: The Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-OS cohort was analyzed. Univariate Cox analysis identified survival-associated EMGs. Based on least absolute shrinkage and selection operator (LASSO) regression and multivariate analysis, a 6-gene prognostic signature termed the epigenetic modification-related prognostic signature (EMRPS) was derived in the testing cohort. Kaplan-Meier and receiver operating characteristic (ROC) curve analysis confirmed predictive accuracy through internal and external validation (GEO accession GSE21257). A prognostic nomogram incorporating EMRPS and clinical features was constructed. Transcriptomic analysis including differential gene expression, Gene Ontology (GO), gene set enrichment analysis (GSEA), and immune infiltration analysis was conducted to explore mechanisms linking EMRPS to OS prognosis. Additionally, EMRPS impact on drug sensitivity was predicted.\nResults: A 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed. The high-risk group showed significantly shorter survival, consistently observed in both internal and external validation. EMRPS demonstrated high predictive efficacy for 1-, 3-, and 5-year overall survival, with area under curve (AUC) >0.85 in training and ~0.7 in testing. The nomogram integrating age, gender, metastasis status, and EMRPS exhibited high predictive performance based on concordance index analysis. Mechanistic analysis indicated the low-risk group had increased immune infiltration and activity with higher immune checkpoint expression, reflecting an immune-activated tumor microenvironment (TME) suitable for immunotherapy. Drug sensitivity analysis revealed the low-risk group had increased sensitivity to cisplatin, a first-line OS chemotherapy.\nConclusions: Our study successfully established an efficient EMRPS and nomogram, highlighting their potential as novel prognostic markers and indicators for selecting appropriate immunotherapy and chemotherapy candidates in OS treatment.", "claims": [{"id": "claim-method", "statement": "To identify key survival-associated EMGs in the training cohort, we employed the Cox regression model with a least absolute shrinkage and selection operator (LASSO) penalty.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145082", "locator": "sections[7] \"Construction and validation of epigenetic modification-related prognostic signature (EMRPS)\" paragraph 2", "quote": "To identify key survival-associated EMGs in the training cohort, we employed the Cox regression model with a least absolute shrinkage and selection operator (LASSO) penalty."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded RNA-sequencing data and clinical information of 88 OS patients from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov, TARGET-OS project).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145082", "locator": "sections[4] \"Patient characteristics and epigenetic-related genes\" paragraph 2", "quote": "We downloaded RNA-sequencing data and clinical information of 88 OS patients from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov, TARGET-OS project)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39145082", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed.", "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:39145082"], "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:GSE21257"], "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:GSE21257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39145082", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2300", "sha256": "sha256:ab74584fb105ae62701453889cc6c02148e3cb36161770d78591bc29be82b7c6", "bytes": 57439, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-23-2300", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11319966", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21257", "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=GSE21257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21257", "version": null, "note": "We downloaded RNA-sequencing data and clinical information of 88 OS patients from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov, TARGET-OS project).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We downloaded RNA-sequencing data and clinical information of 88 OS patients from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov, TARGET-OS project).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM530667", "GSM530899", "GSM531283", "GSM531284", "GSM531285", "GSM531286", "GSM531287", "GSM531288", "GSM531289", "GSM531290", "GSM531291", "GSM531292", "GSM531293", "GSM531294", "GSM531295", "GSM531296", "GSM531297", "GSM531298", "GSM531299", "GSM531300", "GSM531301", "GSM531302", "GSM531303", "GSM531304", "GSM531305", "GSM531306", "GSM531307", "GSM531308", "GSM531309", "GSM531310", "GSM531311", "GSM531312", "GSM531313", "GSM531314", "GSM531319", "GSM531320", "GSM531321", "GSM531322", "GSM531323", "GSM531324", "GSM531325", "GSM531326", "GSM531327", "GSM531328", "GSM531329", "GSM531330", "GSM531331", "GSM531332", "GSM531333", "GSM531334", "GSM531335", "GSM531351", "GSM531352"], "sample_count": 53, "group_sizes": null, "group_sizes_basis": null, "platform": "10295", "taxon": "Homo sapiens", "title": "Genome-wide gene expression profiling on pre-chemotherapy biopsies of osteosarcoma patients who developed metastases within 5yrs (n=34) and patients who did not develop metastases within 5yrs (n=19)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE21257 not downloaded", "asset:data:geo:GSE21257 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": "To identify key survival-associated EMGs in the training cohort, we employed the Cox regression model with a least absolute shrinkage and selection operator (LASSO) penalty."}}, {"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": 0.8, "band": "high", "quote": "logistic regression"}}, {"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"}}], "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": ["GSE21257"], "resolved_accessions": ["GSE21257"], "statements": ["We downloaded RNA-sequencing data and clinical information of 88 OS patients from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov, TARGET-OS project)."], "basis": "The paper names GSE21257 in Gene Expression Omnibus (GEO) database 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:39145082", "data:geo:GSE21257"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE21257"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:28:36Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 14394, "completion_tokens": 1196, "cached_tokens": 256}}, "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:39145082", "type": "paper", "label": "pmid:39145082"}, {"id": "source:pmid:39145082", "type": "manuscript", "label": "PMID 39145082 full text"}, {"id": "data:geo:GSE21257", "type": "dataset", "label": "GEO GSE21257"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39145082", "type": "paper", "label": "pmid:39145082"}, {"id": "source:pmid:39145082", "type": "manuscript", "label": "PMID 39145082 full text"}, {"id": "data:geo:GSE21257", "type": "dataset", "label": "GEO GSE21257"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39145082", "type": "extraction", "used": ["source:pmid:39145082"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE21257", "type": "accession_resolution", "used": ["data:geo:GSE21257"], "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:39145082", "parent_hash": "sha256:327455c1d959507e6b235676a2bb7baa7535f9286c2400bd8b407fbb95fc2b44", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE21257 licence not read", "asset:data:geo:GSE21257 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": ["To identify key survival-associated EMGs in the training cohort, we employed the Cox regression model with a least absolute shrinkage and selection operator (LASSO) penalty."], "order_basis": "analysis_only", "id": "assay:1"}]}