Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:23502430", "kind": "science-record", "revision": 3, "content_hash": "sha256:e81858e9a9406a43d530627293c21dd1011b3ef3bed9f420a08ad7fb9765384c", "created_at": "2026-11-01T15:40:02Z", "updated_at": "2026-11-01T21:19:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis.", "doi": "10.1093/neuonc/not024", "pmid": "23502430", "authors": ["Yong-Wan Kim", "Dimpy Koul", "Se Hoon Kim", "Agda Karina Lucio-Eterovic", "Pablo R Freire", "Jun Yao", "Jing Wang", "Jonas S Almeida", "Ken Aldape", "W K Alfred Yung"], "journal": "Neuro-oncology", "year": 2013, "abstract": "BACKGROUND: The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM).\nMETHODS: Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant.\nRESULTS: To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables.\nCONCLUSIONS: By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.", "claims": [{"id": "claim-target-result", "statement": "Using multivariate Cox regression on integrated TCGA gene-expression and copy-number aberration data, the 42-probe-set survival model significantly stratified GBM patients into three prognostic groups, was validated in other public GBM gene-expression datasets, and retained independent prognostic value (P < .018).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:23502430", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset)."}, {"asset": "source:pmid:23502430", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using multivariate Cox regression on integrated TCGA gene-expression and copy-number aberration data, the 42-probe-set survival model significantly stratified GBM patients into three prognostic groups, was validated in other public GBM gene-expression datasets, and retained independent prognostic value (P < .018).", "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:23502430"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "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": {"median_survival": "r plots showed that the median survival of group 3 was markedly"}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:23502430", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/neuonc/not024", "sha256": "sha256:b21a18d6d4da79519b6e10898057d96a1a6e80b2b9052b7c72fba9c490fc393d", "bytes": 1431, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/neuonc/not024", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC3688008", "source": "ncbi_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {"median_survival": "r plots showed that the median survival of group 3 was markedly"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-2", "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": 0.7, "band": "medium", "quote": "Cox 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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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