A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31032367", "kind": "science-record", "revision": 6, "content_hash": "sha256:e526ff6189411966c7ee951bb974dd9f02271a7174c0628a886d1b719f5da7e6", "created_at": "2026-11-01T15:40:44Z", "updated_at": "2026-11-01T21:22:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients.", "doi": "10.1155/2019/9506461", "pmid": "31032367", "authors": ["Ziming Hou", "Jun Yang", "Hao Wang", "Dongyuan Liu", "Hongbing Zhang"], "journal": "BioMed research international", "year": 2019, "abstract": "OBJECTIVE: This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.\nMETHODS: Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.\nRESULTS: We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10-9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.\nCONCLUSION: The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.", "claims": [{"id": "claim-method", "statement": "After that, least absolute shrinkage and selection operator (LASSO) penalized Cox-Proportional Hazards (Cox-PH) model was used to further optimize the prognostic genes (Set Y).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31032367", "locator": "sections[3] \"2.1. Workflow of Construction of Prognostic Models\" paragraph 2", "quote": "After that, least absolute shrinkage and selection operator (LASSO) penalized Cox-Proportional Hazards (Cox-PH) model was used to further optimize the prognostic genes (Set Y)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31032367", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "All data generated or analysed during this study are included in this published article."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:31032367"], "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:GSE13041"], "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:GSE13041"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31032367", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2019/9506461", "sha256": 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