Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30572911", "kind": "science-record", "revision": 8, "content_hash": "sha256:a3f06762e1806dc82837d7cdce271b553c30406ebe664f5b778b9eb3319929d9", "created_at": "2026-11-01T15:40:40Z", "updated_at": "2026-11-01T22:43:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients.", "doi": "10.1186/s12967-018-1744-8", "pmid": "30572911", "authors": ["Wei-Zhen Gao", "Lie-Mei Guo", "Tian-Qi Xu", "Yu-Hua Yin", "Feng Jia"], "journal": "Journal of translational medicine", "year": 2018, "abstract": "BACKGROUND: Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients.\nMETHODS: The protein coding genes (PCGs) and long non-coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n = 76) and a test set (n = 77) and used GSE7696 (n = 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature.\nRESULTS: Six PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD-2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG-lncRNA signature for its predictive power was strongest (AUC = 0.78 in the training dataset). The PCG-lncRNA signature could divide patients into high- risk or low-risk group with significantly different survival (median 7.47 vs. 18.27 months, log-rank test P < 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log-rank test P = 0.001) and the independent set (median 8.93 vs. 16.22 months, log-rank test P = 0.007). Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O-(6)-methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ-chemoradiation into high- and low-risk groups in TCGA and GEO dataset.\nCONCLUSIONS: The PCG-lncRNA signature was a novel prognostic marker to predict survival and TMZ-chemoradiation response in GBM patients after surgery.", "claims": [{"id": "claim-method", "statement": "The Kaplan–Meier analysis and the log-rank test were used to assess and compare survival differences between the low-risk and high-risk groups. ROC analysis was tested to compare the survival predictive power. Furthermore, to test whether the signature was an independent prognostic factor, multivariable Cox regression analysis and data stratification analysis were performed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30572911", "locator": "sections[5] \"Statistical analysis and bioinformatics analysis\" paragraph 2", "quote": "The Kaplan–Meier analysis and the log-rank test were used to assess and compare survival differences between the low-risk and high-risk groups. ROC analysis was tested to compare the survival predictive power. Furthermore, to test whether the signature was an independent prognostic factor, multivariable Cox regression analysis and data stratification analysis were performed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data generated or analysed during this study are available from the corresponding author upon reasonable request.\nAdditional material can be mentioned in the link: https://pan.baidu.com/s/1TKcsqUHUGxN0Y9jlSRVQ_A.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30572911", "locator": "sections[21] \"Availability of data and materials\" paragraphs 1-3", "quote": "The data generated or analysed during this study are available from the corresponding author upon reasonable request.\nAdditional material can be mentioned in the link: https://pan.baidu.com/s/1TKcsqUHUGxN0Y9jlSRVQ_A."}], "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:30572911"], "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:GSE7696"], "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:GSE7696"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30572911", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-018-1744-8", "sha256": 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Kaplan–Meier analysis and the log-rank test were used to assess and compare survival differences between the low-risk and high-risk groups. 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