A 63 signature genes prediction system is effective for glioblastoma prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29393370", "kind": "science-record", "revision": 4, "content_hash": "sha256:5fa877ce509296b2a551097c25eb4e7e197a65f726c9247e16d052460cfbbf48", "created_at": "2026-11-01T15:40:31Z", "updated_at": "2026-11-01T21:21:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 63 signature genes prediction system is effective for glioblastoma prognosis.", "doi": "10.3892/ijmm.2018.3422", "pmid": "29393370", "authors": ["Yang Zhang", "Jiaming Xu", "Xiangdong Zhu"], "journal": "International journal of molecular medicine", "year": 2018, "abstract": "The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM). Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis‑associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA), as well as the GEO dataset. Finally, a co‑expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co‑expression network of these prognosis‑associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33x10‑6, 1.63x10‑4 and 0.00534, respectively). The co‑expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase Cγ (PRKCG), protein kinase Cβ (PRKCB) and calcium/calmodulin‑dependent protein kinase IIα (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.", "claims": [{"id": "claim-method", "statement": "Based on the co-expression network of prognosis-associated genes, a prognostic prediction system comprising 63 signature genes was constructed using Bayes discriminant analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29393370", "locator": "sections[15] \"Construction of prognostic prediction system\" paragraph 2", "quote": "Based on the co-expression network of prognosis-associated genes, a prognostic prediction system comprising 63 signature genes was constructed using Bayes discriminant analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA expression data for GBM were downloaded from TCGA database (https://gdc-portal.nci.nih.gov/) on 25th Dec 2016, including 154 tumor samples (survival time information was available for 152 samples) and 13 normal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29393370", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "The mRNA expression data for GBM were downloaded from TCGA database (https://gdc-portal.nci.nih.gov/) on 25th Dec 2016, including 154 tumor samples (survival time information was available for 152 samples) and 13 normal samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An effective prognostic prediction system for GBM patients was constructed and validated using Cox regression, Kaplan-Meier survival analysis, correlation-based co-expression networks, Bayes discriminant analysis, and ROC analysis of TCGA data with validation in GSE13041 and CGGA data.", "kind": "quantitative", "target": {"value": 3.4, "unit": "correlation coefficient", "tolerance": null, "source": "The effectiveness of the prognostic prediction system was evaluated by the receiver operating characteristic (ROC) curve using the pROC package in R3.4.1 (https://cran.r-project.org/web/packages/pROC/index.html)."}, "evidence": [{"asset": "source:pmid:29393370", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In the present study, an effective prognostic prediction system for GBM patients was constructed and validated."}, {"asset": "source:pmid:29393370", "locator": "sections[5] \"Identification of prognosis-associated DEGs\" paragraph 2", "quote": "Cox regression analysis in the survival package (13) was utilized to select the prognosis-associated genes from the overlapping DEGs, based on the expression values and survival status data."}, {"asset": "source:pmid:29393370", "locator": "sections[7] \"Construction of a prognostic prediction system\" paragraph 2", "quote": "Based on this grouping, Bayes discriminant analysis was performed to analyze the genes in the co-expression network constructed for the prognosis-associated DEGs by using the discriminant Bayes function in R language (15,16)."}, {"asset": "source:pmid:29393370", "locator": "sections[7] \"Construction of a prognostic prediction system\" paragraph 2", "quote": "The effectiveness of the prognostic prediction system was evaluated by the receiver operating characteristic (ROC) curve using the pROC package in R3.4.1 (https://cran.r-project.org/web/packages/pROC/index.html)."}, {"asset": "source:pmid:29393370", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948."}, {"asset": "source:pmid:29393370", "locator": "sections[16] \"Validation of the prognostic prediction system\" paragraph 2", "quote": "In order to validate the performance of the prognostic prediction system, it was tested on the GEO GSE13041 and CGGA datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An effective prognostic prediction system for GBM patients was constructed and validated using Cox regression, Kaplan-Meier survival analysis, correlation-based co-expression networks, Bayes discriminant analysis, and ROC analysis of TCGA data with validation in GSE13041 and CGGA data.", "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:29393370"], "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. 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