Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31632497", "kind": "science-record", "revision": 7, "content_hash": "sha256:bc18b20ff273fb18f366980f565f19f392d92014cb1ae62fa5e27f8c436f2387", "created_at": "2026-11-01T15:40:50Z", "updated_at": "2026-11-01T21:22:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients.", "doi": "10.7150/jca.30614", "pmid": "31632497", "authors": ["Ningrong Ye", "Nian Jiang", "Chengyuan Feng", "Feiyifan Wang", "Hanwen Zhang", "Harrusin Xiao Bai", "Li Yang", "Yandong Su", "Chunhai Huang", "Siyi Wanggou", "Xuejun Li"], "journal": "Journal of Cancer", "year": 2019, "abstract": "Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.", "claims": [{"id": "claim-method", "statement": "The Kaplan-Meier method was used to estimate survival time for all the datasets and Xiangya GBM cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31632497", "locator": "sections[18] \"Statistical analysis\" paragraph 4", "quote": "The Kaplan-Meier method was used to estimate survival time for all the datasets and Xiangya GBM cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GBM gene expression profiles and corresponding clinical data were obtained from three public databases: the TCGA dataset (http://cancergenome.nih.gov), the GEO GSE4271 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4271) and the CGGA dataset (http://cgga.org.cn).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31632497", "locator": "sections[11] \"Gene expression datasets and clinical data\" paragraph 2", "quote": "GBM gene expression profiles and corresponding clinical data were obtained from three public databases: the TCGA dataset (http://cancergenome.nih.gov), the GEO GSE4271 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4271) and the CGGA dataset (http://cgga.org.cn)."}], "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:31632497"], "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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