Evidence map›Paper›PMID 31632497›Full record

ArticleJournal of Cancer2019

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

Ningrong Ye, Nian Jiang, Chengyuan Feng, Feiyifan Wang, Hanwen Zhang, Harrusin Xiao Bai, Li Yang, Yandong Su, Chunhai Huang, Siyi Wanggou and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of Cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Ningrong YeDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Nian JiangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Chengyuan FengDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Feiyifan WangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Hanwen ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Harrusin Xiao BaiDepartment of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
Li YangDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yandong SuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Chunhai HuangDepartment of Neurosurgery, The First Affiliated Hospital of Jishou University, Jishou, Hunan China.
Siyi WanggouDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xuejun LiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiangya Hospital Central South University · CNCentral South University · CNHospital of the University of Pennsylvania · USSecond Xiangya Hospital of Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

CTSIGene signatureGlioblastomaPrognosis

Identifiers

PMID31632497
PMCPMC6775685
OpenAlexW2972669472

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.