Evidence map›Paper›PMID 33790946›Full record

ArticleFrontiers in genetics2021

A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme.

Zhentao Liu, Hao Zhang, Hongkang Hu, Zheng Cai, Chengyin Lu, Qiang Liang, Jun Qian, Chunhui Wang, Lei Jiang

Abstract read
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Article
  2. Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025
    Review
  3. Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
  4. Article
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  12. Article
  13. The predictive prognostic values ofAnnals of translational medicine · 2021
    Article
  14. Review
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

9 authors.

Zhentao LiuDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Hao ZhangDepartment of Orthopaedic Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Hongkang HuDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Zheng CaiDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Chengyin LuDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Qiang LiangDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Jun QianDepartment of Neurosurgery, Tongji Hospital, Shanghai Tong Ji University School of Medicine, Shanghai, China.
Chunhui WangDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Lei JiangDepartment of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

Indexed as

biomarkersglioblastoma multiformeLASSOprognosissix-mRNA signature

Identifiers

PMID33790946
PMCPMC8006298

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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.