Evidence map›Paper›PMID 33102518›Full record

ReviewFrontiers in molecular biosciences2020

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy.

Pei Zhang, Qin Xia, Liqun Liu, Shouwei Li, Lei Dong

Abstract readReview
In one paragraph

Review in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.

0numbers the graph read from it
0cells of the map it votes in
101citing 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

101 citing papers in PubMed.

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  13. Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025
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41 more citing papers are in PubMed but not listed here.

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

5 authors.

Pei ZhangSchool of Life Sciences, Beijing Institute of Technology, Beijing, China.
Qin XiaSchool of Life Sciences, Beijing Institute of Technology, Beijing, China.
Liqun LiuSchool of Life Sciences, Beijing Institute of Technology, Beijing, China.
Shouwei LiDepartment of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
Lei DongSchool of Life Sciences, Beijing Institute of Technology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Indexed as

DNA methylation-based subtypegenetic alteration-based subtypeglioblastomamolecular heterogeneitysubtype-specific therapytranscription-based subtype

Identifiers

PMID33102518
PMCPMC7506064

What OpenQuestion holds

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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.