Evidence map›Paper›PMID 40124014›Full record

ArticleACS omega2025

Unveiling a Novel Glioblastoma Deep Molecular Profiling: Insight into the Cancer Cell Differentiation-Related Mechanisms.

Weili Zhou, Hongtao Ruan, Lihua Zhu, Shunqiang Chen, Muyi Yang

Abstract read
In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Weili ZhouDepartment of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan 450003, China.ORCID https://orcid.org/0009-0008-4632-2650
Hongtao RuanDepartment of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan 450003, China.
Lihua ZhuDepartment of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan 450003, China.
Shunqiang ChenDepartment of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan 450003, China.
Muyi YangDepartment of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan 450003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundthe sophisticated cellular heterogeneity of cell populations in glioblastoma (GBM) has been a key factor influencing tumor progression and response to therapy. The lack of more precise stratification based on cellular differentiation status poses a great challenge to therapeutic strategies. MATERIALS AND

methodsharnessing the bulk multiomics and single-nucleus RNA sequencing data available from the National Center for Biotechnology Information (NCBI) and The Cancer Genome Atlas (TCGA) Program repositories, we developed a novel and accurate GBM risk classification using an ensemble consensus clustering approach based on the junction of prognosis and trajectory analysis. Comprehensive cluster labeling and multiomics data characterization were also performed.

resultsa novel GBM stratification model was constructed using 45 malignant cell fate genes: (a) energy metabolism-enhanced-type GBM; (b) invasion-enhanced-type GBM; (c) invasion-attenuated-type GBM; and (d) glycolysis-dominant energy metabolism-enhanced-type GBM. The biological plausibility of the model was verified through a range of comprehensive analyses of multiomics data, showing that cases with invasion-attenuated-type were the best prognosis and energy metabolism-enhanced-type the poorest.

conclusionsthe study has uncovered GBM complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying tumorigenesis. This precise stratification system provided implications for further studies of individual therapies.

Identifiers

PMID40124014
PMCPMC11923693

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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.