Evidence map›Paper›PMID 39963421›Full record

ArticleComputational and structural biotechnology journal2024

Analysis of transcription profiles for the identification of master regulators as the key players in glioblastoma.

Sergey M Ivanov, Alexey A Lagunin, Olga A Tarasova

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Article
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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

3 authors.

Sergey M IvanovDepartment of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow 119121, Russia.
Alexey A LaguninDepartment of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow 119121, Russia.
Olga A TarasovaDepartment of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow 119121, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma (GBM) is the most common malignant brain tumor with poor overall survival. Current treatment management for GBM has low efficacy, mainly due to high inter-patient heterogeneity. The transcription profiles in GBM define cell properties essential for tumor progression. We have developed an approach for the identification of master regulators (MRs) that are responsible for the gene expression changes in GBM. The approach is based on transcription factor enrichment analysis with subsequent "upstream" analysis in the signaling network. The main feature of the approach is that all calculations are performed for transcription profiles from individual samples, which allows taking into account GBM transcription heterogeneity. We identified 451 MRs that were up-regulated or down-regulated and, thus, were important parts of positive feedback loops. The number of MRs in the samples correlated with the degree of tumor immune infiltration, while the differences in MR profiles were generally consistent with the known GBM subtypes: mesenchymal, classical, and proneural. MRs densely interact with each other in the signaling network that may be associated with the robustness to pharmacological intervention. We identified 102 receptors among MRs, which is coherent with the importance of cell-cell interactions for GBM progression. The role of some of them in GBM is not currently investigated: lysophosphatidic acid receptors 5 and 6, sphingosine-1-phosphate receptor 4, lysophosphatidylserine receptors GPR34 and GPR174, and G protein-coupled receptors 84 and 132 for fatty acids. Information on the revealed MRs can be used to search for novel therapeutic strategies to treat GBM.

Indexed as

Feedback loopsGene expressionGlioblastomaIndividual samplesMaster regulatorsTranscription factors

Identifiers

PMID39963421
PMCPMC11832006

What OpenQuestion holds

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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.