Evidence map›Paper›PMID 38617348›Full record

ArticlebioRxiv : the preprint server for biology2024

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.

Ehsan Saghapour, Zongliang Yue, Rahul Sharma, Sidharth Kumar, Zhandos Sembay, Christopher D Willey, Jake Y Chen

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Ehsan SaghapourDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
Zongliang YueHealth Outcome Research and Policy Department, Harrison College of Pharmacy, Auburn University, AL, US.
Rahul SharmaDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
Sidharth KumarDepartment of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, US.
Zhandos SembayDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.
Christopher D WilleyDepartment of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, US.
Jake Y ChenDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, US.ORCID 0000-0001-8829-7504

Funding

UAB Center for Clinical and Translational Science (CCTS)U54TR001005 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI KIMBERLY, ROBERT P. · 2014 to 2014
$4.6M
Role of bone marrow Tregs in maintaining stromal cell functionR01HL150078 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI WELNER, ROBERT S · 2020 to 2023
$1.5M
NCATS NIH HHS U54 TR001005NHLBI NIH HHS R01 HL150078
6 · The paper itself

Abstract

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

Identifiers

PMID38617348
PMCPMC11014492

What Socratic holds

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LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.