Evidence map›Paper›PMID 38601343›Full record

ArticleFrontiers in neurology2024

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival.

Shelli R Kesler, Rebecca A Harrison, Alexa De La Torre Schutz, Hayley Michener, Paris Bean, Veronica Vallone, Sarah Prinsloo

Abstract read
In one paragraph

Article in Frontiers in neurology, 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

5 · Who and what money

Authors and funding

7 authors.

Shelli R KeslerDivision of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States.
Rebecca A HarrisonDivision of Neurology, BC Cancer, The University of British Columbia, Vancouver, BC, Canada.
Alexa De La Torre SchutzDivision of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States.
Hayley MichenerDepartment of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States.
Paris BeanDepartment of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States.
Veronica ValloneDepartment of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States.
Sarah PrinslooDepartment of Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States.

Funding

Using Connectomics and Machine Learning to Predict Survival in Diffuse GliomaR03CA241862 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI KESLER, SHELLI R · 2021 to 2022
$174k
NCI NIH HHS R03 CA241862
6 · The paper itself

Abstract

Introduction: Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. Methods: We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. Results: We found that all 17 genes were significantly co-localized with brain connectivity ( Discussion: Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

Indexed as

connectomegliomaimaging transcriptomicsMRItranscriptome

Identifiers

PMID38601343
PMCPMC11004301

What Socratic holds

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LicenceCC BY
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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 Socratic graph.