Evidence map›Paper›PMID 37237462›Full record

ArticleBiology2023

Identification of Prominent Genes between 3D Glioblastoma Models and Clinical Samples via GEO/TCGA/CGGA Data Analysis.

Brandon Wee Siang Phon, Saatheeyavaane Bhuvanendran, Qasim Ayub, Ammu Kutty Radhakrishnan, Muhamad Noor Alfarizal Kamarudin

Open access · goldAbstract read
In one paragraph

Article in Biology, 2023. 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
1.4field-weighted citation impact, top 17% of its field
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, 5 citations in OpenAlex.

  1. Review
  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 at 1 institution in 1 country.

Brandon Wee Siang PhonJeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.ORCID 0000-0002-7992-9597
Saatheeyavaane BhuvanendranJeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.ORCID 0000-0002-3889-0163
Qasim AyubSchool of Science, Monash University Malaysia, Bandar Sunway 47500, Malaysia.
Ammu Kutty RadhakrishnanJeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.ORCID 0000-0003-2638-907X
Muhamad Noor Alfarizal KamarudinJeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.ORCID 0000-0003-2505-1018
Monash University Malaysia · MY

Funding

jeffrey cheah school of medicine and health sciences SED000032jeffrey cheah school of medicine and health sciences SED000057
6 · The paper itself

Abstract

A paradigm shift in preclinical evaluations of new anticancer GBM drugs should occur in favour of 3D cultures. This study leveraged the vast genomic data banks to investigate the suitability of 3D cultures as cell-based models for GBM. We hypothesised that correlating genes that are highly upregulated in 3D GBM models will have an impact in GBM patients, which will support 3D cultures as more reliable preclinical models for GBM. Using clinical samples of brain tissue from healthy individuals and GBM patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases, several genes related to pathways such as epithelial-to-mesenchymal transition (EMT)-related genes (

Indexed as

3D cultureCGGAdifferential expressionEMTGlioblastomaTCGAtherapy resistance

Identifiers

PMID37237462
PMCPMC10215494
OpenAlexW4366987718

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.