Evidence map›Paper›PMID 39815665›Full record

SynthesisCNS neuroscience & therapeutics2025

A Risk Model Based on Ferroptosis-Related Genes OSMR, G0S2, IGFBP6, IGHG2, and FMOD Predicts Prognosis in Glioblastoma Multiforme.

Yaqiu Wu, Ling Liu, Zhili Li, Tian Zhang, Qi Wang, Meixiong Cheng

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Comprehensive analysis of the potential role of Ferroptosis-related genes in glioblastoma multiforme.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Article
  4. Review
  5. Article
  6. Recent advances on the role of G0S2.Discover oncology · 2025
    Review
  7. Article
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

6 authors.

Yaqiu WuDepartment of Neurosurgery Intensive Care Unit, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ling LiuDepartment of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Zhili LiDepartment of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Tian ZhangDepartment of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Qi WangDepartment of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.ORCID 0009-0002-4614-5478
Meixiong ChengDepartment of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Funding

Sichuan Science and Technology Program (2024NSFSC0614)
6 · The paper itself

Abstract

backgroundGlioblastoma multiforme (GBM) is a common and highly aggressive brain tumor with a poor prognosis. However, the prognostic value of ferroptosis-related genes (FRGs) and their classification remains insufficiently studied.

objectiveThis study aims to explore the significance of ferroptosis classification and its risk model in GBM using multi-omics approaches and to evaluate its potential in prognostic assessment.

methodsFerroptosis-related genes (FRGs) were retrieved from databases such as FerrDB. The TCGA-GBM and CGGA-GBM datasets were used as training and testing cohorts, respectively. Univariate Cox regression and LASSO regression analyses were performed to establish a risk model comprising five genes (OSMR, G0S2, IGFBP6, IGHG2, FMOD). A Meta-analysis of integrated TCGA and GTEx data was conducted to examine the differential expression of these genes between GBM and normal tissues. Key gene protein expression differences were analyzed using CPTAC and HPA databases. Single-cell RNA sequencing (scRNA-seq) analysis was employed to explore the cell type-specific distribution of these genes.

resultsThe five-gene risk model demonstrated significant prognostic value in GBM. Meta-analysis revealed distinct expression patterns of the identified genes between GBM and normal tissues. Protein expression analysis confirmed these differences. scRNA-seq analysis highlighted the diverse distribution of these genes across different cell types, offering insights into their biological roles.

conclusionThe ferroptosis-based risk model provides valuable prognostic insights into GBM and highlights potential therapeutic targets, emphasizing the biological significance of ferroptosis-related genes in tumor progression.

Indexed as

Brain NeoplasmsFerroptosisGlioblastomaBiomarkers, TumorDatabases, GeneticHumansInsulin-Like Growth Factor Binding ProteinsPrognosisBiomarkers, TumorInsulin-Like Growth Factor Binding ProteinsFerroptosisGene and Protein ExpressionGlioblastoma MultiformeMulti‐omicsRisk Model

Identifiers

PMID39815665
PMCPMC11735466

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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.