Evidence map›Paper›PMID 40313243›Full record

ArticleFrontiers in oncology2025

Revealing propionate metabolism-related genes in glioblastoma and investigating their underlying mechanisms.

Yuchen Sun, Huijuan Wang

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yuchen SunDepartment of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Huijuan WangDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Propionate metabolism may affect tumor growth and aggressiveness, but the role of propionate metabolism-related genes (PMRGs) in glioblastoma (GBM) remains poorly understood. Methods: Differentially expressed PMRGs (DE-PMRGs) were identified by comparing differentially expressed genes (DEGs) between GBM and normal tissues using TCGA-GBM, GSE42669, GSE162631 datasets. Functional enrichment analysis of DE-PMRGs was performed, followed by univariate Cox regression and least absolute shrinkage with selection operator (LASSO) analysis to identify potential prognostic biomarkers. In addition, prognostic models were developed and validated using independent cohorts. Genomic enrichment analysis (GSEA) was used to assess immune-related pathways in different risk subgroups. Finally, biomarker expression was confirmed using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Differential expression analysis identified a total of 180 DE-PMRGs, which were strongly associated with drug response and insulin signaling pathways. Six biomarkers (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) were identified to be associated with prognosis. The accuracy of the prognostic model was confirmed using the GSE42669 dataset, with risk score and MGMT promoter status identified as independent prognostic factors. GSEA showed enrichment of immune response activation and cell cycle regulatory pathways. qRT-PCR validation showed up-regulation of PNPLA3 and SARDH, and down-regulation of ADSL, in tumor tissues. Conclusions: This study identified six PMRGs (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) as potential prognostic biomarkers for glioblastoma. These biomarkers reveal the role of propionate metabolism in the progression of glioblastoma and may serve as important indicators of patient prognosis and treatment strategies.

Indexed as

biomarkerglioblastomaprognosispropionate metabolismsingle cell analysis

Identifiers

PMID40313243
PMCPMC12043635

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