Evidence map›Paper›PMID 40761256›Full record

ArticleFrontiers in oncology2025

Integrative analysis of polyamine metabolism-related genes in gliomas: implications for prognosis and therapy.

Yujia Zhao, Zhenkai Fu, Sijie Chen, Fei Li, Xiaoyu Zhang, Kaidiriye Setiwalidi, Zhiping Ruan, Yu Yao, Lanxin Luo

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. 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

9 authors.

Yujia ZhaoDepartment of Oncology, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, China.
Zhenkai FuDepartment of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
Sijie ChenDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Fei LiDepartment of Toxicology, School of Public Health, the Fourth Military Medical University, Xi'an, Shaanxi, China.
Xiaoyu ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Kaidiriye SetiwalidiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Zhiping RuanDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yu YaoDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Lanxin LuoPrecision Pharmacy and Drug Development Center, Department of Pharmacy, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Tumor transformation and progression are accompanied by multiple carcinogenic pathways that dysregulate polyamine demand and metabolism. The importance of polyamines has demonstrated that their metabolism is a potential therapeutic strategy. Yet, few prognostic models based on polyamine metabolism-related gene risk have been developed for gliomas. Methods: The mRNA expression profiles and variations in 37 polyamine metabolism-related genes (PMRGs) were obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. PMRGs-related risk model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and tested for predictive ability across two independent datasets from the Gene Expression Omnibus (GEO). The landscape of the tumor immune microenvironment and drug sensitivity were investigated systematically using multiple methods based on PMRG-related risk subtypes. Weighted gene co-expression network analysis (WGCNA) was applied to identify the key prognostic genes of the PMRGs. In addition, key genes were validated with regard to their expression and prognostic significance in human glioma tissues. To verify the cell types, single-cell RNA sequencing was performed on the cohorts available at GEO. Results: Based on PMRG clusters, patients with glioma showed significant differences in PMRG expression, prognosis, and biological functions. A 11-gene risk model was constructed, and patients were categorized into high- and low-risk subtype according to the risk score. The high-risk subtype exhibited a poorer prognosis due to its immunosuppressive microenvironment. Furthermore, there were striking differences between the distinct subtypes in terms of immune cell infiltration, anticancer immunity cycle, tumor mutation burden, immune checkpoints, and response to targeted inhibitors. Spermine synthase (SMS) was identified as a key PMRG in patients with gliomas. A significant increase in SMS mRNA and protein expression was observed in tumors compared to normal controls. Single-cell sequencing analyses showed that SMS mRNA was highly expressed in all cell types, except oligodendrocytes. Conclusion: A PMRG-related risk model can be used as a reliable prognostic biomarker in glioma treatment. In addition, polyamine metabolism and function can be successfully targeted therapeutically.

Indexed as

gliomaimmunosuppressionpolyamineprognosisspermine synthase

Identifiers

PMID40761256
PMCPMC12319057

What Socratic 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 Socratic graph.