Evidence map›Paper›PMID 39866460›Full record

ArticleHeliyon2025

Comprehensive analysis reveals cholesterol metabolism-related signature for predicting prognosis and guiding individualized treatment of glioma.

Dengfeng Lu, Fei Wang, Yayi Yang, Aojie Duan, Yubo Ren, Yun Feng, Haiying Teng, Zhouqing Chen, Xiaoou Sun, Zhong Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Dengfeng LuDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Fei WangDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Yayi YangDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Aojie DuanDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Yubo RenDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Yun FengDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Haiying TengDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Zhouqing ChenDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Xiaoou SunDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.
Zhong WangDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, 188Shizi Street, Suzhou, 215006, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Gliomas are the most common intracranial tumors with the highest degree of malignancy. Disturbed cholesterol metabolism is one of the key features of many malignant tumors, including gliomas. This study aimed to investigate the significance of cholesterol metabolism-related genes in prognostic prediction and in guiding individualized treatment of patients with gliomas. Methods: Transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Intraoperative glioma samples retained in our unit and the corresponding clinicopathological information were also collected with the patients' knowledge. Firstly, cholesterol metabolism-related gene signatures (CMRGS) were identified and constructed based on difference analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and univariate/multivariate COX analysis. Then, the role of CMRGS in predicting the prognosis of gliomas and distinguishing immune landscapes was evaluated by using nomograms, survival analysis, enrichment analysis, and immune-infiltration analysis. Finally, the drug sensitivity of gliomas in different risk groups was evaluated using the oncoPredict algorithm, and potentially sensitive chemotherapeutic and molecular-targeted drugs were identified. Results: The prognostic CMRGS contained seven genes: APOE, SCD, CXCL16, FABP5, S100A11, TNFRSF12A, and ELOVL2. Patients were divided into high- and low-risk groups based on the median cholesterol metabolic index (CMI). There were significant differences in clinicopathological characteristics and overall survival between groups. COX analysis suggested that CMRGS was an independent risk factor for glioma prognosis and had a better predictive performance than several classical indicators. In addition, GSEA, immune infiltration analysis showed that CMRGS could differentiate the immune landscapes of patients in groups. The reliability of CMRGS was validated in the CGGA cohort and our Gusu cohort. Finally, 14 drugs sensitive to high-risk patients and 16 drugs sensitive to low-risk patients were identified. Conclusion: The CMRGS reliably predicts glioma prognosis in multiple cohorts and may be useful in guiding individualized treatment.

Indexed as

Cholesterol metabolismDrug sensitivityGliomaImmunoscapePrognostic predictionSignature

Identifiers

PMID39866460
PMCPMC11757779

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.