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Identification of metabolism-associated molecular classification and prognostic genes for medulloblastoma based on bioinformatics analysis.

Overview

Authors: Zihan Yan1, Yunwei Ou1,2, Xu Han1, Jian Gong1,3
ORCID iDs: Zihan Yan, Yunwei Ou, Xu Han, Jian Gong
  1. Department of Pediatric Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  2. Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
  3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei City, Anhui Province, China
Institutions: Capital Medical University (China); Beijing Tian Tan Hospital (China)
Journal: Clinics (Sao Paulo, Brazil), volume 81, article 101003
Dates: received 25 August 2025; accepted 1 May 2026; published online 18 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.clinsp.2026.101003 · PMID 42150480 · PMCID PMC13202577 · OpenAlex W7161553822
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: genetics / omics (modality), other condition (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Machine learning
Keywords: Medulloblastoma, Solid brain tumor, Bioinformatics analysis, Metabolism, Gene
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (62276027)
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Objective: Medulloblastoma (MB) is one of the most prevalent solid brain tumors in the pediatric population. Although hundreds of Differentially Expressed Genes (DEGs) have been identified as diagnostic molecules and therapeutic targets for Medulloblastoma (MB), the function of metabolism-associated genes in the pathophysiological mechanisms of MB remains unclear.

Methods: The Gene Expression Omnibus (GEO) provided five datasets that contain mRNA expression profiles and clinical information. In detail, 599 MB samples extracted from GSE85217 were set as the training set (n = 400) and test set (n = 199). For validation, 74 samples from GSE37418, 8 scRNA-seq samples, and 37 RNA-seq samples were used. Nonnegative matrix factorization clustering was conducted, leading to the identification of four MB subclasses (C1, C2, C3, and C4) in both the training and test sets.

Results: C1 and C3 are metabolically active, and C2 involves metabolic hypoactivity; C4 did not exhibit obvious metabolic characteristics. Next, GSE50161, GSE74195, and GSE86574 were used to identify DEGs, and a 17-gene metabolism-associated signature for prognosis prediction was established. The authors performed transcriptome and single-cell sequencing on medulloblastoma bulk specimens to further validate metabolic subclasses and the prognosis prediction model at the transcriptome and cellular level.

Conclusion: The present study classifies Medulloblastoma (MB) based on metabolic signatures, supplementing existing subtype characterization from a metabolic perspective. The authors provide preliminary insights into MB’s metabolic hallmarks and a potential reference for developing multimolecule-based personalized therapies and prognostic tools ‒ with the caveat that the 17-gene signature requires additional multi-center validation to confirm clinical utility.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Other data links

Data availability

The datasets used and/or analyzed during the current study were publicly available from the GEO database, http://www.ncbi.nlm.nih.gov/geo. Analysis scripts used in the current study are available from the corresponding author on reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Authors: added Zihan Yan (0009-0005-4543-7403); Yunwei Ou (0009-0006-3492-7754); Xu Han (0009-0009-8938-7478); Jian Gong (0009-0008-5996-4062); removed Zihan Yan; Yunwei Ou; Xu Han; Jian Gong

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 1 funder, 37 references.

Cite

This paper

Yan, Z., Ou, Y., Han, X., & Gong, J. (2026). Identification of metabolism-associated molecular classification and prognostic genes for medulloblastoma based on bioinformatics analysis. Clinics (Sao Paulo, Brazil), 81, 101003. https://doi.org/10.1016/j.clinsp.2026.101003

BibTeX

@article{yan2026identification,
author = {Yan, Zihan and Ou, Yunwei and Han, Xu and Gong, Jian},
title = {{Identification of metabolism-associated molecular classification and prognostic genes for medulloblastoma based on bioinformatics analysis}},
journal = {Clinics (Sao Paulo, Brazil)},
year = {2026},
month = may,
volume = {81},
pages = {101003},
publisher = {Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo},
issn = {1807-5932},
doi = {10.1016/j.clinsp.2026.101003},
url = {https://doi.org/10.1016/j.clinsp.2026.101003},
pmid = {42150480},
pmcid = {PMC13202577}
}

RIS

TY - JOUR
AU - Yan, Zihan
AU - Ou, Yunwei
AU - Han, Xu
AU - Gong, Jian
TI - Identification of metabolism-associated molecular classification and prognostic genes for medulloblastoma based on bioinformatics analysis
T2 - Clinics (Sao Paulo, Brazil)
J2 - Clinics (Sao Paulo)
PY - 2026
DA - 2026/05/18
VL - 81
SP - 101003
SN - 1807-5932
PB - Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo
DO - 10.1016/j.clinsp.2026.101003
UR - https://doi.org/10.1016/j.clinsp.2026.101003
LA - en
ER -

CSL-JSON

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