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Ubiquitin-Proteasome System-Related Prognostic Model and Immune Landscape in Glioblastoma.

Overview

Authors: Da Huo1, Yue Feng2, Zheng Yu3, Wanting Xie4, Hai Jin1
ORCID iDs: Da Huo, Yue Feng, Zheng Yu, Wanting Xie, Hai Jin
  1. Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China, syjqzyy.com
  2. Department of General Practice, General Hospital of Northern Theater Command, Shenyang, China, syjqzyy.com
  3. Department of Laboratory, 32295 Army Hospital, Liaoyang, China
  4. Department of Nursing, General Hospital of Northern Theater Command, Shenyang, China, syjqzyy.com
Institutions: General Hospital of Shenyang Military Region (China)
Journal: International journal of genomics, volume 2026, issue 1, article 2045937
Dates: received 12 March 2026; accepted 19 June 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1155/ijog/2045937 · PMID 42488132 · PMCID PMC13390022 · OpenAlex W7170067539
Open access: gold, a free copy (OpenAlex)
Status: data only (how the code is found and verified)
Categories: genetics / omics (modality), other condition (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: glioblastoma, immunity, macrophage, prognosis, scRNA-Seq, spatial transcriptome
Topic: Ferroptosis and cancer prognosis (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Background: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study is aimed at establishing an ubiquitin‐proteasome system (UPS)–related prognostic model and investigating its link to immune infiltration and therapy response.

Materials and Methods: GBM datasets were obtained from public databases. Ubiquitin‐proteasome system–related genes (UPSGs) were identified from literature. Consensus clustering defined UPS‐based GBM subtypes. Differentially expressed genes (DEGs) were screened, and a prognostic model was constructed using univariate Cox, least absolute shrinkage and selection operator (LASSO), and stepwise regression. The model′s performance was validated using survival analysis and time‐dependent receiver operating characteristic (ROC) curves. Immune infiltration was assessed using single‐sample gene set enrichment analysis (ssGSEA), TIMER, and ESTIMATE. Drug sensitivity was assessed by correlating the half‐maximal inhibitory concentration (IC50) of candidate drugs with the risk score. Single‐cell RNA sequencing data were used to characterize UPSG expression across distinct cell subpopulations in GBM. For in vitro validation, key UPSGs were silenced in GBM cell lines, and cell proliferation, migration, and invasion were measured using Cell Counting Kit‐8 (CCK‐8), wound healing, and Transwell assays, respectively.

Results: Two UPS‐related GBM subtypes were identified. Six genes (IGFBP6, CTSD, SPAG4, ZNF560, COL22A1, and HOXC13) formed the prognostic model, where high Riskscore indicated poor survival. High Riskscore correlated with greater immune infiltration, including CD8+ T cells and macrophages. IC50 values of 24 drugs were significantly associated with Riskscore. Single‐cell analysis revealed seven GBM subpopulations; notably, COL22A1 was enriched in MES‐like cells, and CTSD in macrophages. IGFBP6 promoted GBM cell proliferation, migration, and invasion.

Conclusion: This study establishes a UPS‐based prognostic model for GBM that links immune infiltration and drug sensitivity, providing potential biomarkers and therapeutic targets for GBM.

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

Code

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Data

Datasets cited

Other data links

Data Availability Statement

The datasets generated and/or analyzed during the current study are available in the [GSE273274 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273274)] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE273274 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273274)], and the [GSE273275 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273275)] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE273275 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273275)].

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Issue: n/a → 1

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 41 references.

Cite

This paper

Huo, D., Feng, Y., Yu, Z., Xie, W., & Jin, H. (2026). Ubiquitin-Proteasome System-Related Prognostic Model and Immune Landscape in Glioblastoma. International journal of genomics, 2026(1), 2045937. https://doi.org/10.1155/ijog/2045937

BibTeX

@article{huo2026ubiquitin,
author = {Huo, Da and Feng, Yue and Yu, Zheng and Xie, Wanting and Jin, Hai},
title = {{Ubiquitin-Proteasome System-Related Prognostic Model and Immune Landscape in Glioblastoma}},
journal = {International journal of genomics},
year = {2026},
month = jul,
volume = {2026},
number = {1},
pages = {2045937},
publisher = {Wiley},
issn = {2314-436X},
doi = {10.1155/ijog/2045937},
url = {https://doi.org/10.1155/ijog/2045937},
pmid = {42488132},
pmcid = {PMC13390022}
}

RIS

TY - JOUR
AU - Huo, Da
AU - Feng, Yue
AU - Yu, Zheng
AU - Xie, Wanting
AU - Jin, Hai
TI - Ubiquitin-Proteasome System-Related Prognostic Model and Immune Landscape in Glioblastoma
T2 - International journal of genomics
J2 - Int J Genomics
PY - 2026
DA - 2026/07/22
VL - 2026
IS - 1
SP - 2045937
SN - 2314-436X
PB - Wiley
DO - 10.1155/ijog/2045937
UR - https://doi.org/10.1155/ijog/2045937
LA - en
ER -

CSL-JSON

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