ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1629102
This article is part of the Research TopicCommunity Series in Post-Translational Modifications of Proteins in Cancer Immunity and Immunotherapy, Volume IVView all 4 articles
Single-cell sequencing combined with machine learning to identify glioma biomarkers and therapeutic targets
Provisionally accepted
- Jiaxing University, Jiaxing, China
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Background: The purpose of this study is to utilize single-cell sequencing data to explore glioma heterogeneity and identify key biomarkers associated with glioblastoma multiforme (GBM) relapse using machine learning. Methods: Single-cell sequencing and transcriptome data for gliomas were obtained from the GEO (GSE159416, GSE159605, and GSE186057) and TCGA databases. A prognostic model based on differentiation-related genes (DRGs) was constructed using weighted correlation network analysis, univariate Cox regression, and LASSO analysis. Key genes were identified using LASSO and SVM-RFE, with intersecting genes selected as the final set of key genes. Further analyses examined immune infiltration patterns and functional pathways. Importantly, we analyzed the relationship between prognostic-related genes and ubiquitination, and further characterized the characteristics of ubiquitination-related prognostic genes. In addition, we performed CCK-8 assays, colony formation, Transwell invasion assays, apoptosis assays to determine the role of ETV4 in glioma. Results: Examination of single-cell RNA-seq data from the GEO database revealed three distinct cell differentiation stages in glioma tissues. Marker genes for each of these cell states were combined to form DRGs. A 16-gene DRG signature was developed for predicting the survival of glioma patients. Machine learning identified four important genes with high AUCs in both training and test sets. Notably, 13 out of 16 genes in the DRG signature are ubiquitin-related, highlighting the involvement of ubiquitination in GBM. Moreover, we reported that inhibition of ETV4 attenuates cell proliferation and invasion in glioma cells. Conclusion：Our prognostic model, based on the differentiation-related gene signatures, may be valuable for predicting prognosis and immunotherapy response in glioma patients. Characterizing these ubiquitination-associated features may elucidate the molecular mechanisms driving GBM progression and offer novel insights for its diagnosis and treatment. Additionally, machine learning identified four biomarkers with potential for aiding in the diagnosis and treatment of GBM.
Keywords: Glioma, ScRNA-seq, prognosis, biomarker, machine learning
Received: 15 May 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Yan, Chu, Zhong and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Genghuan Wang, Jiaxing University, Jiaxing, China
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