ArticleFrontiers in immunology2024
Integrated multi-level omics profiling of disulfidptosis identifis SPAG4 as an innovative immunotherapeutic target in glioblastoma.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Disulfidptosis: Mechanisms, evidence boundaries, and translational opportunities.Redox biology · 2026Review
- Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.Translational cancer research · 2026Article
- Identification of hsa-miR-144-3p as a novel immunotherapeutic target for glioblastoma based on disulfidptosis-related analysis.Discover oncology · 2026Article
- Construction and validation of risk prediction model for glioblastoma associated with cancer stem cells and disulfidptosis.Translational cancer research · 2026Article
- Roles and Impacts of Integrative Medical Interventions in Central Nervous System Tumor Treatment: Multi-Technology Convergence and the Paradigm Shift Toward Functional Reconstruction.CNS neuroscience & therapeutics · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Objective: To investigate the association between disulfidptosis-related genes (DFRGs) and patient prognosis, while concurrently identifying potential therapeutic targets in glioblastoma (GBM). Methods: We retrieved RNA sequencing data and clinical characteristics of GBM patients from the TCGA database. We found there was a total of 6 distinct clusters in GBM, which was identified by the t-SNE and UMAP dimension reduction analysis. Prognostically significant genes in GBM were identified using the limma package, coupled with univariate Cox regression analysis. Machine learning algorithms were then applied to identify central genes. The CIBERSORT algorithm was utilized to assess the immunological landscape across different GBM subtypes. Results: 23 genes, termed DFRGs, were successfully identified, demonstrating substantial potential for establishing a prognostic model for GBM. Single cell analysis revealed a significant correlation between DFRGs and the progression of GBM. Utilizing individual risk scores derived from this model enabled the stratification of patients into two distinct risk groups, revealing significant variations in immune infiltration patterns and responses to immunotherapy. Utilizing the random survival forests algorithm, SPAG4 was identified as the gene with the highest prognostic significance within our model. Conclusions: These findings highlight SPAG4 as a potential GBM therapeutic target and emphasize the complexity of the immune microenvironment in GBM progression.
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