ArticleHuman genomics2024
The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis.
Article in Human genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas.Molecular and cellular biochemistry · 2026Article
- Development and validation of a robust cuproptosis related signature for primary glioma via machine learning aided by loop training and validation.Discover oncology · 2026Article
- SLC31A1 knockdown mitigates post-MI heart failure via regulation of copper metabolism.Frontiers in immunology · 2026Article
- Cuproptosis-driven astrocyte reactivity exacerbates experimental cerebral malaria pathogenesis.Parasites & vectors · 2025Article
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10 authors.
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Abstract
backgroundEvidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored.
methodsWe screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives.
resultsFive genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines.
conclusionThe CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.
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