ArticleFrontiers in immunology2022
A novel model based on necroptosis-related genes for predicting immune status and prognosis in glioma.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Limitations of nomogram models in predicting survival outcomes for glioma patients.Frontiers in immunology · 2025Pooled it
- Construction and validation of a model based on necroptosis-related genes for predicting glioma prognosis.Discover oncology · 2026Article
- HIST1H2BJ is associated with poor prognosis and an immune-altered tumor microenvironment in glioma.Frontiers in immunology · 2026Article
- A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis.Discover oncology · 2025Article
- A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy.Discover oncology · 2025Article
- LRP5 enhances glioma cell proliferation by modulating the MAPK/p53/cdc2 pathway.International journal of medical sciences · 2025Article
- Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.Cancer cell international · 2023Article
- A novel signature of cuproptosis-related lncRNAs predicts prognosis in glioma: Evidence from bioinformatic analysis and experiments.Frontiers in pharmacology · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Glioma is a highly aggressive brain cancer with a poor prognosis. Necroptosis is a form of programmed cell death occurring during tumor development and in immune microenvironments. The prognostic value of necroptosis in glioma is unclear. This study aimed to develop a prognostic glioma model based on necroptosis. Methods: A necroptosis-related risk model was constructed by Cox regression analysis based on The Cancer Genome Atlas (TCGA) training set, validated in two Chinese Glioma Genome Atlas (CGGA) validation sets. We explored the differences in immune infiltration and immune checkpoint genes between low and high risk groups and constructed a nomogram. Moreover, we compiled a third validation cohort including 43 glioma patients. The expression of necroptosis-related genes was verified in matched tissues using immunochemical staining in the third cohort, and we analyzed their relationship to clinicopathological features. Results: Three necroptosis-related differentially expressed genes ( Conclusions: Our necroptosis-related risk model can be used to predict the prognosis of glioma patients and improve prognostic accuracy, which may provide potential therapeutic targets and a theoretical basis for treatment.
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