ArticleCancers2023
Identification, and Experimental and Bioinformatics Validation of an Immune-Related Prognosis Gene Signature for Low-Grade Glioma Based on mRNAsi.
Article in Cancers, 2023. 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, 3 citations in OpenAlex.
- Identification of immune-related gene signature for predicting prognosis of glioblastoma patients.Translational cancer research · 2026Article
- PEA15 promotes osteosarcoma progression and cisplatin resistance by modulating autophagy through the FABP3-TNF signaling axis.iScience · 2025Article
- The solute carrier family 11 transporters: a bridge between iron homeostasis and tumor biology.Cell communication and signaling : CCS · 2025Review
- Glutathione metabolism-related gene signature predicts prognosis and treatment response in low-grade glioma.Aging · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundLow-grade gliomas (LGGs), which are the second most common intracranial tumor, are diagnosed in seven out of one million people, tending to develop in younger people. Tumor stem cells and immune cells are important in the development of tumorigenesis. However, research on prognostic factors linked to the immune microenvironment and stem cells in LGG patients is limited. We critically need accurate related tools for assessing the risk of LGG patients.
methodsIn this study, we aimed to identify immune-related genes (IRGs) in LGG based on the mRNAsi score. We employed differentially expressed gene (DEG) methods and weighted correlation network analysis (WGCNA). The risk signature was then further established using a lasso Cox regression analysis and a multivariate Cox analysis. Next, we used immunohistochemical sections (HPA) and a survival analysis to identify the hub genes. A nomogram was built to assess the prognosis of patients based on their clinical information and risk scores and was validated using a DCA curve, among other methods.
resultsFour hub genes were obtained: C3AR1 (HR = 0.98,
conclusionThe outcome suggests that, when predicting the prognosis of LGG patients, our nomogram was more effective than the IPS. In this study, four immune-related predictive biomarkers for LGG were identified and proven to be IRGs. Therefore, the development of efficient immunotherapy techniques can be facilitated by the creation of the IPS.
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