Construction and validation of a model based on necroptosis-related genes for predicting glioma prognosis

Gliomas are highly aggressive brain tumors that pose significant challenges in terms of prognosis and treatment. The precise expression patterns and prognostic significance of necroptosis-related genes (NRGs) in gliomas remain poorly understood, despite necroptosis demonstrating promise as a therapeutic target in cancer.

We downloaded and preprocessed glioma datasets from TCGA (training cohort) and GEO (GSE43378, serving as an independent external validation cohort). An additional independent validation was performed using the Chinese Glioma Genome Atlas (CGGA) cohort. The KEGG, MSigDB, and GeneCards databases were utilized to develop an extensive list of NRGs. The necroptosis score (N-Score) and immune infiltration were assessed using single-sample GSEA. To identify prognostic genes, we performed WGCNA to identify necroptosis-related modules, followed by univariate Cox regression and LASSO-penalized multivariate Cox regression for gene selection.

Glioma cases were assigned to high- and low-risk groups using the N-Score, and a marked difference in survival was observed (p < 0.001). We identified 11 prognostic genes (CASP6, DDIT3, FASN, FERMT1, GLUD1, GNLY, MYD88, NINJ1, PLA2G4A, PPIA, and TNFRSF1A) that were crucial for constructing a robust risk model that accurately predicts survival at one, two, and three years. Important hub genes, such as TNFRSF1A, were identified by the protein-protein interaction and mRNA-miRNA regulatory networks. These hub genes also showed strong correlations with different types of immune cells, such as myeloid-derived suppressor cells and central memory CD4 + T cells, suggesting that necroptosis may affect the tumor immune microenvironment.

NRGs are crucial in glioma prognosis and immune modulation. The risk model and identified hub gene TNFRSF1A provide valuable insights for further research and potential therapeutic targeting.