BACKGROUND Necroptosis is a type of programmed cell death involved in the pathogenesis of cancers. This work developed a prognostic glioblastoma (GBM) model based on necroptosis-related genes. METHODS RNA-Seq data were collected from the TCGA database. The "WGCNA" method was used to identify co-expression modules, based on which GO and KEGG analyses were conducted. A protein-protein interaction (PPI) network was compiled. The number of key prognostic genes was reduced applying COX regression and least absolute shrinkage and selection operator (LASSO) analysis to build a RiskScore model. Differences in immune microenvironments were assessed using CIBERSORT, ESTIMATE, MCP-count, and TIMER databases. The potential impact of key prognostic genes on GBM was validated by cellular experiments. RESULTS GBM patients in the higher necroptosis score group had higher immune scores and worse survival. The Brown module, which was closely related to the necroptosis score, was considered as a key gene module. Three key genes (GZMB, PLAUR, SOCS3) were obtained by performing regression analysis on the five clusters. The RiskScore model was significantly, positively, correlated with necroptosis score. Low-risk patients could benefit from immunotherapy, while high-risk patients may be more suitable to take multiple chemotherapy drugs. The nomogram showed strong performance in survival prediction. GZMB, PLAUR, and SOCS3 played key roles in GBM development. Among them, high-expressed GZMB was related to the invasive and migratory abilities of GBM cells. CONCLUSIONS A genetic signature associated with necroptosis was developed, and we constructed a RiskScore model to provide reference for predicting clinical outcomes and immunotherapy responses of patients with GBM.

中文翻译：

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识别和验证坏死性凋亡相关特征，以预测胶质母细胞瘤患者的临床结果和免疫治疗反应。


背景 坏死性凋亡是一种参与癌症发病机制的程序性细胞死亡。这项工作开发了一种基于坏死性凋亡相关基因的预后胶质母细胞瘤 （GBM） 模型。方法 从 TCGA 数据库中收集 RNA-Seq 数据。采用 “WGCNA” 方法鉴定共表达模块，并基于该模块进行 GO 和 KEGG 分析。编译了蛋白质-蛋白质相互作用 （PPI） 网络。应用 COX 回归和最小绝对收缩和选择运算符 （LASSO） 分析来构建 RiskScore 模型，减少了关键预后基因的数量。使用 CIBERSORT、ESTIMATE、MCP-count 和 TIMER 数据库评估免疫微环境的差异。关键预后基因对 GBM 的潜在影响通过细胞实验得到验证。结果 坏死性凋亡评分较高的 GBM 患者免疫评分较高，生存率较差。与坏死性凋亡评分密切相关的 Brown 模块被认为是关键基因模块。通过对 5 个簇进行回归分析，获得 3 个关键基因 （GZMB 、 PLAUR 、 SOCS3 ）。RiskScore 模型与坏死性凋亡评分呈显著正相关。低风险患者可能受益于免疫疗法，而高危患者可能更适合服用多种化疗药物。列线图在生存预测方面表现出强劲的表现。GZMB、PLAUR 和 SOCS3 在 GBM 开发中发挥了关键作用。其中，高表达 GZMB 与 GBM 细胞的侵袭和迁移能力有关。结论 建立了与坏死性凋亡相关的遗传特征，构建了 RiskScore 模型，为预测 GBM 患者的临床结局和免疫治疗反应提供参考。