Identifying and validating necroptosis-associated features to predict clinical outcome and immunotherapy response in patients with glioblastoma

IF 4.4 3区医学 Q2 ENVIRONMENTAL SCIENCES

Environmental Toxicology Pub Date : 2024-08-20 DOI:10.1002/tox.24309

Qinghua Yuan, Weida Gao, Mian Guo, Bo Liu

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引用次数: 0

Abstract

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.

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识别和验证坏死相关特征，预测胶质母细胞瘤患者的临床结果和免疫疗法反应。

背景：坏死是一种程序性细胞死亡，与癌症的发病机制有关。这项研究基于坏死相关基因建立了胶质母细胞瘤（GBM）预后模型：方法：从TCGA数据库中收集RNA-Seq数据。方法：从 TCGA 数据库中收集 RNA-Seq 数据，使用 "WGCNA "方法识别共表达模块，并在此基础上进行 GO 和 KEGG 分析。编制了蛋白质-蛋白质相互作用（PPI）网络。通过 COX 回归和最小绝对缩小和选择算子（LASSO）分析，减少了关键预后基因的数量，从而建立了一个 RiskScore 模型。利用 CIBERSORT、ESTIMATE、MCP-count 和 TIMER 数据库评估了免疫微环境的差异。细胞实验验证了关键预后基因对 GBM 的潜在影响：结果：坏死评分较高组的 GBM 患者免疫评分较高，生存率较低。与坏死评分密切相关的布朗模块被认为是关键基因模块。通过对五个聚类进行回归分析，得到了三个关键基因（GZMB、PLAUR、SOCS3）。RiskScore 模型与坏死评分呈显著正相关。低危患者可从免疫疗法中获益，而高危患者可能更适合接受多种化疗药物。提名图在生存预测方面表现出色。GZMB、PLAUR和SOCS3在GBM的发展中起着关键作用。结论：GZMB、PLAUR和SOCS3在GBM的发展中起着关键作用，其中GZMB的高表达与GBM细胞的侵袭和迁移能力有关：结论：我们建立了一个与坏死相关的基因特征，并构建了一个 RiskScore 模型，为预测 GBM 患者的临床预后和免疫治疗反应提供了参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environmental Toxicology 环境科学-毒理学

CiteScore

7.10

自引率

8.90%

发文量

261

审稿时长

4.5 months

期刊介绍： The journal publishes in the areas of toxicity and toxicology of environmental pollutants in air, dust, sediment, soil and water, and natural toxins in the environment.Of particular interest are: Toxic or biologically disruptive impacts of anthropogenic chemicals such as pharmaceuticals, industrial organics, agricultural chemicals, and by-products such as chlorinated compounds from water disinfection and waste incineration; Natural toxins and their impacts; Biotransformation and metabolism of toxigenic compounds, food chains for toxin accumulation or biodegradation; Assays of toxicity, endocrine disruption, mutagenicity, carcinogenicity, ecosystem impact and health hazard; Environmental and public health risk assessment, environmental guidelines, environmental policy for toxicants.