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胶质瘤患者二硫化物相关基因特征的开发与验证

Development and validation of disulfidptosis-related genes signature for patients with glioma.

作者信息

Wang Jia, Luo Junchi, Yang Sha, Deng Yongbing, Chen Peng, Tan Ying, Liu Yang

机构信息

Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China.

Zunyi Medical University, Zunyi, Guizhou Province, China.

出版信息

Discov Oncol. 2024 Dec 18;15(1):758. doi: 10.1007/s12672-024-01664-z.

Abstract

BACKGROUND

Disulfidptosis has recently emerged as a novel form of regulated cell death (RCD). Evasion of cell death is a hallmark of cancer, and the resistance of many tumors to apoptosis-inducing therapies has heightened interest in exploring alternative RCD mechanisms.

METHODS

Transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA). Glioma samples were classified using non-negative matrix factorization (NMF). A predictive model was constructed using Lasso regression analysis, and its performance was evaluated through receiver operating characteristic (ROC) and Kaplan-Meier survival analyses. The relationship between the model and the tumor immune microenvironment (TIME) as well as treatment sensitivity was also assessed. Finally, we validated the expression of key signature genes in glioma.

RESULTS

Glioma samples were categorized into two distinct subtypes based on disulfidptosis-related genes, showing significant differences in overall survival (OS) and progression-free survival (PFS) between the subtypes. A genetic risk score model was then developed using these genes. A nomogram predicting OS was constructed using the risk score and clinical variables. Patients were stratified into low- and high-risk groups based on the median risk score from the TCGA cohort. Low-risk patients had significantly better outcomes compared to high-risk patients (TCGA cohort, OS: p < 0.001; PFS: p < 0.001; CGGA cohort, OS: p < 0.001). The risk score was associated with HLA expression, immune checkpoint genes, immune cell infiltration, immune function, tumor mutation burden, tumor stemness score, and drug sensitivity. Lastly, the expression of 11 signature genes was confirmed in glioma tissues.

CONCLUSIONS

The disulfidptosis-related gene-based risk score model effectively predicted glioma outcomes and highlighted the role of disulfidptosis-related genes in tumor immunity. This study offers potential new avenues for glioma treatment by targeting disulfidptosis.

摘要

背景

二硫化物诱导的细胞焦亡最近作为一种新型的程序性细胞死亡(RCD)形式出现。逃避细胞死亡是癌症的一个标志,许多肿瘤对诱导凋亡疗法的抗性增加了人们对探索替代RCD机制的兴趣。

方法

从癌症基因组图谱(TCGA)、基因型-组织表达(GTEx)和中国胶质瘤基因组图谱(CGGA)获得转录组和临床数据。使用非负矩阵分解(NMF)对胶质瘤样本进行分类。使用套索回归分析构建预测模型,并通过受试者工作特征(ROC)和Kaplan-Meier生存分析评估其性能。还评估了该模型与肿瘤免疫微环境(TIME)以及治疗敏感性之间的关系。最后,我们验证了胶质瘤中关键特征基因的表达。

结果

基于二硫化物诱导的细胞焦亡相关基因,胶质瘤样本被分为两种不同的亚型,各亚型之间的总生存期(OS)和无进展生存期(PFS)存在显著差异。然后使用这些基因开发了一个遗传风险评分模型。使用风险评分和临床变量构建了一个预测OS的列线图。根据TCGA队列的中位风险评分将患者分为低风险和高风险组。与高风险患者相比,低风险患者的预后明显更好(TCGA队列,OS:p < 0.001;PFS:p < 0.001;CGGA队列,OS:p < 0.001)。风险评分与HLA表达、免疫检查点基因、免疫细胞浸润、免疫功能、肿瘤突变负担、肿瘤干性评分和药物敏感性相关。最后,在胶质瘤组织中证实了11个特征基因的表达。

结论

基于二硫化物诱导的细胞焦亡相关基因的风险评分模型有效地预测了胶质瘤的预后,并突出了二硫化物诱导的细胞焦亡相关基因在肿瘤免疫中的作用。本研究通过靶向二硫化物诱导的细胞焦亡为胶质瘤治疗提供了潜在的新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cec/11655816/b73de3fe245f/12672_2024_1664_Fig1_HTML.jpg

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