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新型线粒体相关基因特征预测胶质瘤的预后和免疫状态。

Novel mitochondrial-related gene signature predicts prognosis and immunological status in glioma.

作者信息

Liu Yongsheng, Cai Lize, Wang Hao, Yao Lin, Zhang Kai, Chen Guangliang, Zhou Youxin

机构信息

Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, China.

出版信息

Transl Cancer Res. 2024 Jul 31;13(7):3338-3353. doi: 10.21037/tcr-23-2072. Epub 2024 Jul 26.

Abstract

BACKGROUND

Mitochondria are the center of cellular metabolism. The relationship between mitochondria and diseases has also been studied for a long time. However, the prognostic role of mitochondrial-related genes (MRGs) in patients with glioma and their biological effects are still unclear. The aim of the study was to construct a mitochondria-related model to assess prognosis and potential biological effects like immune infiltration, gene pathway and mutation, and give some predictive chemotherapeutic agents.

METHODS

The data of 675 patients from The Cancer Genome Atlas (TCGA) database were used to identify MRG signature and construct a prognostic model. After validating its robustness in Chinese Glioma Genome Atlas (CGGA), two risk groups derived from the prognostic model were then conducted with Gene Set Enrichment Analysis (GSEA), immune status, mutation status and chemotherapeutic agents prediction.

RESULTS

The prognostic model built from six gene signatures can successfully predict the prognosis and reflect clinicopathological characteristics. Patients in high-risk group displayed significantly worse overall survival (OS), immunosuppression effects, and mutation markers with worse prognosis. Twelve chemotherapeutic agents with strongly correlated sensitivity and risk scores were selected as potential agents.

CONCLUSIONS

The novel MRG signatures (, , , , , ) can predict prognosis and immunological status in glioma.

摘要

背景

线粒体是细胞代谢的中心。线粒体与疾病之间的关系也已被研究了很长时间。然而,线粒体相关基因(MRGs)在胶质瘤患者中的预后作用及其生物学效应仍不清楚。本研究的目的是构建一个线粒体相关模型,以评估预后和潜在的生物学效应,如免疫浸润、基因通路和突变,并给出一些预测性化疗药物。

方法

使用来自癌症基因组图谱(TCGA)数据库的675例患者的数据来识别MRG特征并构建预后模型。在中国胶质瘤基因组图谱(CGGA)中验证其稳健性后,然后对来自预后模型的两个风险组进行基因集富集分析(GSEA)、免疫状态、突变状态和化疗药物预测。

结果

由六个基因特征构建的预后模型可以成功预测预后并反映临床病理特征。高危组患者的总生存期(OS)明显更差,免疫抑制效应和预后更差的突变标志物。选择了十二种与敏感性和风险评分高度相关的化疗药物作为潜在药物。

结论

新的MRG特征(, , , , , )可以预测胶质瘤的预后和免疫状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f64/11319993/3c610fcfa571/tcr-13-07-3338-f1.jpg

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