Prognostic risk of immune-associated signature in the microenvironment of brain gliomas

Author:

Tao Yaling,Zhu Junqi,Yu Xiaoling,Cong Huaiwei,Li Jinpeng,Cai Ting,Chen Qian

Abstract

Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes.

Funder

National Natural Science Foundation of China

Medical Scientific Research Foundation of Zhejiang Province, China

Natural Science Foundation of Ningbo

Natural Science Foundation of Ningbo Municipality

Publisher

Frontiers Media SA

Subject

Genetics (clinical),Genetics,Molecular Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Base on the Cancer Genome Atlas prognostic for grade II and III glioma;Proceedings of the International Conference on Computer Vision and Deep Learning;2024-01-19

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