Research Paper Volume 13, Issue 13 pp 17655—17672
Characterization of ferroptosis signature to evaluate the predict prognosis and immunotherapy in glioblastoma
- 1 Department of Neurosurgery, Zhuzhou Central Hospital, Zhuzhou 412000, Hunan Province, PR China
- 2 Department of Operating Theatre, Zhuzhou Central Hospital, Zhuzhou 412000, Hunan Province, PR China
- 3 Department of Rehabilitation Medicine, Zhuzhou Central Hospital, Zhuzhou 412000, Hunan Province, PR China
Received: March 29, 2021 Accepted: June 19, 2021 Published: July 9, 2021https://doi.org/10.18632/aging.203257
How to Cite
Copyright: © 2021 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Glioblastoma (GBM) is the most common type of brain cancer with poor survival outcomes and unsatisfactory response to current therapeutic strategies. Recent studies have demonstrated that ferroptosis-related genes (FRGs) are linked with the occurrence and development of GBM and may become promising biological indicators in GBM therapy.
Methods: We systematically assessed the relationship between FRGs expression profiles and prognosis in glioma patients based on the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets to establish a risk score model according to the gene signature of multiple survival-associated DEGs. Further, the differences between the tumor microenvironment score, immune cell infiltration, immune checkpoint expression levels, and drug sensitivity in the high- and low-risk group are analyzed through a variety of algorithms in R software.
Results: GBM patients were divided into two subgroups (high- and low-risk) according to the established risk score model. Patients in the high-risk group showed significantly reduced overall survival compared with those in the low-risk group. Also, we found that the high-risk group showed higher ImmuneScore and StromalScore, while different subgroups have significant differences in immune cell infiltration, immune checkpoint expression levels, and drug sensitivity. In summary, we developed and validated an FRGs risk model, which served as an independent prognostic indicator for GBM. Besides, the two subgroups divided by the model have significant differences, which provides novel insights for further studies as well as the personalized treatment of patients.