A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma

Debo Yun; Xuya Wang; Wenbo Wang; Xiao Ren; Jiabo Li; Xisen Wang; Jianshen Liang; Jie Liu; Jikang Fan; Xiude Ren; Hao Zhang; Guanjie Shang; Jingzhang Sun; Lei Chen; Tao Li; Chen Zhang; Shengping Yu; Xuejun Yang

doi:10.3389/fonc.2022.897702

A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma

Front Oncol. 2022 Jun 10:12:897702. doi: 10.3389/fonc.2022.897702. eCollection 2022.

Authors

Debo Yun^{1

2

3}, Xuya Wang^{1

2}, Wenbo Wang^{1

2}, Xiao Ren^{1

2}, Jiabo Li^{1

2}, Xisen Wang^{1

2}, Jianshen Liang^{1

2}, Jie Liu^{1

2}, Jikang Fan^{1

2}, Xiude Ren^{1

2}, Hao Zhang^{1

2}, Guanjie Shang^{1

2}, Jingzhang Sun^{1

2}, Lei Chen^{1

2}, Tao Li^{1

2}, Chen Zhang², Shengping Yu^{1

2}, Xuejun Yang^{1

2

4}

Affiliations

¹ Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
² Laboratory of Neuro-oncology, Tianjin Neurological Institute, Tianjin, China.
³ Department of Neurosurgery, Nanchong Central Hospital, Nanchong, China.
⁴ Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, Beijing, China.

Abstract

Background: Ferroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes.

Methods: RNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets.

Results: Seven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways.

Conclusion: In summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.

Keywords: LASSO analysis; cancer essential genes; clinical outcomes; drug screening; ferroptosis; glioma; risk model.