A Ferroptosis-Related Gene Prognostic Index to Predict Temozolomide Sensitivity and Immune Checkpoint Inhibitor Response for Glioma

Yonghua Cai; Xianqiu Liang; Zhengming Zhan; Yu Zeng; Jie Lin; Anqi Xu; Shuaishuai Xue; Wei Xu; Peng Chai; Yangqi Mao; Zibin Song; Lei Han; Jianqi Xiao; Ye Song; Xian Zhang

doi:10.3389/fcell.2021.812422

A Ferroptosis-Related Gene Prognostic Index to Predict Temozolomide Sensitivity and Immune Checkpoint Inhibitor Response for Glioma

Front Cell Dev Biol. 2022 Jan 31:9:812422. doi: 10.3389/fcell.2021.812422. eCollection 2021.

Authors

Yonghua Cai¹, Xianqiu Liang¹, Zhengming Zhan¹, Yu Zeng¹, Jie Lin¹, Anqi Xu¹, Shuaishuai Xue¹, Wei Xu¹, Peng Chai¹, Yangqi Mao¹, Zibin Song¹, Lei Han¹, Jianqi Xiao², Ye Song^{1

3}, Xian Zhang¹

Affiliations

¹ Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
² Department of Neurosurgery, The First Hospital of Qiqihar City, Qiqihar, China.
³ Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou, China.

Abstract

Background: Gliomas are highly lethal brain tumors. Despite multimodality therapy with surgery, radiotherapy, chemotherapy, and immunotherapy, glioma prognosis remains poor. Ferroptosis is a crucial tumor suppressor mechanism that has been proven to be effective in anticancer therapy. However, the implications of ferroptosis on the clinical prognosis, chemotherapy, and immune checkpoint inhibitor (ICI) therapy for patients with glioma still need elucidation. Methods: Consensus clustering revealed two distinct ferroptosis-related subtypes based on the Cancer Genome Atlas (TCGA) glioma dataset (n = 663). Subsequently, the ferroptosis-related gene prognostic index (FRGPI) was constructed by weighted gene co-expression network analysis (WGCNA) and "stepAIC" algorithms and validated with the Chinese Glioma Genome Atlas (CGGA) dataset (n = 404). Subsequently, the correlation among clinical, molecular, and immune features and FRGPI was analyzed. Next, the temozolomide sensitivity and ICI response for glioma were predicted using the "pRRophetic" and "TIDE" algorithms, respectively. Finally, candidate small molecular drugs were defined using the connectivity map database based on FRGPI. Results: The FRGPI was established based on the HMOX1, TFRC, JUN, and SOCS1 genes. The distribution of FRGPI varied significantly among the different ferroptosis-related subtypes. Patients with high FRGPI had a worse overall prognosis than patients with low FRGPI, consistent with the results in the CGGA dataset. The final results showed that high FRGPI was characterized by more aggressive phenotypes, high PD-L1 expression, high tumor mutational burden score, and enhanced temozolomide sensitivity; low FRGPI was associated with less aggressive phenotypes, high microsatellite instability score, and stronger response to immune checkpoint blockade. In addition, the infiltration of memory resting CD4⁺ T cells, regulatory T cells, M1 macrophages, M2 macrophages, and neutrophils was positively correlated with FRGPI. In contrast, plasma B cells and naïve CD4⁺ T cells were negatively correlated. A total of 15 potential small molecule compounds (such as depactin, physostigmine, and phenacetin) were identified. Conclusion: FRGPI is a promising gene panel for predicting the prognosis, immune characteristics, temozolomide sensitivity, and ICI response in patients with glioma.

Keywords: ferroptosis; ferroptosis-based anticancer therapy; glioma; immune checkpoint inhibitor; immunotherapy; temozolomide; tumor microenvironment.