Glutamine metabolism-related genes predict prognosis and reshape tumor microenvironment immune characteristics in diffuse gliomas

Huanhuan Fan; Shuxin Zhang; Yunbo Yuan; Siliang Chen; Wenhao Li; Zhihao Wang; Yufan Xiang; Junhong Li; Xiaohong Ma; Yanhui Liu

doi:10.3389/fneur.2023.1104738

Glutamine metabolism-related genes predict prognosis and reshape tumor microenvironment immune characteristics in diffuse gliomas

Front Neurol. 2023 Mar 10:14:1104738. doi: 10.3389/fneur.2023.1104738. eCollection 2023.

Authors

Huanhuan Fan^{1

2}, Shuxin Zhang³, Yunbo Yuan³, Siliang Chen³, Wenhao Li³, Zhihao Wang³, Yufan Xiang³, Junhong Li⁴, Xiaohong Ma^{1

2}, Yanhui Liu³

Affiliations

¹ Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
² West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
³ Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
⁴ Department of Neurosurgery, Chengdu Second People's Hospital, Chengdu, Sichuan, China.

Abstract

Background: Diffuse gliomas possess a kind of malignant brain tumor with high mortality. Glutamine represents the most abundant and versatile amino acid in the body. Glutamine not only plays an important role in cell metabolism but also involves in cell survival and malignancies progression. Recent studies indicate that glutamine could also affect the metabolism of immune cells in the tumor microenvironment (TME).

Materials and methods: The transcriptome data and clinicopathological information of patients with glioma were acquired from TCGA, CGGA, and West China Hospital (WCH). The glutamine metabolism-related genes (GMRGs) were retrieved from the Molecular Signature Database. Consensus clustering analysis was used to discover expression patterns of GMRGs, and glutamine metabolism risk scores (GMRSs) were established to model tumor aggressiveness-related GMRG expression signature. ESTIMATE and CIBERSORTx were applied to depict the TME immune landscape. The tumor immunological phenotype analysis and TIDE were utilized for predicting the therapeutic response of immunotherapy.

Results: A total of 106 GMRGs were retrieved. Two distinct clusters were established by consensus clustering analysis, which showed a close association with the IDH mutational status of gliomas. In both IDH-mutant and IDH-wildtype gliomas, cluster 2 had significantly shorter overall survival compared with cluster 1, and the differentially expressed genes between the two clusters enriched in pathways related to malignant transformation as well as immunity. In silico TME analysis of the two IDH subtypes revealed not only significantly different immune cell infiltrations and immune phenotypes between the GMRG expression clusters but also different predicted responses to immunotherapy. After the screening, a total of 10 GMRGs were selected to build the GMRS. Survival analysis demonstrated the independent prognostic role of GMRS. Prognostic nomograms were established to predict 1-, 2-, and 3-year survival rates in the four cohorts.

Conclusion: Different subtypes of glutamine metabolism could affect the aggressiveness and TME immune features of diffuse glioma, despite their IDH mutational status. The expression signature of GMRGs could not only predict the outcome of patients with glioma but also be combined into an accurate prognostic nomogram.

Keywords: diffuse glioma; glutamine metabolism; immune; prognosis; tumor microenvironment.

Grants and funding

This study is supported by Clinical Research Innovation Project, West China Hospital, Sichuan University (19HXCX009 to YL), Post-Doctor Research Project, West China Hospital, Sichuan University (20HXBH035 to SZ).