Remarkable immune and clinical value of novel ferroptosis-related genes in glioma - Xiaoyan Gao; Jiazheng Zhao; Litao Jia; Qiushi Zhang (Scientific Reports, 2022)
Abstract Ferroptosis is a neoteric model of regulated cell death that shows great potential for the understanding of tumor immunology and as a target for therapy. The present study aimed to identify ferroptosis-related differentially expressed genes (DEGs) in glioma and to explore their value through systematic analysis. Ferroptosis-related DEGs were identified through the Gene Expression Omnibus database in combination with the FerrDb database and analyzed in the Genotype-Tissue Expression database and The Cancer Genome Atlas database. Possible signaling pathways involved were explored by construction of enrichment analysis and protein–protein interaction of these DEGs. Potential regulation of the immune microenvironment, immune checkpoint and chemokine was postulated by immune analysis. A prognosis model for glioma was developed using survival analysis, exhibited by the nomogram and evaluated by the calibration curve. The prognostic value of the model was validated by using an independent cohort. A total of 15 ferroptosis-related DEGs were identified, including 7 down-regulated and 8 up-regulated, with ATP6V1G2, GABARAPL1 and GOT1 as hub genes. The expression of all 3 hub genes was positively correlated with T follicular helper cells and natural killer CD56bright cells. These hub genes were negatively correlated with the macrophage cell type as well as B7H3, PDCD1, LAG3 and CXCL16, CXCR4, CCR5. Low expression of all 3 hub genes was associated with poor prognosis in glioma cases. ATP6V1G2 might be an independent prognostic factor and, as such, a high-precision prognostic model of glioma was constructed. We identified novel ferroptosis-related genes with clinical value in glioma and revealed their possible tumor immune relevance. Furthermore, in glioma, we pinpointed underlying critical elements of the chemokine, immune microenvironment and immune checkpoint, and were able to develop a predictive model of prognosis.