Glioma is a fatal brain tumor characterized by rapid proliferation and treatment resistance. Ferroptosis is a newly discovered programmed cell death and plays a crucial role in the occurrence and progression of tumors. In this study, we identified ferroptosis specific markers to reveal the relationship between ferroptosis-related genes and glioma by analyzing whole transcriptome data from Chinese Glioma Genome Atlas, The Cancer Genome Atlas dataset, GSE16011 dataset, and the Repository of Molecular Brain Neoplasia Data dataset. Nineteen ferroptosis-related genes with clinical and pathological features of glioma were identified as highly correlated. Functional assays in glioma cell lines indicated the association of ferroptosis with temozolomide resistance, autophagy, and glioma cell migration. Therefore, the identified ferroptosis-related genes were significantly correlated with glioma progression.

Transcriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower‐grade glioma (LGG) patients to identify immune subtypes. Based on the immune gene profiles of 402 LGG patients from The Cancer Genome Atlas, we performed consensus clustering to determine robust clusters of patients, and evaluated their reproducibility in three Chinese Glioma Genome Atlas cohorts. We further integrated immunogenomics methods to characterize the immune environment of each subtype. Our analysis identified and validated three immune subtypes—Im1, Im2, and Im3—characterized by differences in lymphocyte signatures, somatic DNA alterations, and clinical outcomes. Im1 had a higher infiltration of CD8+ T cells, Th17, and mast cells. Im2 was defined by elevated cytolytic activity, exhausted CD8+ T cells, macrophages, higher levels of aneuploidy, and tumor mutation burden, and these patients had worst outcome. Im3 displayed more prominent T helper cell and APC coinhibition signatures, with elevated pDCs and macrophages. Each subtype was associated with distinct somatic alterations. Moreover, we applied graph structure learning‐based dimensionality reduction to the immune landscape and revealed significant intracluster heterogeneity with Im2 subtype. Finally, we developed and validated an immune signature with better performance of prognosis prediction. Our results demonstrated the immunological heterogeneity within diffuse LGG and provided valuable stratification for the design of future immunotherapy.

Four-CpG combinations of pyrosequencing are sufficient for evaluating the methylation status of MGMT and predicting therapeutic prognosis in gliomas.