Pediatric
gliomas (PGs) are the most common
brain tumors in children and the leading cause of childhood
cancer-related death. The understanding of the immune microenvironment is essential for developing effective antitumor
immunotherapies. Transcriptomic data from 495 PGs were analyzed in this study, with 384 as a training cohort and 111 as a validation cohort. Macrophages were the most common immune infiltrates in the PG microenvironment, followed by T cells. PGs were classified into 3 immune subtypes (ISs) based on immunological profiling: "immune hot" (IS-I), "immune altered" (IS-II), and "immune cold" (IS-III). IS-I
tumors, characterized by substantial immune infiltration and high
immune checkpoint molecule (ICM) expression, had a favorable prognosis and were more likely to respond to anti-PD1 and anti-CTLA4
immunotherapies, whereas IS-III
tumors, characterized by weak immune infiltration and low ICM expression, had a dismal prognosis and poor
immunotherapy responsiveness. IS-II
tumors represented a transitional stage. Immune classification was also correlated with somatic mutations, copy number alterations, and molecular pathways related to
tumorigenesis, metabolism, and immune responses. Three predictive classifiers using eight representative genes were generated by machine learning methods for immune classification. This study established a reliable immunological profile-based classification system for PGs, providing implications for further
immunotherapy strategies.