Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma - Zhu, Yunyang; Feng, Songwei; Song, Zhaoming; Wang, Zhong; Chen, Gang (Frontiers in Genetics, 2022)
Few breakthrough had achieved on the treatment of lower-grade glioma (LGG) in the recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (cluster A, B, C and D) by bioinformatics methods in in TCGA-LGG and two CGGA datasets. Cluster A was an “immune cold” phenotype with the lowest immune infiltration and longest survival expectation while Cluster D was an “immune rich” subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with of immune infiltration degrees among clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine and immune check-point PD1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine-cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules CAMS and ECM receptor interaction were also enriched in cluster D. Hub genes were selected by MCC algorithm in the blue module of gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG.