Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Abstract

Abstract Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Keywords

• Glutamine metabolism
• Breast cancer
• Subgroups
• Immune
• Gene mutation