Prediction of prognosis and immunotherapy response in breast cancer based on neutrophil extracellular traps-related classification.

Abstract

Neutrophil extracellular traps (NETs), a network of DNA histone complexes and proteins released by activated neutrophils, have been demonstrated to be associated with inflammation, infection related immune response and tumorigenesis in previous reports. However, the relationship between NETs related genes and breast cancer remains controversial. In the study, we retrieved transcriptome data and clinical information of BRCA patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. The expression matrix of neutrophil extracellular traps (NETs) related genes was generated and consensus clustering was performed by Partitioning Around Medoid (PAM) to classify BRCA patients into two subgroups (NETs high group and NETs low group). Subsequently, we focus on the differentially expressed genes (DEGs) between the two NETs-related subgroups and further explored NETs enrichment related signaling pathways by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, we constructed a risk signature model by LASSO Cox regression analysis to evaluate the association between riskscore and prognosis. Even more, we explored the landscape of the tumor immune microenvironment and the expression of immune checkpoints related genes as well as HLA genes between two NETs subtypes in breast cancer patients. Moreover, we found and validated the correlation of different immune cells with risk score, as well as the response to immunotherapy in different subgroups of patients was detected by Tumor Immune Dysfunction and Exclusion (TIDE) database. Ultimately, a nomogram prognostic prediction model was established to speculate on the prognosis of breast cancer patients. The results suggest that high riskscore is associated with poor immunotherapy response and adverse clinical outcomes in breast cancer patients. In conclusion, we established a NETs-related stratification system that is beneficial for guiding the clinical treatment and predicting prognosis of BRCA.