A DEIRGs-based immune score model for predicting prognosis and immunotherapy effects in breast cancer.

Abstract

e13558 Background: Immune-based strategies have reignited prospect for the treatment of breast cancer(BRCA).Nevertheless, due to the molecular and genetic heterogeneity of tumors, a single biomarker can no longer meet the clinical needs in the highly complex tumor microenvironment(TMB).The main purpose of this study was to explore the immunobiological markers of BRCA prognosis in help to guide patient management and establish personalized risk assessment. Methods: Univariate/multivariable Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) Cox analysis was used to establish a DEIRGs-based model in BRCA.GSEA analysis was used to explore biological signaling pathways. The immune infiltration landscape of BRCA was conducted via CIBERSORT algorithms and immunotherapy response was calculated through TIDE algorithm and TMB analysis. Results: In this study, 1178 BRCA samples from The Cancer Genome Atlas (TCGA) were used as the training set, and 140 samples from Gene Expression Omnibus (GEO) database were used as the validation set. Immune-related genes were collected from ImmPort and Innatedb database. A total of 547 differentially expressed immune related genes(DEIRGs) were identified. Enrichment analysis of the DEIRGs revealed that they were involved in the regulation of the P13K-Akt signaling pathway and cytokine–cytokine receptor interaction. To identify the genes most relevant to BRCA, we performed a WGCNA analysis to select the modules with the strongest correlation between the module traits. A signature based on six DEIRGs(CETP, DES, IL33, TXNIP, RNF125, STAT5B) was constructed. These cases were stratified into high- and low-risk groups according to the median risk score. Kaplan–Meier analysis showed that OS in the high-risk group was significantly shorter than that in the low-risk group (P<0.001).The model presented unique characteristics in terms of immune cell infiltration and immune function in TME. The results from the tumor mutation burden(TMB) and tumor immune dysfunction and exclusion(TIDE) analysis revealed that the model could efficiently predict the potential response of immunotherapy. ROC curves were primarily used to assess the predictive power of the prognostic model, with AUC values of 0.736, 0.816 and 0.836 at 10, 15 and 20 years, respectively. More importantly, we researched the relationship between this model and TMB and TIDE score, which confirmed that this signature gave the best prognostic value. Conclusions: We have developed a novel immune score model, which can effectively and efficiently predict the prognosis of BRCA patients as well as the effect of immunotherapy. The high-risk subtype was featured by higher TIDE score and lower TMB. Our findings suggest that immunotherapy may be efficacious for low-risk groups of BRCA patients. The findings can be served as reference for the further research and validation of biomarkers.