Identification and Validation of Immune-Related Gene Prognostic Signature for breast cancer

Abstract Background Although the outcome of breast cancer patients has been improved by advances in early detection, diagnosis and treatment, prognostic assessment still faces challenges due to the heterogeneity of the disease. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Methods We screened prognostic immune-relevant gene pairs (IRGPs) via univariate Cox regression analysis in the Cancer Genome Atlas (TCGA) cohort. Then, TCGA cohort were further divided into a training set (n = 755) and internal validation sets (n = 320). Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature in training set. And the IRGP prognostic signature was validated in the internal TCGA validation cohort and external validation cohorts GSE20685 (n = 327), GSE42568 (n = 104), and GSE20711 (n = 88) from Gene Expression Omnibus (GEO) database. We estimated the immune cell infiltration in tumor microenvironment via CIBERSORT and ESTIMATE. We used the TIDE algorithm and stemness indices to evaluate the potential of IRGP signature as an indicator of response to immunotherapy. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results We generated a IRGP prognostic signature consisting of 16 IRGPs. Subsequently, the 16 IRGPs grouped breast cancer patients into high- and low-risk groups. Survival analysis indicated that the IRGP signature possessed an independent prognostic value. The low-IRGP group exhibited a higher level of immune cell infiltration, higher expression of immune checkpoint molecules, lower tumor stemness indices, and was much more sensitive to immunotherapy. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Conclusion The 16-IRGP prognostic signature was developed to provide new insights for the identification of high-risk breast cancer and the evaluation of the possibility of immunotherapy in personalized breast cancer treatment.