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. Due to the heterogeneity of the disease, prognostic assessment still faces challenges. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Objective Construct immune-related gene pairs to evaluate the prognosis of breast cancer and patient survival rate. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, the Gene expression profiles and clinical data of breast cancer samples were downloaded. TCGA cohort were further divided into a training set (n = 764) and internal validation sets (n = 325). The GEO cohort was analyzed as an external validation cohort (n = 327). In the training set, differently expressed immune-relevant genes (IRGs) were screened firstly, and they were used to construct immune-relevant gene pairs (IRGPs). Then, the prognostic IRGPs were identified via univariate Cox regression analysis. Finally, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature. Kaplan-Meier (KM) survival curves, receiver operating characteristic (ROC) curve analysis, univariate and multivariate Cox regression analysis were used to estimate the predictive value of the IRGP prognostic signature. And the IRGP prognostic signature was validated in the internal validation cohort and external validation cohort. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results A total of 474 differently expressed IRGs and 2942 prognostic IRGPs were identified. Finally, we generated a IRGP prognostic signature consisting of 33 IRGPs. Subsequently, the 33 IRGPs grouped BRCA patients into high- and low-risk groups. Kaplan-Meier curves shown a significantly different overall survival in risk groups. Time-dependent ROC curves indicated that the IRGP prognostic signature possessed a high specificity and sensitivity in all the sets. Univariate and multivariate Cox regression analysis showed a statistical significance for the prognostic value of IRGP prognostic signature and the IRGP prognostic signature was a strong independent risk factor. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Immune cell infiltration analysis indicated a significant difference in percentage of M2 macrophages between high- and low-risk groups. Conclusion The 33-IRGPs 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.