Clinical implications and immunological features of iron metabolism-related gene prognostic signature in breast cancer

Abstract

Abstract Backgroud: Increasing evidences suggest that the dysregulation of iron metabolism is linked to the onset and progression of breast cancer. However, prognostic value and therapeutic importance of iron metabolism-related genes in breast cancer remain unclear. Methods RNA sequencing information, clinicopathological data, and iron metabolism-related gene sets were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database and the Molecular Signatures Database. The risk score model was constructed and validated using GSEA, univariate, multivariate Cox, and LASSO regression analysis. The tumor microenvironment landscape of risk model was then evaluated. Finally, we predicted the immunotherapy response and drug prediction of iron metabolism-related signature. Results A total of 7 iron metabolism-related genes were identified, and a novel risk signature was developed in the training cohort for prognosis and risk stratification. The prognostic value of this model was also verified in the testing cohort. Moreover, a nomogram model was constructed and shown high predictive accuracy for 1-, 3-, and 5-year OS rate estimates. In addition, the high risk group had significantly higher immune, stromal and estimate scores, increased immunosuppressive cell infiltrations, elevated marker genes of cancer associated fibroblasts, lower tumor mutation burden, and worse response to anti-PD-L1 immunotherapy. Finally, the associations between drug sensitivity and risk model were analyzed, which might explore targeted drugs to improve the clinical outcomes for breast cancer patients. Conclusions The iron metabolism-related gene prognostic signature was developed and validated, which might provide a method for predicting the prognosis and survival of breast patients, as well as potential targets and drugs for immunotherapy.