An iron metabolism and immune related gene signature for the prediction of clinical outcome and molecular characteristics of triple-negative breast cancer

Abstract

Abstract Background An imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand. Methods We constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA). Results Compared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups. Conclusions The IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.