Identification of Triple-Negative Breast Cancer Subtypes Based on Ferroptosis- Related Gene Signature for Targeted Treatment Prioritization

Abstract

Abstract Ferroptosis, an iron-dependent form of programmed cell death, plays a crucial role in cancer therapies. However, its impact on chemotherapy, immune checkpoint inhibitor (ICI) treatments, and molecular subtypes of triple-negative breast cancer (TNBC) is poorly understood. In this study, we utilized the FUSCC TNBC cohort to classify TNBC patients into distinct subtypes based on the expression of eight ferroptosis-related genes (FRGs). We employed Gene Ontology (GO) and Gene Set Variation Analysis (GSVA) to characterize the immune phenotype and enriched pathways associated with these subtypes. Additionally, we developed the FerrScore model to identify potential compounds and predict the benefits of ICIs in TNBC patients. Our analysis revealed two ferroptosis-related subtypes with contrasting overall survival (OS) outcomes. Cluster 1 had superior OS and exhibited a "hot" tumor phenotype with increased immune cell infiltration and elevated expression of immune checkpoints compared to Cluster 2. We identified Everolimus as the most promising candidate drug for TNBC patients with a high FerrScore, considering CMap score, experimental evidence, and clinical trial status. Moreover, we validated FerrScore as a powerful metric for predicting the benefits of various ICIs. These findings highlight the influence of ferroptosis on the tumor microenvironment, enabling the classification of TNBC patients into subgroups with different OS outcomes. The FerrScore model has potential in screening compounds and predicting the benefits of ICIs in TNBC, offering valuable insights for treatment prioritization in clinical settings.