Construction of a prognostic model based on ferroptosis- and mitochondrial metabolism-related genes for patients with breast cancer.

Mitochondrial metabolism (MM)-mediated ferroptosis plays a critical role in breast cancer (BC). However, the potential targets based on ferroptosis and MM in BC remain poorly understood. This study aimed to explore the prognostic role of ferroptosis- and MM-related genes (FPMMs) in BC. Differentially expressed FPMMs were identified, and functional analyses were performed. Univariate Cox, LASSO, and multivariate Cox regression analyses were used to screen hub genes, and a prognostic risk model was then constructed and validated in external datasets. Gene set variation analysis was conducted to investigate their regulatory functions. Furthermore, immune infiltration analysis was performed using the "quantiseq" algorithm. We identified 206 differentially expressed FPMMs. A prognostic risk model consisting of 6 genes (BRD4, FLT3, SIAH2, CS, EMC2, and PI3KCA) was constructed, exhibiting good predictive capability and stability. These 6 prognostic genes were dysregulated in BC, with PI3KCA exhibiting the highest mutation frequency. Gene set variation analysis further revealed that the PI3K-AKT-mTOR signaling was suppressed in BC. In addition, the risk score based on the prognostic model was associated with immune infiltration, particularly with B cells, T cells, CD4, and dendritic cells. Our study highlights the potential of the prognostic model based on FPMMs as a valuable tool for BC prognosis prediction.