PDP1 related ferroptosis risk signature indicates distinct immune microenvironment and prognosis of breast cancer patients.

OBJECTIVE: We aim to construct a RiskScore model to aid in the early prognosis of breast cancer (BC). METHODS: BC mRNA expression profiles were obtained from TCGA and GEO databases. Differential gene expression analysis identifies PDP1-ferroptosis-related genes. LASSO Cox regression was utilized to screen genes to build a RiskScore model, and survival analysis were performed to investigate the reliability in BC prognosis. Immune cell infiltration proportions were calculated using CIBERSORT and xCell algorithms. Single-cell data processing and analysis were conducted using "Seurat", "monocle", and "iTALK" packages. PDP1 was silenced to validate its influence on the target genes. RESULTS: Data from public databases revealed significant upregulation of PDP1 in BC samples compared to normal tissues. A RiskScore model based on PDP1-related differential ferroptosis-related genes (FRGs) ACSL1, BNIP3, and EMC2 was developed, which effectively predicted BC patient prognosis. High-risk BC samples exhibited poorer overall survival and were associated with immune microenvironment. The model remained significant in multivariate Cox regression analysis, indicating that it could independently predict the survival of BC patients. ACSL1, BNIP3, and EMC2 were downregulated after knockdown of PDP1. CONCLUSION: RiskScore model constructed by PDP1-ferroptosis-related genes ACSL1, BNIP3, and EMC2 is able to help predict the prognosis of BC patients.