Polyamine metabolism and immune related genes as prognostic features in breast cancer: a novel risk model approach

Background: The biological role of polyamine metabolism-related genes (PMRGs) in breast cancer (BRCA) through immune mediation is not well understood. Consequently, this study aimed to explore the prognostic features connected to PMRGs and immune-related genes (IRGs) in BRCA via bioinformatics analysis. Methods: We analyzed The Cancer Genome Atlas (TCGA)-BRCA and GSE20685 datasets. Differential expression analysis revealed differentially expressed genes (DEGs) in TCGA-BRCA, which intersected with 1,793 IRGs and 59 PMRGs to identify candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen prognostic genes, which were then used to develop a risk model. This model was validated in both datasets. A nomogram was constructed using independent prognostic factors from univariate and multivariate Cox regression analyses to predict BRCA patient survival. The immune microenvironment landscape and gene set enrichment analysis (GSEA) results were also characterized. Results: Among 9,558 DEGs, 1,793 IRGs, and 59 PMRGs, 10 candidate genes were identified, with PSME2, PSMB8, and PSMD14 selected as prognostic genes. The risk model stratified BRCA patients into high- and low-risk groups, with high-risk patients showing worse survival according to Kaplan-Meier analysis. The nomogram, which is based on the pathological stage and risk score, accurately predicts patient viability. High-risk patients have poor immune responses. GSEA revealed immune-related pathway involvement. PSME2 and PSMB8 were upregulated in the control samples, whereas PSMD14 was increased in the BRCA samples. The CCK8 assay results indicated that PSMD14 significantly promotes the proliferation of BRCA cells. Conclusions: PMRGs and IRGs, specifically PSME2, PSMB8, and PSMD14, are potential prognostic markers in BRCA. A risk model and nomogram based on these genes were developed to assess BRCA prognosis effectively. These tools can improve the prognostic assessment of BRCA patients.