Polyamine Metabolism Genes Predict Prognosis and Immunotherapy Response in Breast Cancer

Strategic Insights -

Researchers from the Department of Thyroid and Breast Surgery at Ansteel General Hospital in Anshan, People's Republic of China, have conducted a comprehensive study on polyamine metabolism-related genes (PMRGs) in breast cancer. Their findings suggest that PMRGs can accurately predict prognosis and immunotherapy response in breast cancer patients. The study identified 17 polyamine metabolism genes, including 12 survival-related genes, which were abundantly expressed in tumor cells. The researchers developed a prognostic model that combined risk scores and clinicopathological features, which successfully stratified breast cancer patients into high-risk and low-risk groups.

Key Takeaways:

  • The study identified 17 polyamine metabolism genes in breast cancer, including 12 survival-related genes that were abundantly expressed in tumor cells.
  • Two PMRG expression subtypes were identified in the METABRIC cohort, with cluster B being associated with a worse prognosis and more active cancer- and immune-related pathways.
  • Six genes were used to construct a prognostic model through univariate and multivariate Cox regression analyses, which was validated by ROC curve analysis and demonstrated good predictive performance.
  • A nomogram combining risk scores and clinicopathological features was constructed, which guided clinical treatment strategies and successfully stratified BC patients into high-risk and low-risk groups.
  • Four high-risk independent prognostic factors were validated as being upregulated in breast cancer tissues, including , , and .
  • Functional analysis revealed significant differences in immune status and drug sensitivity between high-risk and low-risk groups.

Statistics:

  • 17 polyamine metabolism genes were identified in the study.
  • 12 survival-related genes were selected from the 17 identified genes.
  • Two PMRG expression subtypes were identified in the METABRIC cohort.
  • ROC curve analysis showed an AUC3years of 0.684 in the training cohort (METABRIC) and 0.682 in the validation cohort (GSE86166).
  • Decision Curve Analysis demonstrated that the model could guide clinical treatment strategies.
  • The model successfully stratified BC patients into high-risk and low-risk groups, with the high-risk group exhibiting poorer clinical outcomes.
  • The four high-risk independent prognostic factors were upregulated in 70% of breast cancer tissues.

Sources:

  • Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer. Frontiers in Oncology, 2025;15:1613458.
  • NewsRx. Data on Breast Cancer Described by Researchers at Department of Thyroid and Breast Surgery (Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer). Immunotherapy Weekly. September 3, 2025; p 117.