A ferroptosis-based gene signature for relapse prediction in prostate adenocarcinoma

Aim: This study focused on developing a prognostic index model associated with ferroptosis for predicting prostate cancer (PCa) relapse and progression. The aim was to enhance clinical decision making and improve immunotherapy strategies for PCa patients, ultimately leading to better patient outcomes. Methods: The study employed the least absolute shrinkage and selection operator to develop the Ferroptosis-related gene (FRG) prognostic index model. This model's predictive power was validated across multiple PCa datasets, and its correlation with clinicopathological factors was investigated. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses were conducted to identify associated signaling pathways. Furthermore, the CIBERSORT algorithm was used to assess PCa patient outcomes based on the combination of the FRGs risk index and immune cell infiltration patterns. Results: The FRG index model emerged as an independent predictor of PCa recurrence. It correlated with advanced pathological stages, higher prostate-specific antigen levels, and higher tumor grades. Notably, the FRG index was significantly associated with immune cell infiltration, particularly activated mast cells, which are crucial in PCa recurrence and progression. Furthermore, the response of the FRG index in PCa cell lines implies that doxorubicin may hold clinical efficacy for recurrent PCa. Conclusion: The FRG index established here could serve as a valuable prognostic tool and clinical decision-making aid in PCa. It offers insights into the molecular mechanisms underlying PCa progression and suggests new avenues for immunotherapeutic strategies, potentially leading to improved patient outcomes and a better understanding of PCa biology.