Robust Prediction of Prognosis and Immunotherapy Response for Bladder Cancer through Machine Learning Algorithm

Area of Science:

• Oncology
• Computational Biology
• Cancer Genomics

Background:

• Ferroptosis, a regulated cell death, and machine learning applications in bladder cancer (BCa) remain underexplored.
• Understanding the interplay between ferroptosis and BCa is crucial for developing novel therapeutic strategies.

Purpose of the Study:

• To investigate the role of ferroptosis-related genes (FRGs) in bladder cancer prognosis using machine learning.
• To explore the association between ferroptosis and tumor immunity in BCa.
• To evaluate the impact of ferroptosis on treatment response to immunotherapy.

Main Methods:

• Comprehensive analysis of 19 FRGs in 1322 BCa patients across four cohorts and a pan-cancer cohort (9824 patients).
• Machine learning algorithms were employed to select 12 key FRGs for constructing a prognostic model.
• Functional enrichment analysis and verification in immunotherapy cohorts (IMvigor210) were performed.

Main Results:

• A prognostic model based on 12 FRGs demonstrated significant survival differences between high and low ferroptosis score groups across multiple cohorts.
• Ferroptosis scores were significantly associated with tumor immunity pathways, including IL-17 and JAK-STAT signaling.
• High ferroptosis scores predicted worse survival outcomes in BCa patients receiving tumor immunotherapy and correlated with differential responses to immune checkpoint inhibitors.

Conclusions:

• Machine learning-derived ferroptosis signatures are valuable prognostic biomarkers for bladder cancer.
• Ferroptosis plays a significant role in modulating the tumor immune microenvironment in BCa.
• Ferroptosis status may predict immunotherapy response in bladder cancer patients.