Leveraging Artificial Intelligence for Biomarker Discovery in Prostate Cancer

Prostate cancer ranks among the most prevalent cancers globally and continues to be a major cause of cancer-related mortality in men. Despite advancements in diagnostics, prevention, and therapeutic interventions, in advanced stages of the disease, patient prognosis remains a significant challenge. Recent progress has introduced new strategies for improving early detection and personalized treatment, but there is still a critical need for reliable biomarkers to aid in diagnosis, predict disease course, and guide therapy selection with greater precision.

Artificial Intelligence (AI) and machine learning algorithms have the potential to revolutionize the field by identifying novel biomarkers that can aid in early diagnosis, prognosis, and treatment response prediction. Highlighting the application of AI in biomarker research, including computational models, data integration techniques, and validation studies needs to be more widely understood and researched so we can understand the role of AI in the discovery and validation of biomarkers for prostate cancer. AI algorithms can accelerate the identification of novel biomarkers by mining high-throughput genomic, proteomic, and imaging data to uncover subtle and clinically relevant patterns, predicting disease progression and therapeutic response with improving accuracy and enabling the rapid validation and prioritization of candidate biomarkers for clinical application.

Despite these promising capabilities, the application of AI in biomarker discovery—particularly for prostate cancer remains an evolving field. There is a pressing need for more interdisciplinary research focused on the development, validation, and clinical translation of AI-driven biomarker models. This involves the use of sophisticated computational techniques, robust data integration frameworks, and rigorous validation protocols.

Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.