Multi-Omics Integration in Urological Cancers: Unlocking Precision Diagnosis and Therapy Through Big Data

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Urological cancers (prostate, bladder, renal) present significant global health challenges due to molecular heterogeneity.
• Single-omics approaches offer limited insights, hindering biomarker robustness and therapeutic precision.
• Multi-omics integration provides a systems-level view of cancer biology, crucial for understanding complex regulatory networks.

Purpose of the Study:

• To review recent literature on multi-omics integration in urological cancers.
• To synthesize findings on the application of multi-omics in understanding and managing prostate, bladder, and renal cancers.
• To identify challenges and future directions for clinical implementation.

Main Methods:

• Comprehensive narrative review of English-language, peer-reviewed studies published before September 2025.
• Searched PubMed, Scopus, and Web of Science databases.
• Synthesized findings from studies utilizing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and computational integration frameworks (e.g., machine learning, graph neural networks, stemness-based classifiers, spatial multi-omics).

Main Results:

• Multi-omics integration refines molecular subtypes and identifies prognostic/predictive biomarkers in prostate, bladder, and renal cancers.
• Examples include stemness classifiers in prostate cancer, consensus molecular subtypes in bladder cancer, and cell death signatures in renal cancer.
• Persistent challenges include data heterogeneity, small cohort sizes, lack of standardized pipelines, and translational gaps.

Conclusions:

• Multi-omics integration is transitioning from research to a core component of precision urology.
• Mechanistically grounded, interpretable models hold potential for improved individualized diagnosis, prognostication, and therapy selection.
• Clinical translation requires addressing limitations via standardization, collaborative efforts, and explainable artificial intelligence.