Background: Urological cancers, such as prostate, bladder and renal cell carcinoma, contribute substantially to the global cancer burden. Their management remains challenging due to extensive molecular and clinical heterogeneity. Conventional single-omics approaches (e.g., genomics and transcriptomics) have led to important discoveries but provided only partial views of tumour biology, which limit the robustness of biomarkers and therapeutic precision. Multi-omics integration offers a systems-level perspective that captures the complex regulatory networks underlying tumour initiation, progression and treatment resistance. Methods: We conducted a comprehensive narrative review of recent literature on multi-omics integration in urological cancers. Sources included PubMed, Scopus and Web of Science, and only English-language peer-reviewed studies published before September 2025. We synthesised findings from studies employing genomics, transcriptomics, proteomics, metabolomics and epigenomics, alongside computational integration frameworks, such as machine learning, graph neural networks, stemnessbased classifiers and spatial multi-omics. Results: Multi-omics integration enables the refinement of molecular subtypes, identification of prognostic and predictive biomarkers and discovery of therapeutic targets across prostate, bladder and renal cancers. Examples include stemness-based classifiers in prostate cancer that stratify patients by prognosis and therapy sensitivity, consensus molecular subtypes of bladder cancer with differential therapeutic vulnerabilities and programmed cell death-based signatures in renal cancer linked to prognosis and immune responses. However, key challenges persist, including data heterogeneity, limited cohort sizes, lack of standardised analytical pipelines and translational gaps between discovery and clinical implementation. Conclusions: Multi-omics integration is rapidly evolving from an exploratory research tool into a cornerstone of precision urology. Through mechanistically grounded, clinically interpretable models of disease, multi-omics holds the potential to improve individualised diagnosis, prognostication and therapy selection. Translation of multi-omics into routine clinical practice will hinge on overcoming current limitations through standardisation, collaborative consortia and explainable artificial intelligence.
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