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The role of the urinary microbiome in genitourinary cancers

Nature Reviews Urology (2025)Cite this article

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Abstract

Genitourinary cancers account for 20% of cancer instances globally and pose a substantial burden. The microbiome, defined as the ecosystem of organisms that reside within and on the human body, seems to be closely related to multiple cancers. Research on the gut microbiome has yielded substantial insights into the interactions of this entity with the immune system and cancer therapeutic efficacy, whereas the urinary microbiome has been relatively less well-studied. Advances in next-generation sequencing technologies led to new discoveries in the urinary microbiome, which might aid in early detection, risk stratification and personalized treatment strategies in genitourinary cancers. Mechanistic investigations have also suggested a role for the urinary microbiome in modulating the tumour microenvironment and host immune response. For example, distinct urinary microbial signatures have been linked to bladder cancer occurrence and recurrence risk, with specific taxa associated with cytokine production and inflammation. Urinary microbiome signatures have also been explored as potential biomarkers for non-invasive cancer detection. However, challenges remain in standardizing methodologies, validating findings across studies, and establishing causative mechanisms. As investigations into the urinary microbiome continue to evolve, so does the potential for developing microbiome-modulating therapies and enhancing diagnostic capabilities to improve outcomes in patients with genitourinary cancers.

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Fig. 1: Microbial populations in urinary health and genitourinary cancers.
Fig. 2: Proposed mechanisms through which the urinary microbiome might influence bladder cancer.

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Authors and Affiliations

  1. The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Seoho Lee

  2. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Karen Sfanos

  3. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA

    Karen Sfanos & Nirmish Singla

  4. Department of Urology, Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Karen Sfanos & Nirmish Singla

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  1. Seoho Lee
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  2. Karen Sfanos
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N.S. and S.L. researched data for the article. All authors contributed substantially to discussion of the content. N.S. and S.L. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Nirmish Singla.

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Glossary

ACE index

Estimates species richness with greater sensitivity to rare operational taxonomic units.

Amplicon sequence variants richness

Counts the number of unique amplicon sequence variants richness observed in a sample.

Bray–Curtis distances

Non-phylogenetic distance metric that quantifies dissimilarity between two samples based on species abundance and proportion of shared species.

Chao1 index

Estimates total species richness in a sample by accounting for the presence of rare taxa.

Faith’s Phylogenetic Diversity test

Estimates biodiversity by calculating the total branch length of a phylogenetic tree for species in a sample.

Gini–Simpson index

Calculates the probability that two randomly selected organisms in a sample are different.

Jaccard

Measures the proportion of shared species between two samples, ranging from 0 (completely different) to 1 (identical).

Linear discriminant analysis effect size

Statistical method that identifies and ranks differentially abundant features (for example, taxa) between groups using linear discriminant analysis.

Observed species index

Counts the total number of unique species identified in a sample.

Operational taxonomic units

Groups of closely related microbial sequences used to classify microorganisms based on genetic similarity.

Pielou’s evenness

Measures how evenly species are distributed in a sample, ranging from 0 (uneven) to 1 (perfectly even).

Shannon index

Measures microbial diversity by incorporating species richness and evenness.

Simpson index

Calculates the probability that two randomly selected organisms in a sample are the same.

Unweighted Unifrac

Phylogenetic distance metric that compares microbial communities using the presence or absence of taxa, without abundance data.

Weighted Unifrac

Phylogenetic distance metric that compares microbial communities using the presence and relative abundance of taxa.

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Lee, S., Sfanos, K. & Singla, N. The role of the urinary microbiome in genitourinary cancers. Nat Rev Urol (2025). https://doi.org/10.1038/s41585-025-01011-z

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