The Role of the Urinary and Gut Microbiome in Bladder Cancer: Emerging Insights and Clinical Implications

Abstract

Bladder cancer (BCa) arises from the interaction between environmental exposures and the host’s immunity and microbiome. Once considered sterile, the urinary tract is now known to harbor a resident urinary microbiome (UM) that dynamically interacts with the immune system and is influenced by systemic immunomodulatory effects of the gut microbiome (GM) brought on by the emerging gut–bladder axis. Accumulating evidence links alterations in UM and GM leading to BCa development, progression, and recurrence. Loss of protective taxa (e.g., Lactobacillus, Bifidobacterium and Ruminococcus) and enrichment of pro-inflammatory or genotoxic bacteria (e.g., Fusobacterium, Acinetobacter, Prevotella and Enterobacteriaceae) are associated with immune evasion and systemic inflammation. Microbial metabolites, especially short-chain fatty acids (SCFAs), play a key role in shaping tumor immunity and show diagnostic and prognostic potential, with specific microbial signatures correlating with recurrence risk, survival, and treatment response. Therapeutically, growing evidence suggests that microbiome composition influences immunotherapy response, highlighting opportunities for microbiome-based interventions. This review aims to summarize the rationale to implement microbial modulation strategies (e.g., dietary modulation, probiotics, fecal microbiota transplantation (FMT), and emerging synbiotic or postbiotic approaches) while addressing their current limitations and future requirements in order to develop microbiome-guided therapies, diagnostics and prognostic tools for BCa.

1. Introduction

Bladder cancer (BCa) development is intrinsically linked to interactions between the host’s immune system and external exposures. Host responses to carcinogenic stimuli activate multiple inflammatory pathways that promote dysbiosis, sustaining a chronic pro-inflammatory state that favors oncogenesis [1].

Microbial DNA has been found even in urine samples deemed sterile, revealing a resident urinary microbiome (UM) that interacts closely with the immune system and external pathogens [1]. The role of the UM in BCa appears bidirectional: certain bacterial communities contribute to oncogenesis, whereas others exert tumor-suppressive effects and enhance response to immunotherapies [1,2].

Beyond the bladder, the gut microbiome (GM) is now being studied as a key modulator of BCa through the gut–bladder axis, a bidirectional network linking gastrointestinal and urinary immune responses [3]. Gut dysbiosis can promote BCa progression by driving systemic inflammation and altering commensal UM [4]. Several taxa, including Streptococcus, Dorea and Bacteroides salyersiae, have demonstrated positive associations with BCa through increased production of metabolites such as N-palmitoyl-sphinganine and N-methylproline. At the same time, Bacteroides dorei exhibits protective effects via 2,3-dihydroxypyridine [3]. Microbial metabolites, such as short-chain fatty acids (SCFAs), play a central role by modulating inflammation, immune activation, and epithelial integrity; their loss during dysbiosis may favor pro-inflammatory signaling and tumor growth. Emerging evidence suggests that correcting microbiota imbalances may enhance the efficacy of immunotherapies such as immune checkpoint inhibitors (ICI) and Bacillus Calmette–Guérin (BCG), offering new avenues for precision immuno-oncology in BCa [3,4,5].

At present, the human evidence linking the urinary microbiome to bladder cancer is largely associative; the causal inference relies primarily on indirect support from preclinical and translational models that have no standardized protocols, which diminishes association strength. This review therefore adopts a narrative synthesis framework to integrate heterogeneous clinical, microbiological, and immunological evidence.

2. The Urinary Microbiome: Composition and Function

Since the recognition of a resident UM, extensive efforts have sought to define a commensal or baseline microbial profile to identify dysbiosis-associated taxa better and to develop targeted therapies for BCa. At present, a consensus has not been reached. Nevertheless, studies in healthy individuals consistently report a prevalence of Actinobacteriota, Bacteroides, Corynebacteria, Escherichia, Enterococci, Finegoldia, Firmicutes, Fusobacteria, Lactobacilli, Peptoniphilus, Prevotella, Proteobacteria, Shigella, Streptococci and Staphylococci [1,6,7]. This indicates that, although each person’s microbial composition varies, a stable core of taxa appears to be shared across individuals.

2.1. Factors Affecting the Urinary Microbiome Composition

Environmental factors not only induce direct genotoxic damage but also reshape microbial communities, generating an inflammatory milieu that contributes to tumor initiation and progression [6,8]. Among these factors, smoking and occupational exposure represent the principal risk factors for BCa [2,9,10], with smoking alone accounting for up to 50% of BCa incidence. Smoking status correlates with specific shifts in UM composition, including increased abundance of Anaerostipes [11], Enterobacter, Uruburuella, and Rouxiella [12], supporting its role as a major environmental driver. Occupational carcinogens encountered in textile, rubber, dye, and chemical manufacturing industries, as well as among metalworkers, painters, and firefighters, have also been associated with the development of BCa [2,9]. For example, pesticide exposure has been linked to the enrichment of Cupriavidus in the UM [13], while firefighters exhibit enrichment of Peptostreptococcus anaerobius and depletion of Streptococcus in the GM [14]. Other contributing factors include ionizing radiation, medications (e.g., cyclophosphamide and chlornaphazine), opium consumption, Schistosoma infection and dietary patterns [9,10].

Sex is a major determinant of UM composition, likely reflecting that anatomical differences such as microbial sources originate from gastrointestinal and vaginal niches [1]. In females, the UM is commonly enriched with Lactobacillus, along with taxa such as Actinobacteria, Anaerococcus, Atopobium, Finegoldia and Streptococcus [15,16]. In contrast, male UM profiles show a prevalence of Corynebacterium, Eubacterium, Pseudomonas, Staphylococcus, Streptococcus [1] and Veillonella [2]. These sex-related differences in baseline urinary microbiome composition may contribute to the known disparities in bladder cancer incidence and immune responses between men and women.

Hormone-related changes further influence UM composition, with postmenopausal women exhibiting reduced Lactobacillus [17,18] and increased prevalence of Escherichia [1,6].

Age is also an important determinant, as BCa predominantly affects older adults [19], suggesting a greater susceptibility to dysbiotic changes due to immunosenescence. The decrease in specific taxa, such as Acinetobacter and Corynebacterium [15,19], has been linked to older age, whereas Gardnerella has been associated with young women [1]. These variations in β-diversity may contribute to a permissive inflammatory environment that favors BCa development and recurrence.

Geographic origin is another important determinant of both UM and GM composition, though its role in BCa has received little attention. Gut microbial communities differ across world regions, with dietary and lifestyle practices consistently identified as the most influential drivers of this variation [20]. Western, industrialized living has been associated with reduced bacterial diversity and the loss of native species such as Prevotella copri and fiber-degrading functional pathways, changes that persist in subsequent generations following migration from non-industrialized settings [20]. An analysis of over 168,000 gut microbiome samples from 68 countries showed that genus-level composition alone could predict a sample’s geographic region of origin, and that regions such as Central and Southern Asia (3.4% of available samples), Sub-Saharan Africa (3.7%) and Latin America remain severely underrepresented relative to Europe and North America, which together account for over 60% of all sequenced data [21]. BCa incidence follows a similarly uneven global distribution. According to GLOBOCAN 2022 data, an estimated 614,298 new BCa cases and 220,596 deaths occurred worldwide, with approximately 75% of the burden in males [22]. Incidence rates vary at least 12-fold among men, with the highest age-standardized rates in Southern and Western Europe and North America and the lowest in Middle Africa and South–Central Asia [9,22]. In Northern Africa, schistosomiasis contributes to an elevated incidence and a distinct histological profile that differs from that of urothelial carcinoma, which is predominant in Western populations [22]. Whether region-specific differences in baseline microbial composition confound or contribute to these epidemiological patterns has not been examined.

Regional dietary patterns, including variable consumption of fermented foods, dietary fiber, and animal protein, shape GM composition and its metabolite output, and may therefore act as uncontrolled confounders in studies linking dysbiosis to BCa [20]. UM and GM profiling in BCa cohorts has been performed predominantly in East Asian, European, and North American populations, and the applicability of reported microbial signatures to other demographic groups remains untested.

2.2. Sampling Methods and Analysis

Low-biomass microbiome studies, such as those found in UM samples [23], are highly susceptible to contamination from laboratory reagents, consumables, and the environment, requiring rigorous contamination controls all throughout their processing [24]. Negative extraction controls (extraction blanks) and sequencing blanks are essential for detecting and quantifying background DNA introduced during sample processing and sequencing (e.g., contaminants from DNA extraction kits or “kitome”), as they can dominate sequencing reads in low-biomass samples and because they vary significantly between reagent brands, lots and laboratories [25]. Routine inclusion and sequencing of negative controls in every batch should be strongly recommended to monitor and characterize contaminant profiles across studies. Reporting standards should emphasize transparency; the documentation of how and when a sample was taken, as well as the quality assurance measures used, should be clearly stated on each study [26,27]. Analytical best practices include thresholding approaches based on the abundance of dominant contaminants in controls, and the application of computational tools, such as Decontam, to statistically identify and remove contaminant sequences based on their prevalence in negative controls and their inverse correlation with sample DNA concentration [25]. Adoption of these controls and reporting standards is critical to ensure the validity and reproducibility of findings in low-biomass metagenomic studies.

Urine sampling methodology remains a critical determinant of UM characterization. The three main sampling methods used include midstream voided urine collection, transurethral catheterization (TUC) and suprapubic aspiration (SPA). SPA is the gold standard, not only because it bypasses all types of contaminants, but because it shows the highest fidelity when representing UM composition [28], yet it is the most invasive. Midstream voided urine collection is the most commonly used, despite its high susceptibility to contamination [4]. Due to anatomical variants, female samples overrepresent vaginal microbes, such as Lactobacillus and Gardnerella [16], whereas urethral microbes, such as Corynebacterium, contaminate male samples [29]. Thus, TUC offers a pragmatic compromise between feasibility and reduced contamination when examining urine samples, as it bypasses sex-related bias; however, its invasiveness still limits its feasibility for population-level screening [28,30,31].

Most studies use midstream voided urine samples to identify the UM in healthy patients as its less invasive methodology could warrant more willing participants, yet contamination remains the biggest worry. Because sex-related differences have already been recognized between male and female midstream urine samples, the implementation of strategies that limit the representability of contaminant taxa should be implemented as the search for a baseline UM is pursued. Strategies include identifying potential contaminants using decontamination bioinformatics, reporting the concentrations of 16S rRNA genes in negative controls alongside samples to assess signal to noise and carefully checking sequence data from negative and positive controls to assess potential sources of contamination or cross-contamination in order to not highlight taxa that have already been identified as contaminants [23].

Microbiome analysis is primarily conducted through 16S rRNA gene sequencing or shotgun metagenomics. Although 16S rRNA sequencing is the most widely used method, it has limited taxonomic resolution due to its reliance on PCR amplification, which does not capture all microbial species, and because it can be affected by sequence artifacts that distort sample results [32]. In contrast, shotgun metagenomics enables the capture of all genomic material within a sample, thereby achieving higher taxonomic resolution. These advantages, however, come at the cost of higher sequencing expenses, greater computational demands and challenges associated with host DNA contamination and incomplete reference databases [16,32,33].

To standardize urine sampling methods, consensus is needed to balance accurate representation of the UM with patient safety and comfort. Achieving this will require large multicenter studies designed to address current gaps and inconsistencies arising from heterogeneous patient selection, laboratory protocols, and sample collection and processing methods, which limit reproducibility and cross-study comparability, thereby contributing to fragmented and sometimes conflicting characterizations of the UM.

3. Microbiome and Bladder Carcinogenesis

3.1. Mechanistic Insights

The UM undergoes significant changes in diversity and composition in the presence of BCa, with dysbiosis acting both as a driver and consequence of tumor development [13,29,34] (

Table 1. Studies linking urinary or gut microbiome alterations with bladder cancer.

Sample Type	Male/Female	Key Microbial Findings	Clinical Endpoint	Reference
Urine	NA	Smoking linked to Enterobacter, Uruburuella, Rouxiella enrichment	BCa risk association	[6]
Urine	NA	Smoking-associated with Anaerostipes; Mycobacterium undetectable > 1 week post-BCG	BCG response	[8]
Urine	NA	BCa enriched in Fusobacterium, Actinobaculum, Facklamia, Veillonella, Corynebacterium	BCa vs. control	[2]
Urine	NA	Pesticide exposure linked to Cupriavidus; Lactobacillus/Pseudomonas/Bacillus activate anticancer pathways	BCG response	[13]
Stool	NA	Firefighters show Peptostreptococcus anaerobius enrichment, Streptococcus depletion	Occupational exposure	[14]
Tissue	NA	Pre-BCG: Actinobacteriota predominance; non-recurrence: Lactobacillus enrichment	BCG response/recurrence	[17]
Urine, Tissue	NA	Curvibacter enriched in BCa; tissue shows Acinetobacter, Akkermansia, Sphingomonas	BCa vs. control	[31]
Urine	NA	Pre-BCG enrichment of Actinobacteriota, Bacteroidetes, Proteobacteria, Firmicutes	BCG-induced immunity	[30]
Urine	NA	Prevotella/Corynebacterium-dominant urinetypes associated with higher disease risk; 12-genus model AUC ~90%	BCa diagnosis	[29]
Urine, Tissue	NA	Acinetobacter/Corynebacterium decreased in elderly; recurrence linked to specific taxa	BCa/recurrence	[15]
Urine	NA	Lower alpha diversity in non-recurrence; higher diversity linked to recurrence risk	Recurrence-free survival	[18]
Tissue	NA	Sphingomonas, Massilia, Aquabacterium enriched; reduced GM diversity in BCa	BCa tissue dysbiosis	[13]
Urine	NA	Recurrence: Prevotella, Massilia, Thermomonas enriched; reduced Lactobacillus	BCa recurrence	[29]
Urine	NA	BCG transiently alters UM toward pro-inflammatory state	BCG response	[34]
Urine, Tissue	NA	Escherichia–Shigella associated with reduced gemcitabine efficacy; Pseudomonas/Bifidobacterium protective	Chemotherapy response	[36]
Stool	NA	Prevotella enriched; Lactobacillus, Bifidobacterium, Ruminococcus reduced in BCa	Neoadyuvant chemotherapy response	[11]
Stool	NA	Blautia coccoides/trigonelline enhance CD8+ T-cell killing; Bacteroides/Blautia in recurrence	ICI/BCG response	[37]
Urine	NA	Reduced Pseudomonas fluorescens/putida impairs DNA sensing, antigen presentation	NMIBC to MIBC progression	[38]
Stool	NA	Veillonellaceae/Prevotellaceae correlate with poor survival, elevated neutrophil-to-lymphocyte ratio (NLR)	ICI response/survival	[39]
Urine	NA	Recurrence group: reduced Lactobacillus/Lacticaseibacillus; less diversity	BCG/chemo response	[40]
Urine	NA	Pre-BCG: Corynebacterium, Prevotella, Streptococcus enriched	BCa vs. control	[41]
Urine	NA	BCG responders vs. non-responders show distinct urinary microbiota	BCG response	[42]
Urine	NA	Higher alpha diversity associated with increased recurrence risk	BCa recurrence	[43]
Stool	NA	Clostridiales/Ruminococcaceae/Faecalibacterium: increased effector T-cells post anti-PD-1	ICI response	[44]
Stool	NA	Parabacteroides distasonis produces 3-IAA; inhibits tumor migration	BCa prognosis	[45]

NA: Not available.

). Comparisons between BCa and control samples consistently reveal a chronic inflammatory milieu driven by pathological host–microbe interactions with the environment [35]. The loss of beneficial taxa such as Lactobacillus, Ruminococcus, Bifidobacterium, and Pseudomonas is associated with reduced antigen presentation and impaired cytosolic DNA sensing, thereby facilitating immune evasion [11,36].

In parallel, there is an enrichment of pro-inflammatory taxa, such as Parabacteroides [37], Curvibacter [31], Prevotella [11], Fusobacterium, Klebsiella, Enterobacter [38], Sphingomonas, Corynebacterium, Massilia, and Aquabacterium [13,29,34], that drive a chronic inflammatory milieu. Taxa such as Akkermansia, Acinetobacter [39], and Bacteroides [11] degrade mucin and compromise epithelial integrity, exposing submucosal layers to microbial antigens and inflammatory mediators [12,46]. Certain taxa produce carcinogenic metabolites or toxins, including colibactin produced by colibactin-producing Klebsiella pneumoniae and related Enterobacteriaceae, which induce DNA double-strand breaks and genomic instability [1]. Together, these processes generate a tumor-permissive environment favored by chronic inflammation, DNA damage, and impaired immune surveillance [15,33] (

Table 2. Microbial taxa associated with bladder cancer.

Taxon	Association	Sample Source	Male/Female	Proposed Mechanism	Reference
Acinetobacter	Risk	Urine, Tissue	NA	Mucin degradation, epithelial barrier disruption; decreased in elderly populations	[1]
Actinobaculum	Risk	Urine	NA	Not specified	[2]
Actinomyces	Risk	Urine	NA	Not specified (pre-BCG enrichment)	[41]
Akkermansia	Risk	Tissue	NA	Mucin degradation, epithelial barrier disruption	[1]
Anaerococcus	Risk	Urine	NA	Not specified	[1]
Anaerostipes	Risk	Urine	NA	Smoking-associated UM shift	[11]
Anoxybacillus	Risk	Urine, Tissue	NA	Oxidative damage, metabolic disturbances	[47]
Aquabacterium	Risk	Urine	NA	Pro-inflammatory milieu	[29]
Atopobium	Risk	Urine	NA	Not specified	[15]
Bacteroides	Risk	Urine, Gut	NA	Mucin degradation, epithelial barrier disruption; elevated regulatory T-cells and myeloid-derived suppressor cells	[1]
Bacteroides salyersiae	Risk	Gut	NA	Increased N-palmitoyl-sphinganine and N-methylproline production	[3]
Bilophila	Risk	Gut	NA	Established role in carcinogenesis and inflammatory conditions	[48]
Blautia	Risk (recurrence)	Urine, Gut	NA	Associated with recurrence post-BCG	[37]
Campylobacter	Risk	Urine	NA	Not specified	[36]
Corynebacterium	Risk/Context-dependent	Urine, Tissue	NA	Pro-inflammatory milieu; variable by context	[31]
Cupriavidus	Risk	Urine, Tissue	NA	Linked to pesticide exposure	[13]
Curvibacter	Risk	Urine	NA	Pro-inflammatory milieu	[31]
Dorea	Risk	Gut	NA	Increased metabolite production associated with BCa	[3]
Enterobacter	Risk	Urine, Tissue	NA	Pro-inflammatory milieu	[6]
Enterobacteriaceae	Risk	Urine, Tissue	NA	Colibactin production causing DNA double-strand breaks and genomic instability	[1]
Escherichia	Risk	Urine, Tissue	NA	Not specified; increased in postmenopausal women	[1]
Escherichia–Shigella	Risk	Urine	NA	Associated with reduced gemcitabine efficacy	[36]
Facklamia	Risk	Urine	NA	Not specified	[36]
Faecalibacterium	Risk (recurrence)	Urine, Gut	NA	Associated with recurrence post-BCG	[44]
Firmicutes	Risk	Urine, Gut	NA	Oxidative damage, metabolic disturbances (expansion in dysbiosis)	[18,29,42]
Fusobacterium	Risk	Urine, Gut	NA	Pro-inflammatory; established role in carcinogenesis	[36]
Geobacillus	Risk	Tissue	NA	Not specified	[15]
Jonquetella anthropi	Risk	Urine	NA	Not specified	[2]
Klebsiella	Risk	Urine, Tissue	NA	Pro-inflammatory milieu	[1]
Klebsiella pneumoniae	Risk	Tissue	NA	Colibactin production causing DNA double-strand breaks and genomic instability	[1]
Lachnospirales	Risk (recurrence)	Gut	NA	Associated with recurrence	[37]
Lachnospiraceae	Risk (recurrence)	Gut	NA	Linked to recurrence and progression post-treatment	[37]
Massilia	Risk	Urine	NA	Pro-inflammatory milieu; oxidative damage	[29]
Parabacteroides	Risk	Urine	NA	Pro-inflammatory milieu	[45]
Peptostreptococcus anaerobius	Risk	Gut	NA	Enriched in firefighters (occupational exposure)	[14]
Porphyromonas	Risk	Urine	NA	Not specified (pre-BCG enrichment)	[11]
Prevotella	Risk	Urine, Gut	NA	Pro-inflammatory milieu; poorer survival outcomes	[18,49]
Pelomonas	Risk	Tissue	NA	Not specified	[15]
Prevotellaceae	Risk	Gut	NA	Poorer survival, elevated neutrophil-to-lymphocyte ratio	[39]
Proteobacteria	Risk	Urine, Gut	NA	Oxidative damage, metabolic disturbances; expansion in dysbiosis	[2]
Proteus	Risk (recurrence)	Urine	NA	Associated with recurrence post-BCG	[36]
Ralstonia	Risk	Tissue	NA	Not specified	[29]
Rhodococcus	Risk	Tissue	NA	Not specified	[38]
Rhizobium	Risk	Tissue	NA	Not specified	[38]
Rubrobacter	Risk	Tissue	NA	Not specified	[1]
Rouxiella	Risk	Urine	NA	Smoking-associated UM shift	[6]
Ruminococcaceae	Risk	Urine	NA	Not specified	[44]
Ruminococcus torques	Risk (recurrence)	Gut	NA	Associated with recurrence	[37]
Shigella	Risk	Urine, Tissue	NA	Not specified	[15]
Sphingomonas	Risk	Urine	NA	Pro-inflammatory milieu	[31]
Staphylococcus	Risk (recurrence)	Urine	NA	Associated with recurrence post-BCG	[18]
Streptococcus	Risk/Context-dependent	Urine, Gut	NA	Increased metabolite production; context-dependent	[14]
Thermomonas	Risk	Urine	NA	Oxidative damage, metabolic disturbances	[29]
Tolumonas	Risk (recurrence)	Urine	NA	Associated with recurrence post-BCG	[11]
Uruburuella	Risk	Urine	NA	Smoking-associated UM shift	[6]
Veillonella	Risk (recurrence)	Urine, Gut	NA	Associated with recurrence post-BCG	[29]
Veillonellaceae	Risk	Gut	NA	Poorer survival, elevated neutrophil-to-lymphocyte ratio	[39]
Bacteroides dorei	Protective	Gut	NA	Production of 2,3-dihydroxypyridine	[3]
Bifidobacterium	Protective	Urine, Gut	NA	Reduced antigen presentation/immune evasion when lost; SCFA production; enhances anti-PD-L1 efficacy	[11]
Bifidobacterium longum	Protective	Gut	NA	Enhances ICI response via FMT	[50]
Blautia	Protective (SFA producer)	Gut	NA	SCFA production (context-dependent)	[51]
Blautia coccoides	Protective	Gut	NA	Enhances CD8+ T-cell-mediated tumor killing; reduces pro-inflammatory cytokines; metabolite trigonelline inhibits β-catenin	[37]
Clostridiales	Protective	Gut	NA	Increased circulating effector CD4+ and CD8+ T-cells post anti-PD-1	[44]
Clostridium butyricum MIYARI 588	Protective	Gut	NA	Promotes TRAIL and MMP-8 secretion; suppresses angiogenesis; reduces metastasis-associated cytokines	[52]
Eubacterium	Protective	Gut	NA	SCFA production	[51]
Enterococcus faecium	Protective	Gut	NA	Enhances ICI response via FMT	[50]
Akkermansia muciniphila	Protective	Gut	NA	Enhances ICI response via FMT; SCFA production	[50]
Faecalibacterium	Protective (ICI response)	Gut	NA	Increased circulating effector CD4+ and CD8+ T-cells post anti-PD-1	[44]
Lactobacillus	Protective	Urine, Gut	NA	Anti-inflammatory SCFA production (butyrate, propionate); lactic acid and bacteriocin production; competition for adhesion sites; increases BCG internalization via fibronectin binding; activation of antioxidant/anticancer pathways	[51]
Lactobacillus delbrueckii	Protective	Gut	NA	Anticarcinogenic properties and immune-enhancing effects (fermented dairy)	[53]
Lactobacillus_mucosae	Protective	Gut	NA	Increased in non-recurrence groups	[37]
Lacticaseibacillus	Protective	Urine	NA	Anti-inflammatory SCFA production; immunoregulatory mechanism stabilizing urinary microenvironment	[40]
Lactococcus	Protective	Urine	NA	Immunoregulatory mechanism; lactate production	[51]
Methanobrevibacter	Protective	Urine, Gut	NA	Increased in non-recurrence groups post-BCG	[37]
Mycobacterium	Protective (BCG-related)	Urine	NA	BCG-induced pro-inflammatory immune response supporting tumor control	[17]
Parabacteroides distasonis	Protective	Gut	NA	Produces 3-indoleacetic acid (3-IAA) inhibiting tumor cell migration; reduces fatty acid synthase and stearoyl-CoA desaturase expression	[45]
Pseudomonas	Protective	Urine, Tissue	NA	Reduced antigen presentation/immune evasion when lost; activation of antioxidant/anticancer pathways (LPS biosynthesis pathway)	[51]
Pseudomonas fluorescens	Protective	Urine	NA	Activates DNA sensing, antigen presentation, leukocyte transendothelial migration pathways	[38]
Pseudomonas putida	Protective	Urine	NA	Activates DNA sensing, antigen presentation, cytokine signaling, WNT/β-catenin pathways	[38]
Roseburia	Protective	Gut	NA	SCFA production	[51]
Ruminococcus	Protective	Urine, Gut	NA	Reduced antigen presentation/cytosolic DNA sensing when lost; SCFA production; increased effector T-cells post anti-PD-1	[11]
Streptococcus thermophilus	Protective	Gut	NA	Anticarcinogenic properties and immune-enhancing effects (fermented dairy)	[53]

NA: Not available.

).

3.2. Preclinical and Human Evidence

Studies comparing UM profiles between BCa and control patients have shown an increased prevalence of Actinobaculum, Anaerococcus, Atopobium, Bacteroides, Campylobacter, Corynebacterium, Curvibacter [31], Enterobacter, Escherichia, Facklamia, Fusobacterium, Jonquetella anthropi, Ruminococcaceae, Shigella, Streptococcus and Veillonella [2,36] in BCa samples, while also showing decreased protective or anti-inflammatory taxa like Bifidobacterium, Lactobacillus [31] and Ruminococcus [37], which could lead to a loss in homeostasis and thus favor inflammation.

In tissue samples, Actinobacteriota (Rhodococcus), Bacteroidota (Bacteroides), Firmicutes (Clostridia), Proteobacteria (e.g., Acinetobacter, Enterobacter, Klebsiella, Pseudomonas), and Verrucomicrobiota (Akkermansia) have shown prevalence [31,36]. Studies comparing urine and tissue bladder samples have shown that they share over 80% similarities, suggesting that the UM may accurately mirror tissue microenvironment [6].

Not only do we wonder what kind of microbial disarray promotes cancer but also what changes in the UM favor or lessen recurrence. Microbial signatures should not be assumed to exert uniform biological effects across bladder cancer subtypes, as non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC) differ substantially in tumor biology, immune microenvironment, and therapeutic context. Several studies have compared the UM in BCa patients with and without recurrence. Across cohorts assessing the response of BCa to BCG, α-diversity and β-diversity findings are heterogeneous. However, recurrence is more consistently associated with the enrichment of specific pro-inflammatory taxa such as Actinobacteria, Bacteroidota, Firmicutes, Proteobacteria, Prevotella, Streptococcus, Corynebacterium, and Enterobacteriaceae, which have been linked to oxidative damage and metabolic disturbances that foster a tumor-promoting niche [5,15,29,40]. On the other hand, the depletion of Lactobacillus [18,42], which normally produces anti-inflammatory SCFAs such as butyrate and propionate, could suggest the removal of a critical regulatory check on inflammation [33]. This demonstrates that specific bacterial composition may be more informative than total α- or β-diversity [2] and underscores the importance of defining microbial biomarkers in BCa (Table 1).

Among patient characteristics, sex differences have been the most frequently examined variable in comparisons of UM samples. Yet, age has also been shown to affect the diversity of the UM. In adolescents, Anaerococcus, Corynebacterium, Firmicutes, Gardnerella, Lactobacillus, Prevotella, Streptococcus and Veillonella are dominant. Younger adults show a higher prevalence of Lactobacillus and Gardnerella, with dominant Firmicutes. Finally, the elderly show increased Proteobacteria and decreased Lactobacillus, with increased levels of Acinetobacter, Actinomyces, Anaerococcus, Bacillus, Brevundimonas, Corynebacterium and Fusobacterium [1,7].

One recurring limitation in UM research is the limited statistical power [18] of most studies, which restricts its external validity. Cohort generalizability is constrained by several factors, most notably small sample sizes, which are frequently under 100 participants and predominantly composed of elderly male populations [54].

The geographic and ethnic composition of study cohorts further constrains generalizability. Most UM studies have recruited participants from single institutions in East Asia, Europe, or North America, with virtually no representation from Sub-Saharan Africa, the Middle East, South Asia, or Latin America. Over 60% of all publicly available gut microbiome data originates from Europe and North America, while Central and Southern Asia and Sub-Saharan Africa together contribute less than 8% [21]. Baseline GM and UM profiles differ across populations due to dietary habits, environmental exposures, and host genetics, and microbial taxa reported as enriched or depleted in one cohort may not replicate in another [20,21]. Prevotella, for instance, is a dominant genus in non-industrialized populations but is characteristically depleted in Western cohorts; it has also been reported as an enriched pro-inflammatory taxon in BCa urine samples, raising the question of whether this association reflects disease biology or geographic confounding [11,20]. These considerations are compounded by known disparities in BCa outcomes across populations: incidence rates vary at least 12-fold across world regions, five-year survival ranges from approximately 80% in the United States to below 40% in transitioning healthcare systems, and schistosomiasis-driven squamous cell histology in parts of Africa may involve distinct microbial interactions not captured in studies focused on urothelial carcinoma [9,22]. Multi-ethnic, geographically distributed cohorts will be necessary to determine whether current microbial associations hold across diverse patient populations.

Additionally, a patient’s background history, factors such as sexual activity, diet, exercise, comorbidities, recent instrumentation, infections, and use of medications, to name a few, introduce sustainable variability. These factors should be explicitly reported, as background standardization is essential to minimize heterogeneity and to ensure that findings can be replicated and compared across different populations [11,55].

Furthermore, there remains a lack of standardized methodologies for urine collection, preservation, and storage in microbiome research. Studies have explored various storage conditions, and results indicate that lower temperatures, shorter storage times, and the use of preservatives contribute most effectively to the reproducibility of the UM [14]. Collectively, these limitations underscore the importance of standardized protocols in UM research to enable cross-study comparisons.

4. Gut Microbiome and Systemic Immunity in Bladder Cancer

The gut–bladder axis is an emerging field of research that seeks to explain the link between dysbiosis in the GM and UM and BCa pathogenesis. Disrupting factors such as age, diet and infection have been associated with alterations in GM diversity, creating a systemic environment that may favor cancer initiation and progression [19] through a pro-inflammatory systemic profile that reshapes the commensal UM [34] (Figure 1). In this context, systemic immune modulation emerges as a critical component of cancer prevention and control, potentially mediated through interactions along the gut–bladder axis.

Systemic immune modulation by the GM is primarily mediated through immune cell programming within the intestinal mucosa. Gut-resident microbes and their metabolites interact with epithelial and antigen presenting cells (APCs), shaping both innate and adaptive immunity by influencing cytokine production, T-cell polarization, and immune cell metabolism. These locally primed immune cells subsequently enter systemic circulation, where they can modulate immune surveillance and inflammatory responses in distal tissues [13,51,56]. Specifically in BCa treatment, effective immune cell function becomes crucial, as both BCG and ICI therapies depend on proper antigen presentation and cytotoxic cell priming to arrest cancerous cell growth.

Gut dysbiosis, characterized by reduced GM diversity [13], is marked by an imbalance of functionally beneficial bacterial taxa observed in members of Actinobacteriota, Bacteroidota and Verrucomicrobiota [57], which allows the expansion of dysbiotic taxa such as Proteobacteria, Firmicutes [58], Bilophilia and Fusobacterium, the latter two having an established role in carcinogenesis and inflammatory conditions of the gut [48,59]. Concomitantly, intestinal barrier dysfunction and increased intestinal permeability enable microbial components to translocate into the systemic circulation, promoting chronic inflammation that may affect distant organs, such as the bladder, and facilitating cancer progression [13,60]. Such systemic inflammatory spillover provides a plausible mechanistic link of how gut dysbiosis could favor BCa susceptibility.

Microbial metabolites produced by the GM, including SCFAs, lactate, and succinate, play key roles in maintaining epithelial and mucosal barrier integrity, regulating gene expression, and serving as energy substrates for immune cells. Importantly, the production of these metabolites reflects the functional state of the GM. SCFAs (butyrate, propionate and acetate) are produced by genera including Akkermansia, Bacteroides, Bifidobacterium, Blautia, Faecalibacterium, Eubacterium, Roseburia, and Ruminococcus. Lactococcus and Bifidobacterium primarily produce lactate, while succinate is generated by taxa such as Bacteroides, Blautia, and Prevotella [51]. In BCa patients undergoing BCG induction, the reduced abundance of immunoregulatory bacteria and metabolites in urine samples may reflect GM dysregulation, contributing to a pro-inflammatory environment that can impair antitumor immune responses and favor disease persistence [30].

Beyond shaping systemic inflammatory tone, GM composition also influences antitumor immunity by modulating immune surveillance mechanisms. Balanced microbial communities and their metabolites support the effective activation of cytotoxic CD8⁺ T-cells, natural killer (NK), and Th1-polarized responses, which are critical for tumor control. In contrast, dysbiosis-associated chronic inflammation may impair antitumor immune efficacy by promoting immune exhaustion and tolerogenic phenotypes, thereby facilitating tumor persistence and progression. These systemic immune effects are particularly relevant in BCa, where immune-mediated therapies such as BCG rely on intact antitumor immune responses [56].

Emerging evidence indicates that fecal miocrobiota transplant (FMT) may enhance responses to ICI by reshaping the GM in ways that support antitumor immunity in cancers such as melanoma. The main mechanisms hypothesized to underlie this effect include the enrichment of beneficial bacterial taxa such as Bifidobacterium longum, Enterococcus faecium and Akkermansia muciniphila [50], increased gut microbial α-diversity, and immune modulation characterized by reduced inflammation [61]. Collectively, these findings support a model in which GM diversity regulation could help influence antitumor immunity and therapeutic responses to ICI in BCa.

Consistent with this hypothesis, ICI responders exhibit distinct gut microbial profiles compared with non-responders. These patterns provide a biological rationale for the use of FMT, as the transfer of a favorable microbial ecosystem may confer or restore ICI sensitivity in selected clinical contexts [62], as seen with early-phase clinical trials, predominantly outside BCa, that show higher response rates when FMT is combined with dual-checkpoint blockade therapy (anti-PD-1 plus anti-CTLA-4) compared to ICI monotherapy [50].

Collectively, these findings support the concept of a gut–bladder axis, as systemic immune priming and inflammation produce spillover that spreads from the GM towards the urinary system; the GM influences the bladder tumor microenvironment and consequently the treatment response. This model supports the fact that GM diversity not only modulates systemic inflammatory tone but also governs the balance between immune activation and suppression, thereby critically influencing antitumor immunity and clinical responses to ICI therapy. Given the encouraging effects of FMT in enhancing treatment responses across other cancer types and the established gut–bladder axis linking the GM and UM, further investigation is needed to determine whether and how FMT could be integrated into BCa therapy.

These findings support the concept of a gut–bladder axis, whereby systemic immune priming and inflammatory spillover from the gut microbiome may influence the bladder tumor microenvironment and treatment response.

5. Microbiome and Treatment Response

5.1. Bacillus Calmette–Guérin (BCG) Therapy

Immune regulation plays a central role in BCa cancer progression and regression, as reflected by the unique use of BCG as an effective immunotherapy. Although the mechanisms underlying BCG efficacy are still being clarified, current evidence suggests that BCG induces a localized pro-inflammatory immune response by activating APC, enhancing pre-existing antitumor immunity, promoting T-cell infiltration and activating NK and CD4⁺ T-cells. These effects are partly mediated by BCG binding to fibronectin on urothelial cells, thereby facilitating immune activation and the attenuation of immune checkpoint signaling [60,63].

BCG therapy also transiently alters the UM towards a pro-inflammatory state that supports immune-mediated tumor control during treatment. Its effect appears short-lived, as Mycobacterium is no longer detectable in urine samples more than one week after intravesical installation [8].

Prior to therapy, BCa urine samples commonly show enrichment of Actinobacteriota, Bacteroidota, Proteobacteria and Firmicutes, with predominant genera including Actinomyces, Corynebacterium, Prevotella, Porphyromonas and Streptococcus, alongside reduced Faecalibacterium and Pseudomonas [15,30,34,41,55]. Tissue samples similarly demonstrate a predominance of Actinobacteriota [17]. Following BCG treatment, patients without recurrence exhibit enrichment of taxa such as Bacteroides, Corynebacterium, Methanobrevibacter, Lactobacillus, Mycobacterium, Pseudomonas and Streptococcus, whereas recurrence is associated with taxa such as Blautia, Faecalibacterium, Proteus, Ruminococcus, Staphylococcus, Tolumonas and Veillonella [17,18,29,37,40,41,42,59] (

Table 3. Studies linking urinary or gut microbiome alterations with bladder cancer responders (non-recurrence)/non-responders (recurrence).

Title	Publication Year	Sample Size	Male/Female Ratio	Mean Age	Sample Type	Method of Characterization	Predominant Bacteria				Reference
							Responders/Non-Recurrence		Non-Responders/Recurrence
							Abundance	Scarce	Abundance	Scarce
Investigating the association between the urinary microbiome and bladder cancer: An exploratory study	2021	N = 53	36/7	70	Urine	16S rRNA sequencing	Firmicutes Proteobacteria Brochothrix Serratia Negativicoccus Escherichia–Shigella Pseudomonas			Brochothrix Escherichia–Shigella Negativicoccus Serratia Pseudomonas	[38]
Blautia coccoides and its metabolic products enhance the efficacy of bladder cancer immunotherapy by promoting—CD8+ T-cell infiltration	2024	N = 50	41/9	60	Stool	16S rDNA sequencing	Bacteroides Blautia Faecalibacterium Lachnospirales Lachnospiraceae Ruminococcus torques Firmicutes Proteobacteria Bacteroidota	Actinobacteria Streptococcus Lactobacillus	Methanobrevibacter Lactobacillus_ mucosae Enterobacter	Fusobacteriota Ruminococcus torques	[34]
Impact of intravesical Bacillus Calmette-Guérin and chemotherapy on the bladder microbiome in patients with non-muscle-invasive bladder cancer	2023	N = 29	22/7	67.8	Urine	16S rRNA sequencing	Escherichia/Shigella Ureaplasma		Aerococcus		[37]
An exploratory study investigating the impact of the bladder tumor microbiome on Bacillus Calmette Guerin (BCG) response in non-muscle-invasive bladder cancer	2024	N = 11	Only male	72	Urine and Tissue	16S rRNA and shotgun metagenomics	Lactobacillus gasseri and johnsonii		Corynebacterium Streptococcus		[17]
Alterations in Urobiome in Patients with Bladder Cancer and Implications for Clinical Outcome: A Single-Institution Study	2020	N = 81	Only male	65	Urine and Tissue	16S rRNA (V4) amplicon sequencing	Corynebacterium Prevotella Staphylococcus Streptococcus			Lactobacillus	[18]
The role of urinary microbiota in primary and recurrent bladder cancer: insights from a propensity score matching study.	2025	N = 170	BCa-Primary 107/18 BCa-Recurrence 31/14	67	Urine	16S rRNA sequencing	Firmicutes Aeromonas Bradyrhizobium Cupriavidus Escherichia/Shigella Lactobacillus Ralstonia Veillonella	Bacteroidetes			[26]
Urinary microbiota changes among NMIBC patients during BCG therapy: comparing BCG responders and non-responders	2025	N = 12	Only male	72	Urine	16S rRNA sequencing	Actinobacteriota 9.2% Firmicutes 43.7% Proteobacteria 36.9%, Enterococcus 14.8% Lactobacillus 5.4% Serratia 15.8%			Actinobacteriota 9.8% Firmicutes 32.6% Proteobacteria 40.5% Enhydrobacter 6.9% Lactobacillus 6.5% Pseudomonas 5.4%	[39]
Differential urinary microbiome and its metabolic footprint in bladder cancer patients following BCG treatment	2024	N = 87	Benign only male BCa 46/12	73	Urine	16S rRNA sequencing	Klebsiella oxytoca Morganella morganii Salmonella enterica	Anoxybacillus kestanbolensis Bacillus flexus	did not exhibit a significant difference in microbial composition		[44]
PD42-04 Tumor microbiome associated with BCG response in non-muscle-invasive bladder cancer	2021		NR		Tissue	16S high-throughput sequencing	Corynebacterium Pseudomonas		Acinetobacter Lactobacillus Corynebacterium Pseudomonas Staphylococcus Mycobacterium bovis		[62]
PD12-01 Bladder tumor microbiome may augment response to BCG in non-muscle-invasive bladder cancer	2022	N = 47	NR		Tissue	16S next-generation sequencing (NGS) and shotgun metagenomics	Lactobacillus spp.		Corynebacterium spp.		[63]
Association of the commensal urinary microbiome with response to Bacillus Calmette–Guérin (BCG) immunotherapy in non-muscle-invasive bladder cancer.	2019	N = 31	22/9	69	Urine	16S rRNA sequence	Firmicutes Lactobacillale		Proteobacteria		[64]
Bladder cancer microbiome and its association with chemoresponse	2025	N = 76	62/24	70	Urine	16S rRNA sequence	E. Faecalis		Granulicatella Proteus		[33]
Stool microbiome signature associated with Rrsponse to neoadjuvant Pembrolizumab in patients with muscle-invasive bladder cancer	2023	N = 42	NR		Stool		Proteobacteria Sutterella		R. bromii		[65]
Gut microbiota Parabacteroides distasonis enhances the efficacy of immunotherapy for bladder cancer by activating antitumor immune responses	2024	N = 50	NR		Stool	16S rDNA sequencing	Lachnospira	Parabacteroides	Parabacteroides		[66]
Association of Bacteroides acidifaciens relative abundance with high-fiber diet-associated radio-sensitization	2020		NR		Stool	16S rRNA sequencing	Bacteroides acidifaciens Allobaculum Bacteroidaceae Flavobacterium Flavobacteriales Lactococcus Streptococcus Streptococcaceae Allobaculum Erysipelotrichales		Bifidobacterium Bidifobacteriaceae Bifidobacteriales Parabacteroides Porphyromonadaceae Lactobacillus Lactobacillaceae Lactobacillales		[67]
Role of Gut Microbiome in Neoadjuvant Chemotherapy Response in Urothelial Carcinoma: A Multi-institutional Prospective Cohort Evaluation	2024	N = 142	NR		Stool	16S rRNA sequencing	Proteus Fastidiosipila Tolumona Lachnospiraceae		Bacteroides Pseudomonas		[11]
Profiling the Urinary Microbiota in Male Patients with Bladder Cancer in China	2018	N = 49	Only male	60	Urine	16S rRNA sequencing			Herbaspirillum Gemella Bacteroides Porphyrobacter Faecalibacterium Aeromonas Herbaspirillum Porphyrobacter Bacteroides Marmoricola		[68]

NR: Not reported.

).

Comparative studies indicate higher α-diversity in BCa samples, which is associated with increased recurrence risk, likely reflecting a dysbiotic expansion of harmful organisms [18,43,55], such as Acinetobacter and Escherichia–Shigella, the latter of which is associated with reduced gemcitabine efficacy [36]. In contrast, healthy controls demonstrate higher abundance of Lactobacillus, whose production of lactic acid, bacteriocins, and antimicrobial metabolites, as well as competition for adhesion sites and nutrients, may inhibit pathogenic bacterial growth and carcinogenesis [31,64].

As an exogenous bacterium introduced into a pre-existing microbial ecosystem, BCG efficacy likely depends on interactions with resident UM taxa [34]. Bacteria associated with an improved BCG response include Blautia Coccoides, which has been shown to enhance CD8⁺ T-cell-mediated tumor killing while reducing pro-inflammatory cytokines [37], and Lactobacillus species, which increase BCG internalization by urothelial cancer cells through fibronectin binding [17,60] (Figure 2). In combination with Pseudomonas and Bacillus, Lactobacillus has also been linked to the activation of antioxidant and anticancer pathways (e.g., LPS biosynthesis pathway) [55].

Conversely, reduced abundance of Pseudomonas fluorescens and Pseudomonas putida has been associated with impaired activation of DNA-sensing pathways, antigen presentation, and leukocyte transendothelial migration, as well as the dysregulation of cytokine signaling and Wingless-related integration site/Beta-catenin (WNT/β-catenin) pathways, potentially facilitating immune evasion and disease progression from NMIBC to MIBC [38].

Probiotic interventions, particularly involving Lactobacillus strains, have demonstrated antitumor effects in preclinical models through enhanced neutrophil chemotaxis, immune modulation by stimulating CD8⁺ T and NK cell recruitment, and the facilitation of macrophage infiltration into the bladder mucosa, thereby promoting the release of antitumor cytokines such as Interferon-gamma (IFN-γ) and TNF-α within the bladder microenvironment [57]. Additional taxa, such as Clostridium butyricum MIYARI 588, have shown antitumor activity by promoting the secretion of mediators including TNF-related apoptosis-inducing ligand (TRAIL) and matrix metalloproteinase-8 (MMP-8), suppressing angiogenesis, and reducing metastasis-associated cytokines [52].

Collectively, these findings suggest that the modulation of the urinary microbiome through selective enrichment or depletion of specific taxa may represent a promising strategy to enhance BCG efficacy and improve therapeutic outcomes in bladder cancer.

5.2. Immune Checkpoint Inhibitors

ICI therapy for BCa, particularly targeting the PD-1/PD-L1 axis, is increasingly influenced by GM composition, which correlates with treatment responsiveness. Mechanistically, PD-1/PD-L1 blockade enhances dendritic cell (DC) priming and T-cell activation by relieving PD-L1-mediated inhibition of costimulatory signaling on peripheral and tumor-associated DCs, thereby improving antigen presentation and CD8⁺ T-cell cross-priming within the tumor microenvironment. GM-derived signals and metabolites further modulate DC maturation and shape the quality of antitumor T-cell responses [65,66].

Chronic microbial stimulation can induce sustained expression of inhibitory receptors such as PD-1 and PD-L1 on tumor-infiltrating lymphocytes, a context-dependent feature in BCa that may reflect immune evasion or pre-existing immune activation. While this regulatory pathway limits tissue damage during infection, in cancer, it facilitates immune evasion, providing a biological rationale for the use of ICI [4]. Therefore, ICI targeting PD-1, PD-L1, or CTLA-4 is used as second- or subsequent-line therapy for non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) to restore effective antitumor immunity [67,68].

Accumulating evidence indicates that GM diversity and specific taxa critically determine ICI efficacy [37]. The efficacy of anti-PD-L1 can be significantly enhanced by administering taxa such as Bifidobacterium [44,69] and Blautia coccoides, via its metabolite, trigonelline, which augments CD8⁺ T-cell cytotoxicity against BCa models by inhibiting β-catenin [37], as shown in preclinical murine models. Similarly, 3-indoleacetic acid (3-IAA), produced by Parabacteroides distasonis, is associated with improved BCa prognosis by inhibiting tumor cell migration and reducing fatty acid synthase and stearoyl-CoA desaturase expression [45]. In contrast, the enrichment of Veillonellaceae and Prevotellaceae correlates with poorer survival outcomes and elevated neutrophil-to-lymphocyte ratios, a marker of systemic inflammation linked to impaired immune regulation [39]. Patients enriched in Clostridiales, Ruminococcaceae, and Faecalibacterium exhibit increased circulating effector CD4⁺ and CD8⁺ T-cells and preserved cytokine responses following anti-PD-1 therapy. In contrast, the dominance of Bacteroidales correlates with elevated regulatory T-cells and myeloid-derived suppressor cells, reflecting an immunosuppressive systemic milieu [44].

6. Therapeutic Modulation of the Microbiome

Targeted modulation of the microbiome is an emerging strategy in BCa aimed to improve the efficacy of existing treatments, including ICI and BCG intravesical therapy. Approaches under investigation include FMT, probiotics, dietary interventions, antibiotic exposure, and, most recently, synbiotic and postbiotic strategies (

Table 4. Therapeutic modulation strategies for bladder cancer.

Intervention Type	Male/Female	Specific Agent/Strain	Target Mechanism	Observed Outcome	Reference
FMT	NA	Donor fecal microbiota	GM reshaping; enrichment of Bifidobacterium longum, Enterococcus faecium, Akkermansia muciniphila	Enhanced ICI response; increased gut microbial alpha diversity; reduced inflammation	[50]
FMT	NA	Donor fecal microbiota (with dual ICI)	Restore favorable microbial ecosystem	Higher response rates with anti-PD-1 plus anti-CTLA-4 vs. ICI monotherapy	[61]
FMT	NA	Autologous FMT	Post-antibiotic mucosal reconstitution	Improved gut microbiome recovery vs. probiotics	[70]
Probiotic	NA	Lactobacillus spp.	Enhanced neutrophilic chemotaxis; CD8+ T and NK cell recruitment; macrophage infiltration; IFN-γ and TNF-α release	Antitumor effects in preclinical models	[57]
Probiotic	NA	Lactobacillus spp.	Increased BCG internalization via fibronectin binding	Improved BCG efficacy	[60]
Probiotic	NA	Lactobacillus + Pseudomonas + Bacillus	Activation of LPS biosynthesis pathway; antioxidant and anticancer pathways	Enhanced BCG response	[51]
Probiotic	NA	Clostridium butyricum MIYARI 588	TRAIL and MMP-8 secretion; angiogenesis suppression; reduced metastasis-associated cytokines	Antitumor activity in preclinical models	[52]
Probiotic	NA	Bifidobacterium spp.	DC priming enhancement; T-cell activation modulation	Improved anti-PD-L1 efficacy in murine models	[51]
Probiotic	NA	Bacteroides fragilis	Partial reversal of antibiotic-induced immunosuppression	Restored anti-CTLA-4 response	[61]
Probiotic	NA	Blautia coccoides	Trigonelline production; β-catenin inhibition	Enhanced CD8+ T-cell cytotoxicity against BCa	[37]
Diet	NA	Fermented dairy (yogurt)	Lactobacillus delbrueckii and Streptococcus thermophilus delivery	Reduced BCa risk; anticarcinogenic and immune-enhancing effects	[53]
Diet	NA	High-fiber diet	Increased Lachnospiraceae and Bacteroides; SCFA production	HDAC inhibition; GPR43/GPR109A activation; NF-κB suppression; DC-driven Regulatory T-cell (Treg) differentiation; enhanced systemic immunity	[57]
Diet	NA	Western-style diet (high saturated fat, animal protein)	Increased bile acid secretion; secondary bile acid production	Tumor-promoting inflammation and carcinogenesis (negative outcome)	[61]
Antibiotic	NA	Prophylactic antibiotics (surgical)	Microbial depletion; mucosal integrity disruption	Reduced immunotherapy efficacy (negative outcome)	[70]
Antibiotic	NA	Concomitant antibiotics (with ICI)	GM disruption	Reduced anti-CTLA-4 antitumor response (negative outcome)	[70]
Antibiotic	NA	Renal-excreted antibiotics	UM modulation (systemic effects)	No significant UM alteration with short-term prophylaxis in one study	[36]
Antibiotic	NA	Antibiotic “reset” + probiotic	Microbiome depletion followed by targeted recolonization	Bacterial presence during supplementation; delayed return to homeostasis post-arrest	[70]
Synbiotic/Postbiotic	NA	Defined microbial metabolites or engineered consortia	Expand beneficial taxa (e.g., Blautia coccoides); reduce systemic inflammation	Potential for enhanced BCG/ICI efficacy; lower NLR; minimized live microbial administration risks	[39]
Postbiotic	NA	3-indoleacetic acid (3-IAA) from Parabacteroides distasonis	AhR-FASN axis modulation; fatty acid synthase and stearoyl-CoA desaturase inhibition	Inhibited tumor cell migration; improved BCa prognosis	[45]
Postbiotic	NA	Trigonelline (from Blautia coccoides)	β-catenin pathway inhibition	Augmented CD8+ T-cell cytotoxicity	[37]
Postbiotic	NA	SCFAs (butyrate, propionate, acetate)	HDAC inhibition; GPR43/GPR109A signaling; NF-κB suppression; Treg modulation	Tumor-suppressive effects; enhanced systemic immune activation	[57]

NA: Not available.

).

FMT has attracted interest for BCa patients due to its potential to modulate the GM and enhance antitumor immune responses, particularly in the context of ICI therapy. The current clinical consensus suggests that FMT is generally feasible in patients with cancer; however, robust efficacy data are lacking, and safety concerns persist [61]. Among the challenges foreseen are donor selection, standardization, dosing, route of administration, and patient identification, all of which limit the routine application of most methods due to the variability of fecal matter content and the patient microbiome [51,62].

Antibiotic exposure represents a double-edged intervention in microbiome modulation. While prophylactic antibiotics are necessary in surgical settings, they are associated with microbial depletion and impaired mucosal integrity, which may reduce the efficacy of immunotherapies [36,70]. Concomitant antibiotic use has been shown to reduce antitumor responses to anti-CTLA-4 therapy, an effect that is partially reversible with Bacteroides fragilis supplementation [61]. Attempts to use antibiotics to “reset” the microbial composition before specific probiotic administration to enhance the beneficial effects of probiotics have been relatively successful. On the one hand, while probiotic supplementation is administered, bacterial presence has been prevalent; however, subsequent to its cessation, return to baseline homeostasis has been delayed due to prolonged dysbiosis [70]. In BCa, prior prophylactic antibiotics did not significantly alter UM composition in one study, suggesting that systemic effects may depend on renal excretion of antibiotics and longer therapy duration [36].

Due to these controversial findings, probiotics and dietary interventions could offer more conservative means of microbiome immune modulation. Consumption of fermented dairy products has been associated with reduced BCa risk, incidence, and recurrence, likely mediated by Lactobacillus delbrueckii and Streptococcus thermophilus, which are commonly present in yogurt and other fermented foods and have demonstrated anticarcinogenic properties and immune-enhancing effects [53,71,72]. Dietary composition, especially those rich in fiber, increase the abundance of taxa such as Lachnospiraceae and Bacteroides and promote the production of SCFAs, which have been shown to exert tumor-suppressive effects through the inhibition of histone deacetylases (HDAC), the activation of G protein-coupled receptor 43/G protein-coupled receptor 109A (GPR43/GPR109A) signaling, the suppression of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) mediated inflammation, and the modulation of DC-driven regulatory T-cell differentiation, all of which has been shown to enhance systemic immune activation and influence bladder tumor outcomes [19,68]. In contrast, Western-style diets high in saturated fats and animal protein increase bile acid secretion and favor microbial production of secondary bile acids, which have been linked to tumor-promoting inflammation and carcinogenesis in murine models [61].

Despite these promising findings, safety concerns remain regarding the unregulated use of probiotics and broad-spectrum antibiotics, which may induce dysbiosis, antibiotic resistance, and paradoxically carcinogenic effects [61]. These limitations have shifted attention toward synbiotic and postbiotic approaches, which aim to deliver defined beneficial microbial substrates, strains, or metabolites in a controlled manner to modulate the immune system. Such strategies represent a promising future direction for precision microbiome modulation, with the potential to reduce systemic inflammation, as reflected by a lower neutrophil-to-lymphocyte ratio, and to enhance responsiveness to ICI therapy while minimizing risks associated with live microbial administration [39].

7. Translational and Clinical Implications

Cystoscopy with biopsy remains the foundation of BCa diagnosis and surveillance, despite its invasiveness and limited sensitivity for detecting diffuse or microscopic urothelial changes beyond visibly abnormal regions [67]. These limitations have driven interest in non-invasive biomarkers that capture broader biological alterations rather than focal lesions alone. In this context, the urinary microbiome has emerged as a candidate with potential diagnostic and prognostic relevance [12].

Across multiple independent cohorts, BCa has been associated with reproducible shifts in urinary microbial composition. Higher relative abundance of taxa such as Acinetobacter, Anaerococcus, and Sphingomonas has been reported in affected patients, supporting a disease-associated microbial pattern rather than study-specific variability [12,41]. These observations have been created into discrete microbial profiles, or “urinetypes,” with Prevotella- and Corynebacterium-dominant patterns consistently associated with higher disease risk. In discovery cohorts, machine learning models incorporating 12-genus signatures achieve area under the curve (AUC) approaching 90%, supporting the potential use of microbiome-based classifiers as adjuncts to existing diagnostic workflows rather than replacements for cystoscopy [12].

Urinary microbial features may also inform prognosis. Predictive models integrating microbiome data with clinical variables have linked increased representation of Lachnospiraceae family members to recurrence and progression following treatment. XGBoost-based approaches report accuracies exceeding 85% for non-invasive risk stratification, suggesting potential utility for microbiome-informed surveillance strategies in post-treatment settings [73].

Clinical translation, however, will require moving beyond microbiome profiling in isolation. Studies combining microbial data with host inflammatory markers demonstrate improved diagnostic performance, reflecting biologically relevant interactions between dysbiosis and immune activation [74]. Composite panels integrating microbial abundance with arachidonic acid metabolites and urinary IL-6 outperform microbiome- or metabolome-only approaches, indicating that downstream functional consequences of dysbiosis may be more clinically informative than taxonomic composition alone [74].

Role of UM in Treating BCG Non-Responsive NMIBC

This integrated framework is particularly relevant in NMIBC. Differences in pre-treatment UM have been observed between responders and non-responders to BCG, with Bifidobacterium species more frequently detected among patients who derive durable benefit from intravesical therapy [42,47]. If validated prospectively, microbiome profiling could enable earlier identification of patients unlikely to respond to BCG and support more timely treatment escalation. In this context, the microbiome can be conceptualized as a predictive and potentially modifiable biological system, analogous to tumor mutational burden in precision oncology [37].

Nadofaragene firadenovec-vncg is a non-replicating adenovirus-based gene therapy approved by the Food and Drug Administration (FDA) for the treatment of high-risk NMIBC refractory to BCG therapy. This is a vector-based gene therapy that delivers interferon alfa-2b to the urothelium, inducing a local pro-inflammatory response that leads to bladder tumor regression. Wang B et al., in their prospective study with 5-year follow-up, reported a complete response in more than half of BCG-non-responsive NMIBC cases at 3-month follow-up after intravesical Nadofaragene therapy [75].

ICI-like pembrolizomab has been utilized for the treatment of high-risk BCG non-responsive NMIBC. The KEYNOTE-057 study reported a 41% complete response rate at 3-month follow-up, with up to 46% of complete responders maintaining the response at 12-month follow-up [76].

8. Limitations of Urinary Microbiome Study

This review was designed as a narrative synthesis to integrate heterogeneous clinical, microbiological, and immunological evidence that is not yet amenable to formal meta-analysis in order to not risk over-interpretation given the variability in sampling methods, sequencing platforms and heterogeneity of UM results. The literature was identified through targeted searches of PubMed and Scopus using combinations of ‘urinary microbiome’, ‘gut microbiome’, and ‘bladder cancer’, with emphasis on human studies and mechanistic preclinical models published in the last decade.

Despite these advances, clinical implementation remains constrained by reproducibility. Urinary specimens are inherently low in microbial biomass, increasing susceptibility to contamination and batch effects that can obscure the true biological signal [24]. Compounding this issue, UM studies lack consensus regarding DNA preservation, storage conditions, and contamination controls, resulting in substantial inter-laboratory variability [24,77]. It is important to note that the majority of studies that describe the UM composition rely on 16S RNA sequencing analysis which, as previously mentioned, is not the ideal tool as it primarily characterizes the bacteriome and limits both species-level resolution and functional interference. Additionally, most available studies focus on NMIBC, particularly in the context of BCG therapy, limiting the extrapolation of microbiome associations to muscle-invasive disease and other treatment settings. This limits the generalizability of the clinical assumptions to be made regarding the effects of therapy in BCa progression; without a comparable number of patients with MIBC, results tend to be less trustworthy.

Biological interpretation also remains incomplete. Although associations between Fusobacterium and BCa progression have been reported, mechanistic validation using experimental models remains limited, making it difficult to distinguish causal drivers from secondary microbial changes [33,69].

Addressing these limitations will require coordinated efforts across urology, microbiology, immunology, and computational biology to standardize methodology, validate findings across independent cohorts, and translate associative signals into clinically actionable tools [24].

9. Future Directions

Despite increasing interest in microbiome-based biomarkers for BCa, none have yet achieved regulatory approval [31]. Future progress in UM research depends on moving beyond association-based observations toward mechanistic and causal inference. The achievement of this goal requires overcoming the current heterogeneity in urine sampling, processing, and analytical methodologies that limit cross-study comparability and prevent the establishment of a reproducible commensal UM, ultimately delaying clinical translation [23,24]. Encompassing patient characterization and background, sampling technique, storage conditions, and analytical pipelines (shotgun metagenomics, metabolomics, and functional validation in experimental models) will be essential to achieve standardization. The harmonization of the procedural aspects of research will enable the integration of the resulting microbial profiles with functional pathway profiles and metabolomic data [13,48,74], facilitating the link between microbiome-derived signals, immune modulation within the bladder microenvironment, and BCa pathogenesis, ultimately supporting the development of clinically actionable biomarkers and microbiome-guided interventions [23].

Mechanistic resolution also clarifies therapeutic priorities. Although FMT has demonstrated immunomodulatory potential, its clinical utility remains constrained by safety concerns, compositional variability, and lack of standardization [61]. In contrast, emerging evidence supports the development of precision synbiotics and postbiotics, including defined microbial metabolites or engineered consortia designed to expand beneficial taxa such as Blautia coccoides.

Given the emerging role of the urinary and gut microbiome in modulating immune responses and therapeutic efficacy, the widespread use of broad-spectrum antibiotics in bladder cancer patients warrants careful consideration. Judicious antibiotic use and dysbiosis in bladder cancer management may disrupt commensal microbial communities, impair host immune surveillance, and potentially reduce responsiveness to immunotherapeutic strategies, including intravesical BCG and ICI. Antibiotic-driven dysbiosis may compromise beneficial microbial functions, promote inflammatory states, and interfere with host immune priming, while simultaneously accelerating antimicrobial resistance, complicating the management of recurrent urinary tract infections in this population. In this context, antimicrobial stewardship should be regarded as an integral component of bladder cancer management, balancing infection control with the preservation of microbiome integrity and the minimization of antimicrobial resistance. Integrating microbiome-preserving strategies and rational antibiotic use into bladder cancer care may represent an important step toward more personalized and sustainable oncologic management.

These strategies aim to reduce systemic inflammation and enhance BCG efficacy without the risks associated with live microbial transfer [37,39]. Preclinical models demonstrating increased intratumoral CD8⁺ T-cell infiltration, elevated granzyme B expression, and reduced recruitment of myeloid-derived suppressor cells provide proof of the principle that rational microbiome engineering can modulate the bladder tumor microenvironment [37].

Taken together, these advances support the development of quantitative microbial risk scores integrating microbial abundance, diversity metrics, functional metagenomics, and metabolomic data. Such tools would enable microbiome-informed patient stratification alongside tumor biology, providing a more precise and biologically grounded framework for BCa management [45,73].

10. Conclusions

The UM and GM are increasingly being recognized as active participants in BCa pathophysiology rather than mere bystanders. Accumulating evidence is linking microbial dysbiosis not only to carcinogenesis but also to treatment response and clinical outcomes, all through interactions along the gut–bladder axis. Loss of protective taxa such as Lactobacillus, Bifidobacterium, and Ruminococcus, together with enrichment of pro-inflammatory organisms such as Enterobacteriaceae, Fusobacterium, and Prevotella, may promote a tumor-permissive environment characterized by chronic inflammation, epithelial barrier disruption, and impaired immune surveillance.

Beyond association, microbial signatures show reproducible relationships with diagnosis, recurrence, and therapeutic response. Machine learning models incorporating urinary microbiome features now achieve diagnostic accuracies approaching 90%, and specific taxa have been linked to response or resistance to BCG and ICI. These findings position the microbiome not only as a biomarker but as a biologically plausible target for intervention. Mechanistic studies implicating microbial metabolites, including SCFAs and immunomodulatory compounds such as trigonelline and indole derivatives, provide a functional basis for emerging strategies involving diet, probiotics, and more refined synbiotic or postbiotic approaches.

Despite these advances, clinical translation remains limited. Methodological heterogeneity in sampling, sequencing platforms, and analytical pipelines continues to impair reproducibility and has delayed regulatory adoption of microbiome-based diagnostics. Progress will require consensus-driven standardization of urine collection and storage, prospective multicenter validation, and a shift beyond associative 16S rRNA profiling toward functional metagenomic approaches that resolve strain-level and metabolic contributions. Ultimately, the most significant clinical impact comes from integration rather than isolation. Combining microbiome features with host inflammatory markers, tumor biology, and clinical variables may enable composite risk models that better capture disease behavior and therapeutic responsiveness. Achieving this will require coordinated collaboration across urology, microbiology, immunology, and computational biology to move the microbiome from an exploratory signal to a clinically actionable component of bladder cancer precision oncology.