Bladder cancer (BC) is the most common urologic malignancy, facing enormous diagnostic challenges. Urinary extracellular vesicles (EVs) are promising source for developing diagnostic markers for bladder cancer because of the direct contact between urine and bladder. This study pioneers urinary EV N-glycomics for bladder cancer diagnosis. We have generated a comprehensive N-glycome landscape of urinary EVs through high-throughput N-glycome analysis, identifying a total of 252 N-glycans from 333 individuals. In bladder cancer patients, urinary EVs exhibit decreased fucosylation and increased sialylation level. An Eight N-glycan diagnostic model demonstrates strong performance in both validation cohorts, achieving ROC AUC values of 0.88 and 0.86, respectively. Furthermore, this model successfully differentiates both non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) from healthy individuals, underscoring the model’s superiority. Moreover, urinary EVs N-glycoproteomic analysis reveals that the glycoproteins carrying cancer-associated N-glycan signatures are closely associated with immune activities. The N-glycome comparative analysis of EVs and their source cells indicate that the glycosylation profiles of EVs do not completely match the glycosylation backgrounds of their source cells. In summary, our study establishes urinary EV N-glycomics as a non-invasive BC screening tool and provide a framework for EV glycan biomarker discovery across cancers.

Background: Currently, the impact of previous, simultaneous, or subsequent bladder cancer on the prognosis of upper urinary tract urothelial carcinoma (UTUC) is controversial. We aimed to investigate the impact of previous, simultaneous or intravesical recurrence (IVR) bladder cancer on the prognosis of UTUC based on a large population-based cohort from the Surveillance, Epidemiology, and End Results (SEER) database. Methods: A total of 8,431 UTUC patients diagnosed from 2004 to 2018 met the inclusion criteria were identified based on the SEER database. We evaluated the impact of bladder cancer on the prognosis of UTUC by Kaplan-Meier method and propensity score matching (PSM). Results: In all, 6,831 patients only had UTUC (UTUC-only), 880 patients with previous or simultaneous bladder cancer (UTUC-Bca), 720 patients with IVR (UTUC-IVR). After adjusting baseline covariates that varied significantly among groups, we found UTUC-Bca cohort, regardless of tumor grade and stage, had poorer prognosis than UTUC-only cohort. In general, we demonstrated IVR had no significant impact on the prognosis of UTUC compared to PSM matched patients without IVR. However, subgroup analysis revealed that UTUC patients with subsequent MIBC recurrence or shorter interval (<20 months) between UTUC and IVR had worse prognosis compared with UTUC-only cohort. Conclusions: UTUC patients with previous or simultaneous bladder cancer, IVR with MIBC, and shorter interval between UTUC and IVR were significant predictor for worse prognosis. Thus, more stringent postoperative surveillance and active treatment strategies should be considered for UTUC patients with those risk factors.

While single-cell technologies have advanced bladder cancer research, the microenvironment of lymph node metastases and its role in progression remain uncl

Bladder cancer (BCa) is a common urinary malignancy with high recurrence rates in non-muscle invasive bladder cancer (NMIBC), posing significant clinical challenges. Emerging evidence links urinary microbiota to cancer progression; however, their role in BCa recurrence remains unclear. This study aimed to explore urinary microbiota differences between primary and recurrent BCa to identify potential microbiological markers and mechanisms associated with recurrence. Urine samples were collected from 170 BCa patients, including 125 with primary Bca(BCa_P) and 45 with recurrent BCa (BCa_R). All samples underwent 16 S rRNA gene sequencing, and clinical data were collected, including age, sex, body mass index (BMI), smoking history, pathological grade, and other biological characteristics. Propensity score matching (1:1 ratio, caliper = 0.02) minimized baseline differences, resulting in 39 matched pairs. Microbial diversity was analyzed using α and β diversity indices. Differential taxa were identified with Linear Discriminant Analysis Effect Size (LEfSe), and functional pathways were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). Alpha diversity was significantly higher in BCa_P than BCa_R, particularly in Chao1 indices. β diversity revealed distinct microbial structures (ADONIS, P = 0.004, R² = 0.025). At the phylum level, both BCa_P and BCa_R were dominated by Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria, with Firmicutes significantly higher and Bacteroidetes lower in BCa_R. At the genus level, BCa_P was enriched in Sphingomonas, Corynebacterium, Capnocytophaga, Massilia, and Aquabacterium, while BCa_R showed higher levels of Aeromonas, Cupriavidus, and Bradyrhizobium. Functional predictions revealed glucose metabolism and oxidative stress pathways enriched in BCa_R, while pollutant degradation and TCA cycle pathways were prominent in BCa_P. These findings reveal significant differences in urinary microbiota compositions and functional profiles between primary and recurrent BCa patients, with recurrent cases exhibiting reduced microbial diversity and enrichment of potentially pathogenic communities, highlighting their potential roles in tumor progression and recurrence. Registered with the Chinese Clinical Trial Registry (ChiCTR2300070969) on April 27, 2023.

Bladder cancer (BCa) is a prevalent and lethal malignancy of the urinary system. Recent evidence suggests a strong association between the urinary microbiota and the pathogenesis, progression, and prognosis of BCa. This study investigated the role of the urinary microbiota in BCa, aiming to develop a non-invasive diagnostic model based on microbial biomarkers. Additionally, we proposed a novel urine-based microbiota classification method to enhance diagnostic accuracy and guide treatment strategies. The study included a discovery cohort (104 BCa patients, 56 with Other Malignant Urological Cancer, 98 with benign urinary diseases, and 42 healthy controls) and a validation cohort (66 BCa patients, 5 with Other Malignant Urological Cancer, 51 with benign urinary diseases, and 22 healthy controls). The urinary microbiota composition was analyzed using 16 S rRNA gene sequencing to assess diversity, identify biomarkers, and construct a diagnostic model for BCa. Finally, clustering analysis was used to establish “Urinetypes”. BCa patients exhibited greater richness and diversity in their urinary microbiota, with significant differences in beta diversity observed across the groups. Genera such as Sphingomonas, Anaerococcus, Acinetobacter, Stenotrophomonas, Aeromonas, and Novosphingobium were more abundant in BCa patients, while Lactobacillus and Gardnerella were less abundant, suggesting their potential as biomarkers. PICRUSt analysis revealed significant enrichment in carbohydrate and nucleotide metabolism in BCa patients, reflecting the increased metabolic demands of cancer cells. A biomarker prediction model employing random forest analysis based on 12 microbial genera achieved high accuracy in the discovery cohort (AUC = 89.08%) and demonstrated robust performance in the validation cohort (AUC = 70.8%). To facilitate potential clinical application, we developed a “Patient Differentiation Index” (PDI), which maintained predictive efficiency in both the discovery cohort (AUC = 86.17%) and the validation cohort (AUC = 78%). Additionally, we identified distinct “Urinetypes”, including those dominated by Prevotella and Corynebacterium, which were more prevalent in BCa patients and might represent high-risk subtypes. This study characterizes the urinary microbiota of BCa patients and, for the first time, provides a reliable non-invasive diagnostic method based on urinary microbiota. The introduction of the innovative concept of “Urinetypes” and the identification of high-risk subtypes associated with BCa offer the potential for improved diagnostic and therapeutic strategies. This trial was registered on the Chinese Clinical Trial Registry (ChiCTR) with the registration number ChiCTR2300070969, registered on 27 April 2023, https://www.chictr.org.cn/ ChiCTR2300070969. The registration details are publicly accessible on ChiCTR for verification and reference.