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mRNA‐based molecular subtypes have implications for bladder cancer prognosis and clinical benefit from certain therapies. Whether small extracellular vesicles (sEVs) can reflect bladder cancer molecular subtypes is unknown. We performed whole transcriptome RNA sequencing for formalin fixed paraffin embedded (FFPE) tumour tissues and sEVs separated from matched tissue explants, urine and plasma in patients with bladder cancer. sEVs were separated using size‐exclusion chromatography, and characterized by transmission electron microscopy, nano flow cytometry and western blots, respectively. High yield of sEVs were obtained using approximately 1 g of tissue, incubated with media for 30 min. FFPE tumour tissue and tumour tissue‐derived sEVs demonstrated good concordance in molecular subtype classification. All urinary sEVs were classified as luminal subtype, while all plasma sEVs were classified as Ba/Sq subtype, regardless of the molecular subtypes indicated by their matched FFPE tumour tissue. The comparison within urine sEVs, which may exclude the sample type specific background, could pick up the different biology between NMIBC and MIBC, as well as the signature genes related to molecular subtypes. Four candidate sEV‐related bladder cancer‐specific mRNA biomarkers, FAM71E2, OR4K5, FAM138F and KRTAP26‐1, were identified by analysing matched urine sEVs, tumour tissue derived sEVs, and adjacent normal tissue derived sEVs. Compared to sEVs separated from biofluids, tissue‐derived sEVs may reflect more tissue‐ or disease‐specific biological features. Urine sEVs are promising biomarkers to be used for liquid biopsy‐based molecular subtype classification, but the current algorithm needs to be modified/adjusted. Future work is needed to validate the four new bladder cancer‐specific biomarkers in large cohorts. [ABSTRACT FROM AUTHOR]

Background: Tumor‐infiltrating immune cells participate in the initiation and progression of prostate adenocarcinoma (PRAD). However, it is not fully known how immune infiltration affects the development of PRAD and its clinical presentation. Methods: Herein, we investigated the immune infiltration phenotypes in PRAD based on transcriptome profiles, methylation profiles, somatic mutation, and copy number variations. We also developed an immune prognostic model (IPM) to identify unfavorable prognosis. To verify this model, immunohistochemistry staining was performed on a cohort of PRAD samples. Moreover, we constructed a nomogram to assess the survival of PRAD incorporating immune infiltration and other clinical features. Results: We categorized PRAD patients into high and low‐level clusters based on immune infiltration phenotypes. The patients in the high‐level clusters had worse survival than their low‐level counterparts. Gene set enrichment analysis indicated that both anti‐ and pro‐tumor terms were enriched in high‐level cluster. Moreover, we identified a positive correlation between anti‐ and pro‐tumor immune cells in PRAD microenvironment. Notably, Somatic mutation analysis showed patients in high‐level cluster had a higher somatic mutation burden of KMT2D, HSPA8, CHD7, and MAP1A. In addition, we developed an IPM with robust predictive ability. The model can distinguish high‐risk PRAD patients with poor prognosis from low‐risk PRAD patients in both training and another three independent validation datasets. Besides, we constructed a nomogram incorporating Gleason score, pathological T stage, and IPM for the prognosis prediction of PRAD patients, which displayed robust predictive ability and might contribute to clinical practice. Conclusion: Our work illustrated the immune infiltration phenotypes strongly related to the poor prognosis of PRAD patients, and highlighted the potential of the IPM to identify unfavorable tumor features. [ABSTRACT FROM AUTHOR]