Immune landscape of distinct subtypes in urothelial carcinoma based on immune gene profile.

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The present study aimed to classify distinct immune subtypes and investigate the tumor microenvironment (TME) of urothelial carcinoma, which may help to understand treatment failure and improve the immunotherapy response. RNA-seq data and clinical parameters were obtained from TCGA-BLCA, E-MTAB-4321, and IMVigor210 datasets. A consensus cluster method was used to distinguish different immune subtypes of patients. Infiltrating immune cells, TME signatures, immune checkpoints, and immunogenic cell death modulators were evaluated in distinct immune subtypes. Dimension reduction analysis was performed to visualize the immune status of urothelial carcinoma based on graph learning. Weighted gene co-expression network analysis (WGCNA) was performed to obtain hub genes to predict responses after immunotherapy. Patients with urothelial carcinoma were classified into four distinct immune subtypes (C1, C2, C3 and C4) with various types of molecular expression, immune cell infiltration, and clinical characteristics. Patients with the C3 immune subtype displayed abundant immune cell infiltrations in the tumor microenvironment and were typically identified as \"hot\" tumor phenotypes, whereas those with the C4 immune subtype with few immune cell infiltrations were identified as \"cold\" tumor phenotypes. The immune-related and metastasis-related signaling pathways were enriched in the C3 subtype compared to the C4 subtype. In addition, tumor mutation burden, inhibitory immune checkpoints, and immunogenic cell death modulators were highly expressed in the C3 subtype. Furthermore, patients with the C4 subtype had a better probability of overall survival than patients with the C3 subtype in TCGA-BLCA and E-MTAB-4321 cohorts. Patients with the C1 subtype had the best prognosis when undergoing anti-PD-L1 antibody treatment. Finally, the immune landscape of urothelial carcinoma showed the immune status in each patient, and TGFB3 was identified as a potential biomarker for the prediction of immunotherapy resistance after anti-PD-L1 monoclonal antibody treatment. 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