Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, China.
Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, National Clinical Research Center for Metabolic Disease, Changsha, China.
Front Immunol. 2022 Aug 8;13:970885. doi: 10.3389/fimmu.2022.970885. eCollection 2022.
Immune checkpoint blockade (ICB) has become a promising therapy for multiple cancers. However, only a small proportion of patients display a limited antitumor response. 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. The present study provided a bioinformatics basis for understanding the immune landscape of the tumor microenvironment of urothelial carcinoma.
免疫检查点阻断（ICB）已成为多种癌症的一种有前途的治疗方法。然而，只有一小部分患者表现出有限的抗肿瘤反应。本研究旨在对膀胱癌进行不同免疫亚型分类，并研究肿瘤微环境（TME），这可能有助于了解治疗失败的原因并提高免疫治疗的反应。从 TCGA-BLCA、E-MTAB-4321 和 IMVigor210 数据集获得 RNA-seq 数据和临床参数。使用共识聚类方法区分不同免疫亚型的患者。评估不同免疫亚型中的浸润免疫细胞、TME 特征、免疫检查点和免疫原性细胞死亡调节剂。基于图学习进行降维分析，以可视化膀胱癌的免疫状态。进行加权基因共表达网络分析（WGCNA）以获得预测免疫治疗后反应的枢纽基因。将膀胱癌患者分为四种不同的免疫亚型（C1、C2、C3 和 C4），具有不同类型的分子表达、免疫细胞浸润和临床特征。C3 免疫亚型的患者在肿瘤微环境中有丰富的免疫细胞浸润，通常被鉴定为“热”肿瘤表型，而 C4 免疫亚型的患者免疫细胞浸润较少，被鉴定为“冷”肿瘤表型。与 C4 亚型相比，C3 亚型中富集了与免疫相关和转移相关的信号通路。此外，C3 亚型中高表达肿瘤突变负担、抑制性免疫检查点和免疫原性细胞死亡调节剂。此外，在 TCGA-BLCA 和 E-MTAB-4321 队列中，C4 亚型的患者比 C3 亚型的患者具有更好的总生存率。接受抗 PD-L1 抗体治疗的 C1 亚型患者的预后最佳。最后，膀胱癌的免疫图谱显示了每个患者的免疫状态，并且 TGFB3 被鉴定为预测抗 PD-L1 单克隆抗体治疗后免疫治疗耐药的潜在生物标志物。本研究为了解膀胱癌肿瘤微环境的免疫景观提供了生物信息学基础。
