文章：

膀胱癌患者免疫基因相关预后特征的鉴定

Identification of an immune gene-associated prognostic signature in patients with bladder cancer 

原文发布日期：2022-02-15 

英文摘要：

A deeper understanding of the interaction between tumor cell and the immune microenvironment in bladder cancer may help select predictive and prognostic biomarkers. The current study aims to construct a prognostic signature for bladder cancer by analysis of molecular characteristics, as well as tumor-immune interactions. RNA-sequencing and clinical information from bladder cancer patients were downloaded from the TCGA database. The single sample Gene Sets Enrichment Analysis (ssGSEA) and Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were employed to separate the samples into two clusters. Lasso Cox regression was performed to construct an immune gene signature for bladder cancer. The correlation between key target genes of immune checkpoint blockade and the prognostic signature was also analyzed. Dataset from Gene Expression Omnibus (GEO) was retrieved for validation. Two immunophenotypes and immunological characteristics were identified, and a 17-immune gene signature was constructed to provide an independent prognostic signature for bladder cancer. The signature was verified through external validation and correlated with genomic characteristics and clinicopathologic features. Finally, a nomogram was generated from the clinical characteristics and immune signature. Our study reveals a tumor-immune microenvironment signature useful for prognosis in bladder cancer. The results provide information on the potential development of treatment strategies for bladder cancer patients. Prospective studies are warranted to validate the prognostic capability of this model, but these data highlight the role of the microenvironment in the clinical outcome of patients. 

摘要翻译： 

对膀胱癌中肿瘤细胞与免疫微环境相互作用的深入理解可能有助于筛选预测性和预后性生物标志物。本研究旨在通过分析分子特征及肿瘤-免疫相互作用，构建膀胱癌的预后特征模型。研究从TCGA数据库下载膀胱癌患者的RNA测序数据和临床信息，采用单样本基因集富集分析（ssGSEA）和基于RNA转录本相对亚群估计的细胞类型鉴定法（CIBERSORT）将样本分为两个聚类。通过Lasso Cox回归构建膀胱癌免疫基因特征模型，并分析免疫检查点阻断关键靶基因与预后特征的相关性。从基因表达综合数据库（GEO）获取数据集进行验证。研究识别出两种免疫表型和免疫特征，构建包含17个免疫基因的特征模型作为膀胱癌的独立预后指标，该模型经过外部验证证实与基因组特征和临床病理特征相关。最终结合临床特征和免疫特征构建列线图。本研究揭示的肿瘤-免疫微环境特征对膀胱癌预后具有应用价值，研究结果为膀胱癌患者治疗策略的潜在发展提供信息。虽然需要前瞻性研究验证该模型的预后能力，但这些数据突显了微环境在患者临床结局中的重要作用。

原文链接：

Identification of an immune gene-associated prognostic signature in patients with bladder cancer