Department of Urology, Beijing Chaoyang Hospital Affiliated Capital Medical University, 8 Gong Ti Nan Road, Chaoyang District, Beijing, 100020, China.
Department of Urology, Qilu Hospital of Shandong University, Wenhuaxi Road #107, Jinan, 250012, China.
BMC Cancer. 2024 Aug 19;24(1):1024. doi: 10.1186/s12885-024-12790-w.
In the past few decades, researchers have made promising progress, including the development of immune checkpoint inhibitors (ICIs) in the therapy of bladder cancer (BLCA). Existing studies mainly focus on single immune checkpoint inhibitors but lack relevant studies on the gene expression profiles of multiple immune checkpoints.
RNA-sequencing profiling data and clinical information of BLCA patients and normal human bladder samples were acquired from the Cancer Genome Atlas and Gene Expression Omnibus databases and analyzed to identify different expression profiles of immune checkpoint genes (ICGs) after consensus clustering analysis. Based on the 526 intersecting differentially expressed genes, the LASSO Cox regression analysis was utilized to construct the ICG signature.
According to the expression of ICGs, BLCA patients were divided into three subtypes with different phenotypic and mechanistic characteristics. Furthermore, the developed ICG signature were independent predictors of outcome in BLCA patients, and was correlated with the immune infiltration, the expression of ICGs and chemotherapeutic effect.
This study systematically and comprehensively analyzed the expression profile of immune checkpoint genes, and established the ICG signature to investigate the differences in ICGs expression and tumor immune microenvironment, which will help risk stratification and accelerate precision medicine. Finally, we identified KRT23 as the most critical model gene, and highlighted KRT23 as a potential target to enhance immunotherapy against BLCA.
在过去的几十年中,研究人员在膀胱癌(BLCA)的治疗中取得了令人瞩目的进展,包括免疫检查点抑制剂(ICIs)的开发。现有研究主要集中在单一免疫检查点抑制剂上,但缺乏关于多个免疫检查点基因表达谱的相关研究。
从癌症基因组图谱和基因表达综合数据库中获取 BLCA 患者和正常人类膀胱样本的 RNA 测序分析数据和临床信息,并进行一致性聚类分析,以鉴定免疫检查点基因(ICGs)的不同表达谱。基于 526 个相交的差异表达基因,利用 LASSO Cox 回归分析构建 ICG 特征。
根据 ICG 的表达,BLCA 患者被分为具有不同表型和机制特征的三个亚型。此外,所开发的 ICG 特征是 BLCA 患者预后的独立预测因子,与免疫浸润、ICGs 的表达和化疗效果相关。
本研究系统全面地分析了免疫检查点基因的表达谱,建立了 ICG 特征,以研究 ICGs 表达和肿瘤免疫微环境的差异,这将有助于风险分层并加速精准医学的发展。最后,我们确定 KRT23 为最关键的模型基因,并强调 KRT23 作为增强针对 BLCA 的免疫治疗的潜在靶标。