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鉴定和验证肿瘤浸润淋巴细胞相关预后标志物,用于预测膀胱癌的预后和免疫治疗反应。

Identification and validation of tumor-infiltrating lymphocyte-related prognosis signature for predicting prognosis and immunotherapeutic response in bladder cancer.

机构信息

Department of Urology, Shenshan Medical Center, Memorial Hospital of Sun Yat-Sen University, Shanwei, Guangdong, People's Republic of China.

Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.

出版信息

BMC Bioinformatics. 2023 Mar 27;24(1):118. doi: 10.1186/s12859-023-05241-z.

Abstract

BACKGROUND

It has been discovered that tumor-infiltrating lymphocytes (TILs) are essential for the emergence of bladder cancer (BCa). This study aimed to research TIL-related genes (TILRGs) and create a gene model to predict BCa patients' overall survival.

METHODS

The RNA sequencing and clinical data were downloaded from the TGCA and GEO databases. Using Pearson correlation analysis, TILRGs were evaluated. Moreover, hub TILRGs were chosen using a comprehensive analysis. By dividing the TCGA-BCa patients into different clusters based on hub TILRGs, we were able to explore the immune landscape between different clusters.

RESULTS

Here, we constructed a model with five hub TILRGs and split all of the patients into two groups, each of which had a different prognosis and clinical characteristics, TME, immune cell infiltration, drug sensitivity, and immunotherapy responses. Better clinical results and greater immunotherapy sensitivity were seen in the low-risk group. Based on five hub TILRGs, unsupervised clustering analysis identify two molecular subtypes in BCa. The prognosis, clinical outcomes, and immune landscape differed in different subtypes.

CONCLUSIONS

The study identifies a new prediction signature based on genes connected to tumor-infiltrating lymphocytes, providing BCa patients with a new theoretical target.

摘要

背景

已经发现肿瘤浸润淋巴细胞(TILs)对于膀胱癌(BCa)的出现至关重要。本研究旨在研究 TIL 相关基因(TILRGs)并创建一个基因模型来预测 BCa 患者的总生存率。

方法

从 TGCA 和 GEO 数据库下载 RNA 测序和临床数据。使用 Pearson 相关分析评估 TILRGs。此外,通过综合分析选择了枢纽 TILRGs。根据枢纽 TILRGs 将 TCGA-BCa 患者分为不同的簇,我们能够探索不同簇之间的免疫景观。

结果

在这里,我们构建了一个包含五个枢纽 TILRGs 的模型,并将所有患者分为两组,每组的预后和临床特征、TME、免疫细胞浸润、药物敏感性和免疫治疗反应都不同。低风险组的临床结果更好,免疫治疗敏感性更高。基于五个枢纽 TILRGs,非监督聚类分析在 BCa 中识别出两个分子亚型。不同亚型的预后、临床结局和免疫景观不同。

结论

该研究基于与肿瘤浸润淋巴细胞相关的基因确定了一个新的预测特征,为 BCa 患者提供了一个新的理论靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e80/10041757/2b14d65be2b4/12859_2023_5241_Fig1_HTML.jpg

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