Suppr超能文献

铁死亡相关长非编码 RNA 在膀胱癌中的预测作用及其与免疫微环境和免疫治疗反应的关系。

Predictive role of ferroptosis-related long non-coding RNAs in bladder cancer and their association with immune microenvironment and immunotherapy response.

机构信息

Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, China.

Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, 9 DongDan Santiao, Beijing, 100730, China.

出版信息

World J Surg Oncol. 2022 Feb 24;20(1):47. doi: 10.1186/s12957-022-02514-4.

Abstract

BACKGROUND

We have previously reported that ferroptosis has an important role in bladder cancer development. In this study, we aimed to further explore the possible predictive ability of ferroptosis-related long non-coding RNAs (lncRNAs) in bladder cancer and their relation with immune microenvironment and immunotherapy response.

MATERIALS AND METHODS

The ferroptosis-related lncRNAs were identified by Pearson's correlation analysis. The predictive lncRNA signature was developed by univariate and multivariate regression analyses. Only the main effects of independent variables in multivariate analysis were included in this signature. The TCGA dataset was defined as the training cohort and GEO was the validation cohort in this study. All samples were grouped into a high- or low-risk group depending on risk signature. The prognostic role of lncRNA signature was explored through survival analysis and receiver operating characteristic curve (ROC) analysis in both TCGA and GEO cohorts. Additionally, the independent prognostic ability of the lncRNA signature was confirmed by multivariate independent analysis. Furthermore, the relationship between lncRNAs and immune microenvironment as well as immunotherapy response in bladder cancers was studied.

RESULTS

The Kaplan-Meier curves identified significantly poorer overall survival outcomes for high-risk groups in both TCGA (p < 0.001) and GEO (p < 0.001) cohorts. The area under the curve (AUC) during ROC analysis of 1, 3, and 5 years was 0.781 ± 0.046, 0.784 ± 0.027, and 0.817 ± 0.025, respectively, in the TCGA cohort and 0.665 ± 0.177, 0.719 ± 0.068, and 0.791 ± 0.055, respectively, in the GEO cohort. The multivariate independent analysis in TCGA cohort identified age (p = 0.003), stage (p < 0.001), and signature risk score (p < 0.001) as independent risk factors for overall survival. Furthermore, this study demonstrated a significant difference in infiltration levels of various immune cells between high- and low-risk groups. The high risk group tended to have a lower expression of proteins including PD1 (p < 0.01), PD-L1 (p < 0.01), CTLA-4 (p < 0.05), etc. corresponding to various immune checkpoints. Additionally, the immunotherapy trial confirmed that the high-risk group tended to have a poorer treatment response than the low-risk group (p < 0.001).

CONCLUSIONS

The ferroptosis-related lncRNAs exhibited a good predictive capacity for overall survival in bladder cancer. Additionally, they could be utilized to reveal tumour-immune microenvironment and immunotherapy responses.

摘要

背景

我们之前的研究表明,铁死亡在膀胱癌的发生发展中起着重要作用。本研究旨在进一步探讨膀胱癌铁死亡相关长链非编码 RNA(lncRNA)的预测能力及其与免疫微环境和免疫治疗反应的关系。

材料和方法

采用 Pearson 相关分析鉴定铁死亡相关 lncRNA。通过单因素和多因素回归分析建立预测性 lncRNA 特征。该特征仅包含多因素分析中独立变量的主要效应。本研究的 TCGA 数据集被定义为训练队列,GEO 为验证队列。根据风险特征,所有样本均分为高风险或低风险组。通过 TCGA 和 GEO 队列中的生存分析和受试者工作特征曲线(ROC)分析探讨 lncRNA 特征的预后作用。此外,通过多因素独立分析证实了 lncRNA 特征的独立预后能力。进一步研究了 lncRNA 与膀胱癌免疫微环境和免疫治疗反应的关系。

结果

在 TCGA(p < 0.001)和 GEO(p < 0.001)队列中,Kaplan-Meier 曲线均显示高风险组的总生存期明显较差。ROC 分析 1、3、5 年的 AUC 分别为 0.781 ± 0.046、0.784 ± 0.027 和 0.817 ± 0.025,在 TCGA 队列中为 0.665 ± 0.177、0.719 ± 0.068 和 0.791 ± 0.055,在 GEO 队列中。TCGA 队列的多因素独立分析确定年龄(p = 0.003)、分期(p < 0.001)和特征风险评分(p < 0.001)为总生存期的独立危险因素。此外,本研究表明高风险组和低风险组之间各种免疫细胞的浸润水平存在显著差异。高风险组倾向于表达较低水平的各种免疫检查点蛋白,包括 PD1(p < 0.01)、PD-L1(p < 0.01)、CTLA-4(p < 0.05)等。此外,免疫治疗试验证实高风险组的治疗反应明显低于低风险组(p < 0.001)。

结论

铁死亡相关 lncRNA 对膀胱癌患者的总生存期具有良好的预测能力。此外,它们可以用于揭示肿瘤免疫微环境和免疫治疗反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c475/8867683/45a4773ec5a2/12957_2022_2514_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验