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与自相关的长非编码RNAs作为预后生物标志物,并与前列腺癌中的瘤微环境相关
Lu-Yao Li1,2, Hao Zi1,2,3, Tong Deng1
1Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University Wuhan, Hubei, China.
American journal of cancer research
|March 8, 2024
概括
异常自促进前列腺癌 (PCa) 存活率. 使用与自相关的长非编码RNAs (au-lncRNAs) 的新预后模型准确预测PCa患者的结果和生存率.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 遗传学 遗传学 是一个
背景情况:
- 异常自与前列腺癌 (PCa) 细胞存活和增殖有关.
- 长非编码RNAs (lncRNAs) 是自网络中的关键调节者.
研究的目的:
- 开发和验证PCa的预后模型,使用与自相关的lncRNAs (au-lncRNAs) 作为生物标志物.
- 评估PCa.中的au-lncRNA预后模型的预测准确性和临床影响.
主要方法:
- 利用来自癌症基因组图谱和人类自数据库的数据来识别预后性au-lncRNAs.
- 使用多变量考克斯分析构建了一个风险评分模型,并通过校准,卡普兰-梅尔和ROC曲线进行验证.
- 进行基因组丰富分析 (GSEA) 探索生物功能和定量实时PCR (qRT-PCR) 进行表达验证.
主要成果:
- 确定了五个关键的au-lncRNAs (AC068580.6,AF131215.2,LINC00996,LINC01125,LINC01547) 在PCa中具有预后意义.
- 开发的风险评分模型有效地将患者分为低风险和高风险组,高风险患者的无病生存率较差.
- 实现了高预测准确度,AUC值分别为0.788,0.794和0.818,对于1年,3年和5年的生存时间. GSEA揭示了与瘤微环境因素的关联.
结论:
- 该au-lncRNA预后模型在预测PCa预后方面表现出显著的准确性.
- 该模型与瘤微环境组件的关联表明了潜在的治疗点.
- 这种基于au-lncRNA的模型对个性化PCa患者管理具有重大临床影响.
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