机器学习识别了膀癌的DNA修复相关风险模型,并功能性地将ZWINT描述为潜在的致癌因素
Shijie Liang1,2, Haodong Liu1,2, Qisheng Su1,2
1Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Translational andrology and urology
|March 11, 2026
概括
这项研究使用机器学习开发了与DNA修复相关的风险评分 (DRRS),以预测膀癌 (BCa) 患者的结果. DRRS和一个关键基因ZWINT显示出作为BCa.生物标志物和治疗点的潜力.
科学领域:
- 在瘤学瘤学.
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 膀癌 (BCa) 呈现出显著的分子和临床异质性,使预后和治疗决策复杂化.
- 正确的预后评估和治疗分层受到这种异质性的阻碍.
研究的目的:
- 开发一种基于机器学习的预后模型,利用与膀癌有关的DNA修复基因.
- 在这个模型中研究特定基因在BCa进展中的功能作用.
主要方法:
- 在BCa转录数据上使用101个机器学习算法构建了DNA修复相关风险评分 (DRRS).
- 评估了DRRS的预后价值,免疫相关性和治疗影响.
- 通过体外实验进行了ZWINT (ZW10交互性基因蛋白) 的功能性表征.
主要成果:
- DRRS成功地将BCa患者分为高风险和低风险组,具有不同的存活率,免疫微环境和治疗响应概况.
- ZWINT knockdown抑制了细胞的增殖,迁移和入侵,诱导了细胞亡,并导致G1细胞周期停止.
- ZWINT的敲除导致p65酸化的增加,这表明NF-κB通路的激活.
结论:
- 为BCa建立了一个机器学习衍生的DNA修复签名 (DRRS).
- 在BCa.中,ZWINT被功能验证为潜在的瘤驱动因素.
- DRRS和ZWINT代表了膀癌治疗分层的有希望的生物标志物和治疗点.
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