综合转录组分析和组合机器学习构建了ccRCC的乙化同静态模型,并验证了关键基因GCNT4
Baohua Zhu1, Ziyang Mo1, Yi Bao2
1Department of Urology, The First Affiliated Hospital, Naval Medical University, Second Military Medical University, Shanghai, China.
Cancer cell international
|June 25, 2025
概括
这项研究确定了清细胞细胞癌 (ccRCC) 中关键的乙化相关基因,并开发了一种预后模型. 基因GCNT4显示出作为治疗点的潜力,抑制瘤生长和迁移.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 清细胞细胞癌 (ccRCC) 是一种普遍存在的尿路系统恶性瘤.
- 蛋白质乙化对于调节细胞过程和癌症途径至关重要.
- 这项研究通过乙化透视来研究ccRCC机制.
研究的目的:
- 探索与蛋白质乙化相关的ccRCC的潜在生物机制.
- 为了确定影响ccRCC预后的乙化调节基因.
- 构建一个ccRCC患者预测结果的预测模型.
主要方法:
- 利用来自TCGA,ICGC和GEO数据库的RNA-seq和单细胞RNA测序数据.
- 采用机器学习算法来分析与乙化相关的差异表达基因 (DEG) 并构建预后风险模型.
- 进行基因组丰富分析 (GSEA) 并评估免疫透相关性. 通过细胞实验验证了GCNT4的功能.
主要成果:
- 确定了84个与ccRCC预后相关的乙化调节关键基因.
- 一个LASSO+RSF模型准确地预测了患者的存活率,高风险组显示出更糟糕的结果.
- 高GCNT4表达与更好的预后相关,抑制癌细胞增殖和入侵,并可能调节O-GlcNAc修饰.
结论:
- 使用转录组分析和机器学习开发了一个ccRCC乙化稳态模型.
- 验证了GCNT4作为与改善生存相关的关键基因.
- 在调节O-GlcNAc修饰方面,GCNT4的作用为ccRCC提供了潜在的治疗策略.
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