在肝细胞癌中对TP53/CTNNB1突变和微血管入侵的综合转录组和组织学分析
Ignacio Garach1, Nerea Hernandez1, Luis J Herrera1
1Department of Computer Engineering, Automation and Robotics, CITIC, University of Granada, 18071 Granada, Spain.
Genes
|February 27, 2026
概括
与TP53和CTNNB1突变和微血管入侵 (MVI) 相关的肝细胞癌 (HCC) 分子变化显示出不同的基因表达特征. 这些分子变化可以在常规组织学中检测到,支持综合形态分子风险评估.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 计算病理学计算病理学
背景情况:
- 肝细胞癌 (HCC) 呈现出显著的分子和组织病理异质性.
- 在TP53和CTNNB1的突变和微血管入侵 (MVI) 是HCC的关键预后指标.
- 这些分子变化与瘤形态之间的关系尚未得到充分理解.
研究的目的:
- 调查与TP53/CTNNB1突变和HCC中的MVI相关的转录组变化.
- 通过深度学习来确定这些分子定义状态是否反映在常规基因病理学中.
主要方法:
- 来自HCC样本的RNA测序数据的差异基因表达分析.
- 基于机器学习的特征选择以识别转录程序.
- 在全幻灯片图像上进行弱监督的深度学习,以推断组织学模式.
主要成果:
- 对于TP53突变,CTNNB1突变和MVI阳性HCC.发现了不同的基因表达特征.
- 这些配置文件涉及与扩散,新陈代谢和入侵相关的途径.
- 深度学习模型成功地从组织学中识别了与这些分子状态相关的形态模式.
结论:
- 一致的生物计划是HCC的关键风险决定因素的基础.
- 这些分子变化的表型效应在常规基因病理学中是可检测的.
- 综合性形态分子方法可以提高HCC风险评估.
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