建立和分析人工神经网络诊断模型,用于冠状动脉疾病中与凝血相关的分子子组
Biwei Zheng1, Yujing Li2,3, Guoliang Xiong2
1Department of Cardiology, Dongguan Hospital of Integrated Chinese and Western Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Dongguan, China.
Frontiers in genetics
|March 18, 2024
概括
冠状动脉疾病 (CAD) 患者可以根据与凝血相关的基因表达被分为不同的分子子组. 使用关键基因识别这些子组可能使个人化CAD治疗成为可能.
科学领域:
- 心血管医学 心血管医学
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
背景情况:
- 冠状动脉疾病 (CAD) 是导致死亡的主要原因,异常凝血是显著的危险因素.
- 在CAD中凝血的精确分子机制仍然不完全理解.
- 研究与凝血相关的基因 (CRG) 提供了对CAD病变的洞察力.
研究的目的:
- 根据CRG表达模式,将CAD患者分为分子子组.
- 探索这些子组之间的分子和免疫学差异.
- 识别用于分组分类和潜在治疗点的关键基因.
主要方法:
- 使用CRG表达数据对352名CAD患者进行集群.
- 在已识别的子组中分析分子和免疫学变异.
- 使用随机森林 (RF) 和LASSO回归的特征基因识别.
- 人工神经网络 (ANN) 模型的开发和验证.
主要成果:
- 患有CAD的患者被分为两组CRG子组 (C1和C2).
- 小组C1显示在免疫相关途径的丰富;差异性基因与信号转导和能量代谢有关.
- 确定了10个特征差异表达的CRG (DE-CRG),构成了ANN诊断模型的基础.
结论:
- 在CAD患者群体中存在不同的分子子组,其特点是独特的基因表达特征.
- 一小组特征基因可以有效地识别这些子组.
- 这种分层为开发个人化CAD治疗策略提供了基础.
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