DPCDI:一种人工智能衍生指标,解释药物可用性编程细胞死亡在心力衰竭中的诊断,分层和治疗含义
Lili Zhang1, Yihao Zhu2, Yuan Fang1
1Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Frontiers in genetics
|January 30, 2026
概括
编程细胞死亡途径是心力衰竭 (HF) 的关键. 我们使用机器学习开发了一种可使用药物编程的细胞死亡指数 (DPCDI),以识别HF亚型和潜在的治疗点,以改善诊断和治疗.
科学领域:
- 心血管研究研究心血管研究
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 编程细胞死亡 (PCD) 途径为心力衰竭 (HF) 提供了潜在的治疗点.
- 目前对这些通路的理解和对高频的治疗利用仍然有限.
- 药物可用PCD途径对于推进HF诊断,预后和治疗策略至关重要.
研究的目的:
- 开发一种新型指数,即Druggable编程细胞死亡指数 (DPCDI),用于评估HF中的PCD途径.
- 根据PCD活动使用机器学习识别关键基因和HF亚型.
- 探索特定基因在HF病变发生过程中的作用,并确定潜在的治疗配体.
主要方法:
- 采用了结合拉索和随机森林算法的集成机器学习框架.
- 非负矩阵因子化 (NMF) 用于将HF患者分为亚型.
- 使用单细胞RNA测序,门德尔随机化,小鼠模型中的实验验证和分子对接.
主要成果:
- 确定了15个关键基因用于DPCDI构造,包括JAK2,STAT3和CXCL12.
- 肝炎患者被分为两种亚型,其中亚型2显示出亡和线粒的增加.
- 对较高的JAK2和STAT3的遗传倾向与较低的HF风险有关,而CXCL12增加了敏感性;分子对接确定了潜在的药物配体.
结论:
- DPCDI为了解和准高频PCD提供了一个强大的框架.
- DPCDI促进了HF诊断,风险分层和向治疗的开发.
- 这项研究强调了针对心力衰竭管理中的特定PCD途径和基因的治疗潜力.
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