在缺血性中风中与cuproptosis相关的分子的识别和免疫学特征
Chunhua Liu1, Binbin Wu1, Yongjun Tao1
1Department of Rehabilitation Research, Lishui Hospital of Traditional Chinese Medicine Affiliated to the Zhejiang University of Chinese Medicine.
Neuroreport
|November 20, 2023
概括
这项研究确定了与cuproptosis相关的关键基因集群,并开发了一种高度准确的缺血性中风预测模型. 这些发现突出了理解中风的新型分子目标.
科学领域:
- 生物医学研究的研究.
- 基因组学就是基因组学.
- 计算生物学是一种计算生物学.
背景情况:
- 缺血性中风是全球死亡和残疾的主要原因.
- 新发现的一种编程细胞死亡形式,即cuproptosis,与各种疾病有关.
- 了解缺血性中风的分子机制对于开发有效的治疗方法至关重要.
研究的目的:
- 在缺血性中风中阐明与cuproptosis相关的分子.
- 根据这些集群开发和验证基于缺血性中风的预测模型.
- 确定缺血性中风病原和异质性的新生物标志物.
主要方法:
- 来自基因表达综合 (GEO) 数据库的转录和免疫学数据分析.
- 权重基因共同表达网络分析 (WGCNA) 来识别差异表达基因 (DEG).
- 机器学习模型 (随机森林,XGBoost) 用于使用名录,决策曲线分析和ROC曲线进行预测建模和验证.
主要成果:
- 在缺血性中风中确定了两个重要的cuproptosis相关的分子.
- 集群2 DEG与氨基酸代谢,免疫反应和细胞增殖有关.
- XGBoost模型显示出优异的预测性能 (AUC=0.923) 并得到外部验证 (AUC=0.921).
结论:
- cuproptosis在缺血性中风的发病过程中发挥着重要作用.
- 开发的XGBoost模型为预测缺血性中风提供了一个强大的工具.
- NFE2L2,NLRP3,GLS,LIPT1和MTF1被确定为潜在的关键预测因子,为中风异质性提供了新的见解.
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