在基于生物信息学和机器学习的缺血性中风中识别和验证与糖化相关的基因
Hui Zhang1, Yanan Ji1, Zhongquan Yi1
1Department of Central Laboratory, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224000, People's Republic of China.
Journal of molecular neuroscience : MN
|April 29, 2025
概括
这项研究确定了与缺血性中风 (IS) 相关的关键糖化相关基因 (GRGs). 这些已识别的基因显示出诊断IS和理解其免疫微环境的潜力.
科学领域:
- 生物化学 生物化学
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
背景情况:
- 缺血性中风 (IS) 是一种严重的神经疾病,治疗选择有限.
- 葡萄糖化越来越多地被认为是它在IS发生和预后中的作用.
- 关于IS中的糖基化转录组数据仍然很少.
研究的目的:
- 在缺血性中风中使用生物信息学全面探索与糖化相关的基因 (GRGs).
- 在IS中评估与这些GRG相关的免疫特征.
- 确定潜在的诊断生物标志物和IS的治疗点.
主要方法:
- 权重基因共同表达网络分析 (WGCNA).
- 不同表达式分析 (DEGs).
- 从五种糖化途径中识别GRGs.
- 机器学习算法 (LASSO,随机森林,SVM-RFE) 用于枢纽基因识别.
- 无监督的集群用于患者分层.
主要成果:
- 通过交叉WGCNA,GRGs和DEGs确定了20个候选GRG.
- 3个枢纽GRG (F5,PPP6C,UBE2J1) 通过机器学习精确确定.
- 一个经过验证的与糖化相关的基因诊断模型有效地将IS患者与健康对照区分开来.
- IS患者被分为三个群体,免疫细胞透的显著差异.
结论:
- 在IS中成功识别了枢纽GRG.
- 该研究阐明了在IS免疫微环境中的枢纽GRG的作用.
- 已识别的GRG和诊断模型有可能用于IS管理中的临床应用.
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