综合性转录和生物信息学方法与变压器模型相结合,识别了动脉样硬化中的关键基因网络
Dongdong Yan1, Wenqiang Li2, Erkun Xing1
1Department of Cardiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
概括
这项研究使用深度学习和生物信息学确定TYROBP和TNF作为动脉样硬化进展的关键基因. 这些发现突出了免疫炎症轴和动脉样硬化潜在的治疗点.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 免疫学 免疫学 免疫学
背景情况:
- 动脉样硬化是一种复杂的炎症性疾病.
- 确定动脉样硬化进展的关键分子驱动因素对于开发有效疗法至关重要.
研究的目的:
- 在人类动脉样硬化进展中优先考虑候选转录基因介质.
- 将深度学习与生物信息学和实验验证相结合.
主要方法:
- 使用limma进行差异基因表达分析.
- 基于变压器的深度学习 (TabTransformer) 用于疾病预测.
- 谢普利添加剂扩展 (SHAP) 对于基因的重要性.
- 网络分析 (STRING,Cytoscape) 和路径丰富 (GO,KEGG).
- 使用氧化LDL处理的内皮细胞进行实验验证.
主要成果:
- 确定了638个差异表达的基因.
- 在TabTransformer模型中,AUC达到0.964,超过了LASSO.
- 最重要的基因包括TYROBP,TNF,PTPRJ,DHRS9,COL1A1,与炎症和光滑肌肉功能障碍有关.
- 在实验验证中,TYROBP和TNF显示出显著的上调.
结论:
- 动脉样硬化涉及一个免疫炎症轴抑制平滑肌肉的身份.
- TYROBP和TNF是有希望的候选生物标志物和动脉样硬化治疗点.
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