动脉瘤学观点:使用机器学习识别动脉样硬化诊断的枢纽基因
Liyan Zhao1,2,3, Xuzhen Lv2, Wen Chen4
1School of Basic Medicine, Ningxia Medical University, Yinchuan, China.
Frontiers in immunology
|November 20, 2025
概括
这项研究在动脉样硬化中确定了四个与癌症相关的枢纽基因 (IRF7,FHOD1,TNF,ZSWIM3),开发了早期检测的诊断模型. 这些发现突出了光滑肌肉细胞的转化和免疫调节,为这种复杂的疾病提供了潜在的治疗途径.
科学领域:
- 心血管生物学 心血管生物学
- 癌症生物学 癌症生物学
- 生物信息学是一种生物信息学.
背景情况:
- 动脉样硬化涉及光滑肌肉细胞 (SMC) 的表型转变,与癌症共享瘤途径.
- 将DNA损伤和SMC中的瘤性途径与动脉样硬化联系起来的精确分子机制尚不清楚.
研究的目的:
- 用生物信息学整合癌症基因组,以确定动脉样硬化中的关键枢纽基因.
- 探索与这些枢纽基因相关的免疫分子机制.
- 开发一种基于已识别的基因的早期动脉样硬化检测诊断模型.
主要方法:
- 在GEO数据集上的差异基因表达分析 (Limma,WGCNA).
- 机器学习算法 (SVM-RFE,LASSO,随机森林) 用于枢纽基因识别.
- 在人类和小鼠模型中进行单细胞RNA测序和验证.
主要成果:
- 确定了四个与癌症相关的枢纽基因:IRF7,FHOD1,TNF和ZSWIM3.
- 使用高精度的IRF7,FHOD1和TNF开发了一个诊断名录.
- 观察到改变的免疫微环境和SMC衍生的表达IRF7和FHOD1.1的巨类细胞.
结论:
- 确定了与动脉样硬化和癌症相关的关键枢纽基因.
- 建立了一个可靠的诊断工具,用于早期检测动脉样硬化.
- 对SMC表型切换和免疫调节的洞察力提供了潜在的治疗点.
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