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  1. 首页
  3. 通过全面的生物信息学分析,机器学习和动物实验,探索与多囊性卵巢综合征相关的潜在关联和生物标志物
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通过全面的生物信息学分析,机器学习和动物实验,探索与多囊性卵巢综合征相关的潜在关联和生物标志物

Xiaoxuan Zhao1,2, Yuanyuan Zhang3, Qingnan Fan3

  • 1Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.

Functional & integrative genomics
|August 30, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

多囊卵巢综合征 (PCOS) 与动脉样硬化 (AS) 有关. 这项研究确定了像CSF2RA和LILRA5这样的关键基因作为预测和理解PCOS相关AS进展的潜在生物标志物.

关键词:
动物实验动脉样硬化生物信息学分析化学药物免疫细胞机器学习多囊性卵巢综合征

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科学领域:

  • 内分泌学和心血管研究
  • 基因组学和生物信息学
  • 免疫学

背景情况:

  • 多囊卵巢综合征 (PCOS) 是生育年龄妇女常见的内分泌疾病.
  • PCOS与心血管疾病动脉样硬化 (AS) 的风险增加有关.
  • 了解PCOS和AS之间的分子联系对于早期检测和治疗至关重要.

研究的目的:

  • 探索PCOS和AS之间潜在的分子关联.
  • 为了确定与AS复杂的PCOS的潜在生物指标 (生物标志物).
  • 研究免疫细胞和相关分子在PCOS相关AS的发病过程中的作用.

主要方法:

  • 基因表达数据集 (PCOS和AS) 的分析,使用权重基因共表达网络分析 (WGCNA) 和蛋白与蛋白相互作用 (PPI) 网络.
  • 基因和基因组的京都百科全书 (KEGG) 路径丰富分析以了解生物过程.
  • 免疫透分析,化学因子/受体-免疫细胞网络的构建,以及用于生物标志物识别的机器学习算法 (GBM,XGBoost).
  • 在PCOS和AS小鼠模型和名录结构中验证发现.

主要成果:

  • 鉴定PCOS组织和AS动脉壁中的差异表达基因 (DEG),突出显示免疫炎症,脂质代谢和胰岛素信号通路.
  • 在AS样本中增加了原始B细胞,毛囊T辅助细胞和中性粒细胞的比例.
  • 六个关键的化学激素 (CCL5,CCL20,CCL23,CCL28,CXCL1,CXCL6) 和它们的受体参与了免疫细胞的招募.
  • 机器学习发现了五个特征基因 (LILRA5,CSF2RA,S100A8,CD6,CCL24) 具有高预测准确性,用于PCOS相关的AS.
  • 在AS和PCOS小鼠模型中验证了CSF2RA和LILRA5,显示出改变的表达模式.

    • 结论:

    • 这项研究揭示了PCOS和AS之间的显著分子关联,特别是涉及免疫失调和代谢途径.
    • CSF2RA和LILRA5成为预测和分层PCOS相关AS风险的有希望的生物标志物.
    • 这些发现为进一步研究PCOS相关AS的诊断和治疗策略提供了基础.