综合的多omics分析和心力衰竭的预测建模使用败血症相关的基因签名
Yiping Lang1, Tianyu Liang2, Fei Li1
1Department of Nursing, Emergency and Critical Care Center, Intensive Care Unit, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
PloS one
|June 17, 2025
概括
这项研究使用与败血症相关的基因特征开发了心力衰竭 (HF) 的预测模型. 该模型准确预测HF风险,为早期诊断和改善临床管理提供了潜力.
科学领域:
- 心血管生物学 心血管生物学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 心力衰竭 (HF) 涉及复杂的分子变化.
- 与败血症相关的基因可能会导致心血管功能障碍.
研究的目的:
- 使用与败血症相关的基因特征开发心力衰竭 (HF) 的预测模型.
- 为了确定关键的败血症相关基因用于HF预测.
主要方法:
- 分析了三个与败血症相关的基因表达数据集 (GSE65682,GSE54514,GSE95233) 进行批量效应校正.
- 拉索-考克斯回归的应用,以确定显著的基因特征.
- 在独立的高频数据集上对预测模型的验证 (GSE57345,GSE141910,GSE5406).
- 使用CIBERSORT,IPS和xCell进行免疫透的评估.
主要成果:
- 鉴定了340个高调和333个低调的败血症相关基因.
- 一个六基因预测模型实现了HF预测的AUC>0.9,优于单个基因.
- 在HF患者和对照人群之间观察到免疫透概况的显著差异.
- 在小鼠败血症模型中的验证证实了六个关键基因的表达模式.
结论:
- 基于与败血症相关的基因的预测模型为预测心力衰竭提供了有价值的工具.
- 鉴定到的基因特征可以作为早期HF诊断和风险分层的潜在生物标志物.
- 这种方法有望改善心力衰竭的临床管理和预防策略.
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