机器学习和生物信息学用于识别血相关脏损伤中的凝血生物标志物
Ling Liu1, Tiancong Zhang2, Liman Li2
1Department of Ophthalmology, and Research Laboratory of Macular Disease, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Shock (Augusta, Ga.)
|April 10, 2025
概括
这项研究确定了C3,F3,Fgg和Serping1作为败血症相关急性损伤 (SA-AKI) 的新生物标志物. 这些凝血免疫标志物显示出诊断效用,并为早期SA-AKI检测提供了一个框架.
科学领域:
- 生物化学 生物化学
- 免疫学 免疫学 免疫学
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 败血症相关的急性损伤 (SA-AKI) 死亡率高,分子驱动因素不明.
- 失调的凝血和免疫交叉涉及SA-AKI,但缺少脏特定的生物标志物.
研究的目的:
- 确定SA-AKI的新型凝血相关基因和生物标志物.
- 在临床环境中研究这些生物标志物的诊断效用.
主要方法:
- 生物信息学和机器学习整合了小鼠转录基因数据,以识别差异表达的凝血相关基因 (DE-CRGs).
- 在小鼠模型中使用外部数据集,qRT-PCR和免疫组织化学验证了Hub基因.
- 分析了免疫透和检查点相关性,并在临床队列中评估了诊断性能.
主要成果:
- 四个枢纽DE-CRG (C3,F3,Fgg,Serping1) 在小鼠SA-AKI中得到了持续的上调.
- 证实F3蛋白表达,免疫分析显示T细胞/NK细胞透和PD-L1与枢纽基因共同表达.
- 一个多标记面板 (纤维素,TAT,C3) 实现了SA-AKI诊断的AUC为0.853.
结论:
- C3,F3,Fgg和Serping1被确定为SA-AKI的潜在的新型凝血免疫生物标志物.
- 这些生物标志物证明了经过验证的诊断效用.
- 这些发现弥合了SA-AKI病原体的知识差距,并为早期检测提供了翻译框架.
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