非侵入性尿蛋白生物标志物用于毒症预后评估
Qingbo Zeng1,2, Nianqing Zhang2, Junjie Zeng1
1Intensive Care Unit, the 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, 330002, China.
Scientific reports
|July 12, 2025
概括
这项研究确定了新的尿蛋白生物标志物,包括SLC25A24,UBQLN1和CREB3L3,以预测败血症死亡风险. 一个开发的诺莫格拉姆模型为个性化治疗策略提供了准确的,个性化的风险评估.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 生物标志物发现发现
- 败血症研究 败血症研究
背景情况:
- 早期识别败血症死亡风险对于改善患者的治疗结果至关重要.
- 尿蛋白生物标志物为败血症预后提供了一种非侵入性方法.
- 目前对败血症死亡率的预测模型需要改进.
研究的目的:
- 确定尿路蛋白质生物标志物,以预测败血症患者的短期结果.
- 开发一种与败血症相关的死亡风险的预测模型.
- 调查与已识别的生物标志物相关的功能途径.
主要方法:
- 数据独立获取 (DIA) 蛋白质组应用于46名败血症患者 (14人死亡,32人幸存) 的尿样.
- 包括KEGG和GO在内的生物信息分析被用于探索蛋白质功能.
- 机器学习算法 (LASSO,随机森林) 和ROC曲线 (AUC>0.8) 用于生物标志物选择和模型开发.
主要成果:
- 总共有2570种蛋白质被确定,其中255种具有差异性表达 (146种上调,109种下调).
- 涉及的关键途径包括静血调节,actin细胞骨组织和Rap1信号传递.
- 结合SLC25A24,UBQLN1和CREB3L3的诺米图显示出预测败血症死亡率的高准确性.
结论:
- 新的尿蛋白生物标志物 (SLC25A24,UBQLN1,CREB3L3) 已被确定用于败血症预后.
- 开发了一种实用的名图,用于个性化预测败血症死亡风险.
- 这种工具可以帮助为败血症患者定制个性化治疗策略.
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