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基于Omics的策略开发出瘤生物标志物衍生的特征,以预测心力衰竭的发生和进展

Yihao Zhu¹, Jianrong Guo²

¹International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, People's Republic of China; Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, People's Republic of China.

International journal of cardiology

|August 11, 2025

概括

瘤生物标志物可以预测心力衰竭 (HF) 风险. 一个新的签名 (TBS) 识别了七种生物标志物,其中S100B显示了HF患者的治疗潜力.

科学领域:

生物医学研究的研究.
心血管医学心血管医学
在瘤学瘤学.

背景情况:

瘤生物标志物越来越多地与心力衰竭 (HF) 的病理生理学有关.
了解瘤生物标志物和HF之间的联系对于推进患者护理至关重要.

研究的目的:

探索瘤生物标志物在心力衰竭 (HF) 的发生和进展中的作用.
使用瘤生物标志物开发HF风险的预测模型.

主要方法:

使用集成机器学习框架,在11个独立的HF队列中开发出预测性瘤生物标志物衍生签名 (TBS).
路径丰富分析,门德尔随机化 (MR) 和分子对接被用来阐明机制和治疗点.
使用蛋白质组学数据集的外部验证评估了开发的签名的性能.

主要成果:

开发的TBS包括七种瘤生物标志物 (FLT1,CSF1,SPP1,ENO2,GOLM1,GPC3,S100B),在预测HF方面表现出高准确度和灵敏度.
关键的途径,如ECM受体相互作用和PI3K-AKT信号传递被确定为潜在的机制.
在S100B和HF之间确立了显著的因果关系,并确定了Quercetin作为针对S100B的潜在治疗剂.

结论:

瘤生物标志物衍生签名 (TBS) 显示出预测,指导风险和治疗心力衰竭 (HF) 患者的前景.
S100B成为一个关键的生物标志物和潜在的治疗点,用于管理HF.

关键词:

生物信息学是一种生物信息学.心脏衰竭是因为心脏衰竭.机器学习是机器学习.瘤生物标志物的生物标志物

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