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通过机器学习算法识别慢性阻塞性肺病中与端粒维护相关的生物标志物和调节机制

Haiyan Cao¹, Xiangjian Chu²

¹Department of Respiratory and Critical Care, Rugao People's Hospital, Nantong, 226500, Jiangsu, China.

Scientific reports

|July 22, 2025

概括

研究人员确定了五个关键生物标志物 (RMI1,RAD51,RAD52,SNRNP70,CHEK1),可以准确诊断慢性阻塞性肺病 (COPD). 这些发现为这种渐进的呼吸道疾病提供了新的诊断潜力.

科学领域:

肺部医学肺部医学
生物标志物发现发现
遗传学是一个遗传学.

背景情况:

慢性阻塞性肺病 (COPD) 是一种渐进的呼吸道疾病,在了解疾病机制和死亡率驱动因素方面存在重大未满足的需求.
目前COPD的管理策略解决了症状和恶化,但对疾病进展的知识存在差距.

研究的目的:

识别和验证可靠的生物标志物,以准确诊断COPD.
通过识别与已识别的生物标记物相互作用的小分子来探索潜在的治疗点.

主要方法:

采用多组学方法来确定一个由五个候选生物标志物组成的小组 (RMI1,RAD51,RAD52,SNRNP70,CHEK1).
使用培训和独立验证集与曲线下面面积 (AUC) 分析验证的生物标志物诊断准确性.
进行基因组丰富分析 (GSEA) 以确定相关的生物途径.
进行了分子对接研究,以确定针对CHEK1.1的潜在候选药物.
采用RT-qPCR来确认COPD患者样本中的差异基因表达.

主要成果:

一个由五个生物标志物 (RMI1,RAD51,RAD52,SNRNP70,CHEK1) 组成的小组在区分COPD与正常样本方面表现出高准确性 (AUC> 0.65训练,> 0.80验证).
GSEA揭示了诸如非酒精性脂肪肝病和Spliceosome等途径的丰富性.
分子对接确定了五种与CHEK1.1相互作用的小分子 (U-0126, KN-62, BX-912, LY-294,002, AZD-7762).
在COPD患者中,RT-qPCR证实了SNRNP70和RAD52的显著表达差异.

结论:

已确定的五基因生物标记面板为准确的COPD诊断提供了一个有前途的工具.
这些生物标志物和相关途径为COPD病原体提供了洞察力.
用已识别的小分子准CHEK1可能代表了COPD的未来治疗途径.

关键词:

慢性阻塞性肺病是一种慢性阻塞性肺病.诊断标记物诊断标记物诊断标记物机器学习是机器学习.端粒的维护端粒的维护

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