使用机器学习识别和实验验证性结肠炎中与氧化压力相关的生物标志物
Siwei Duan1,2, Qincheng Yi3, Min Qiu2
1Gastroenterology Ward Department, the Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, People's Republic of China.
Journal of inflammation research
|August 27, 2025
概括
使用生物信息学和小鼠模型,确定了6种性结肠炎 (UC) 的氧化应激生物标志物. 这些发现为UC管理提供了新的诊断和治疗策略.
科学领域:
- 生物医学研究
- 基因组学
- 生物信息学
背景情况:
- 由于缺乏可靠的生物标志物,性结肠炎的诊断和治疗受到阻碍.
- 氧化应激在UC病变中起着重要作用.
研究的目的:
- 确定性结肠炎 (UC) 的新型氧化应激相关生物标志物.
- 通过生物信息学和实验模型验证这些生物标志物.
- 探索它们在UC诊断和治疗方面的潜力.
主要方法:
- 多个基因表达数据集 (GEO) 和氧化应激基因集的分析.
- 应用机器学习算法,包括LASSO回归和随机森林用于生物标志物识别.
- 一个人工神经网络 (ANN) 诊断模型的构建.
- 在使用qPCR和Western blot的3%硫酸 (DSS) 诱导的大肠炎小鼠模型中进行实验验证.
主要成果:
- 在UC中与氧化应激相关的六个枢纽基因 (DUOX2,ETFDH,GPX8,ITGA5,NPY,PDK2) 被识别并通过数据集验证.
- 在DSS结肠炎模型中,DUOX2和ITGA5被调高,而ETFDH,PDK2和NPY被调低.
- 在小鼠模型的结肠粘膜中,GPX8蛋白表达升高.
结论:
- 这项研究成功地确定了六种与氧化应激相关的UC生物标志物.
- 这些生物标志物是治疗性结肠炎的潜在诊断和治疗点.
- 需要进一步的临床研究来将这些发现转化为患者护理.
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