基于多芯片关节分析的炎症性肠病特征基因和机器预测模型的构建
Yan Chaosheng1,2, Sun Haowen2, Rao Jingjing1,2
1Department of Gastroenterology, Affiliated Hospital of Jiangnan University, 214122, Wuxi city, Jiangsu Province, China.
Journal of translational medicine
|August 20, 2025
概括
这项研究确定了四个主要的血液生物标志物 (LOC389023,DUOX2,LCN2,DEFA6) 用于使用机器学习早期检测炎症性肠病 (IBD),从而提高了诊断潜力.
科学领域:
- 生物标志物发现
- 计算生物学
- 免疫学
背景情况:
- 炎症性肠病 (IBD) 是一种慢性炎症性疾病,其病因不明,因此存在诊断上的挑战.
- 早期检测和外周血液生物标志物检测对于IBD治疗至关重要.
- 目前的IBD诊断缺乏确定的非侵入性标志物.
研究的目的:
- 使用机器学习技术开发IBD的诊断模型.
- 确定用于早期IBD检测的新型周围血液生物标志物.
- 探索与IBD生物标志物相关的免疫路径.
主要方法:
- 使用多芯片联合分析方法与机器学习算法 (LASSO,SVM,RF) 和人工神经网络 (ANN).
- 开发了一种结合SHAP模型的IBD诊断模型,用于特征解释.
- 对差异表达基因进行基因本体学 (GO) 和KEGG通路丰富分析.
- 检查了已识别的基因和免疫细胞群之间的关系.
主要成果:
- 确定了四个关键的IBD生物标志物:LOC389023,DUOX2,LCN2和DEFA6.
- 这些生物标志物与免疫系统调节和微生物改变有关.
- 丰富的途径包括抗菌和IL-17信号;M1巨细胞显示稳定的差异变化.
结论:
- 在IBD中,LOC389023,DUOX2,LCN2和DEFA6基因的差异表达与免疫效应有关.
- 与ANN相比,机器学习模型对IBD的诊断性能更好.
- 这些发现为临床IBD诊断,向治疗和预后评估提供了基础.
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