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在功能性消化不良中表征十二指肠免疫微环境:一种自动ML驱动的诊断框架

Xueping Zhang¹, Xingfu Fan², Xinxin Hu¹

¹Department of Gastroenterology, Beijing Key Laboratory of Functional Gastrointestinal Disorders Diagnosis and Treatment of Traditional Chinese Medicine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China.

Journal of inflammation research

|July 21, 2025

概括

这项研究确定了功能性消化不良 (FD) 的免疫相关生物标志物,这是一个常见的肠道疾病. 一个自动化的机器学习模型准确地诊断高风险的FD患者,提供新的治疗见解.

科学领域:

胃肠病学胃肠病学
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

功能性消化不良 (FD) 是一种普遍存在的胃肠关节疾病,其病因不明.
新出现的证据表明,十二指肠免疫激活与FD发展有关.

研究的目的:

为了确定功能性消化不良的免疫相关的诊断生物标志物.
使用自动机器学习开发FD的高性能诊断模型.
阐明FD病原体背后的生物机制.

主要方法:

门德尔的随机化和全基因组关联研究 (GWAS) 确定了与FD相关的基因.
对患者数据进行了差异基因表达分析和免疫透评估.
权重基因共同表达网络分析 (WGCNA) 和最大集团中心性 (MCC) 确定了关键基因.
自动机器学习 (AutoGluon) 和后勤回归开发了一个诊断模型.
基因本体学 (GO) /基因和基因组的京都百科全书 (KEGG) 和基因组丰富分析 (GSEA) 探索了生物途径.
用于验证的FD老鼠模型.

主要成果:

GWAS揭示了259个与FD相关的基因,富含免疫和炎症通路.
表达式分析显示免疫细胞种群发生变化,包括血细胞增加和调节性T细胞减少.
9个生物标志物显示出高的诊断性能 (AUC=0.94).
自动机器学习模型 (LightGBMLarge,XGBoost) 被确定为临床使用的最佳模型.
GSEA和GO/KEGG的分析将高风险的FD与免疫-炎症通路,细胞粘附,氧化应激和营养代谢联系起来.

结论:

这项研究提出了功能性消化不良的新型免疫相关诊断生物标志物.
这些发现提供了有关FD病原体和潜在治疗点的见解.
一个自动化的机器学习模型提供了高风险的FD患者的准确识别,并描述了生物变化.

关键词:

自动机器学习自动化机器学习生物信息学是一种生物信息学.诊断模型诊断模型诊断模型功能性消化不良症功能性消化不良症免疫透免疫透是什么?

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