Machine Learning and Mendelian Randomization Identify Six Cell Death-Related Genes Driving Ulcerative Colitis Progression and Treatment Response.

IF 4.1 2区医学 Q2 IMMUNOLOGY

Journal of Inflammation Research Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI:10.2147/JIR.S536145

Longfei Dai, Weiguo Zhou, Along Li, Xinjian Xu, Bin Yuan, Zhen Zhang

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引用次数: 0

Abstract

Background: The pathogenesis of ulcerative colitis (UC) is thought to involve abnormal regulation of cell death. However, key cell death-related genes (CDGs) that drive disease progression have not been fully characterized. The identification of these CDGs is thought to potentially reveal new therapeutic targets.

Methods: Machine learning (ML) and Mendelian randomization (MR) methods were integrated to identify CDGs with causal effects in UC progression. The validation included immune-related analysis, drug response assessment (infliximab/vedolizumab/golimumab), patient stratification based on consensus clustering, and functional validation.

Results: Six key CDG genes (VNN1, PTGDS, MMP9, IL13RA2, S100A8, and IL1B) were identified by ML. VNN1 and MMP9 were confirmed by MR to be pathogenic risk factors for UC progression. All six genes were significantly associated with immune cell infiltration, pro-inflammatory cytokines, and intestinal barrier dysfunction. Compared with non-responders, the expression of these six CDGs was significantly downregulated in biologic therapy responders. Based on these genes, patients with UC were classified into two groups: the C1 group with severe disease activity and the C2 group with reduced Mayo scores and enhanced treatment sensitivity. Additionally, knocking down VNN1 functionally alleviated intestinal inflammation.

Conclusion: These six genes can be used to assess the severity of UC and predict treatment outcomes.

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机器学习和孟德尔随机化鉴定六个驱动溃疡性结肠炎进展和治疗反应的细胞死亡相关基因。

背景：溃疡性结肠炎（UC）的发病机制被认为涉及细胞死亡的异常调节。然而，驱动疾病进展的关键细胞死亡相关基因（CDGs）尚未得到充分表征。这些CDGs的鉴定被认为可能揭示新的治疗靶点。方法：结合机器学习（ML）和孟德尔随机化（MR）方法来识别在UC进展中具有因果影响的CDGs。验证包括免疫相关分析、药物反应评估（英夫利昔单抗/vedolizumab/golimumab）、基于共识聚类的患者分层和功能验证。结果：ML鉴定出6个关键CDG基因（VNN1、PTGDS、MMP9、IL13RA2、S100A8和IL1B）， MR证实VNN1和MMP9是UC进展的致病危险因素。所有六个基因都与免疫细胞浸润、促炎细胞因子和肠屏障功能障碍显著相关。与无应答者相比，这六种CDGs的表达在生物治疗应答者中显著下调。基于这些基因，UC患者被分为两组：具有严重疾病活动性的C1组和Mayo评分较低且治疗敏感性增强的C2组。此外，敲除VNN1在功能上减轻了肠道炎症。结论：这6个基因可用于评估UC的严重程度和预测治疗结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Inflammation Research Immunology and Microbiology-Immunology

CiteScore

6.10

自引率

2.20%

发文量

658

审稿时长

16 weeks

期刊介绍： An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.