A two-transcript classifier model for assessing disease activity in patients with ulcerative colitis: A discovery and validation study

IF 4 3区医学 Q1 GASTROENTEROLOGY & HEPATOLOGY

Digestive and Liver Disease Pub Date : 2025-06-01 DOI:10.1016/j.dld.2025.02.015

Huipeng Zhang , Nannan Xu , Gang Huang , Guanwei Bi , Jing Zhang , Xiaohan Zhao , Xinrui Guo , Miaolin Lei , Gang Wang , Yanbo Yu

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引用次数: 0

Abstract

Aims

We aimed to develop and validate classifier models to assess disease activity in UC patients by evaluating potential classifier genes expression levels.

Methods

The study comprised UC and healthy control participants undergoing colonoscopy. We screened candidate genes (TGF-β1, CEACAM1 and CD177) using Differentially Expressed Genes. We compared candidate genes expression levels with the validated UC scores. UC patients were subsequently randomly assigned (1:1) to the discovery or validation groups. A logistic regression model integrating candidate genes expression was developed using discovery group and assessed its predictive effect in validation group.

Results

Three candidate genes were differentially associated with UC disease activity. TGF-β1 and CD177 were used to construct the logistic regression model. The two-transcript classifier model had an area under the receiver operating curve (AUC) of 0.938 (95 % confidence interval [CI]=0.888–0.987) in discriminating between remission and active UC and an AUC of 0.919 (0.862–0.977) in discriminating between remission-mild and moderate-severe activity in UC.

Conclusions

TGF-β1 and CD177 transcript levels, measured by RT-PCR, are robust classifiers for assessing disease activity in UC patients, and the measurement of these transcript levels appears to be an effective method of monitoring condition of UC patients and predicting treatment effectiveness over time.

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评估溃疡性结肠炎患者疾病活动性的双转录本分类模型：一项发现和验证研究。

目的：我们旨在开发和验证分类器模型，通过评估潜在的分类器基因表达水平来评估UC患者的疾病活动性。方法：该研究包括UC和健康对照组接受结肠镜检查。我们使用差异表达基因筛选候选基因（TGF-β1、CEACAM1和CD177）。我们将候选基因表达水平与验证的UC评分进行比较。随后将UC患者随机（1:1）分配到发现组或验证组。利用发现组建立整合候选基因表达的逻辑回归模型，并评估其在验证组的预测效果。结果：三个候选基因与UC疾病活动性有差异相关。采用TGF-β1和CD177构建logistic回归模型。双转录本分类器模型区分缓解型和活动性UC的受试者工作曲线下面积（AUC）为0.938(95%可信区间[CI]=0.888-0.987)，区分缓解-轻度和中重度UC活动性的AUC为0.919（0.862-0.977）。结论：通过RT-PCR测量TGF-β1和CD177转录物水平，是评估UC患者疾病活动性的可靠分类指标，测量这些转录物水平似乎是监测UC患者病情和预测治疗效果的有效方法。

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

Digestive and Liver Disease 医学-胃肠肝病学

CiteScore

6.10

自引率

2.20%

发文量

632

审稿时长

19 days

期刊介绍： Digestive and Liver Disease is an international journal of Gastroenterology and Hepatology. It is the official journal of Italian Association for the Study of the Liver (AISF); Italian Association for the Study of the Pancreas (AISP); Italian Association for Digestive Endoscopy (SIED); Italian Association for Hospital Gastroenterologists and Digestive Endoscopists (AIGO); Italian Society of Gastroenterology (SIGE); Italian Society of Pediatric Gastroenterology and Hepatology (SIGENP) and Italian Group for the Study of Inflammatory Bowel Disease (IG-IBD). Digestive and Liver Disease publishes papers on basic and clinical research in the field of gastroenterology and hepatology. Contributions consist of: Original Papers Correspondence to the Editor Editorials, Reviews and Special Articles Progress Reports Image of the Month Congress Proceedings Symposia and Mini-symposia.