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机器学习基因表达预测模型用于预测克罗恩病患者乌司奴单抗的应答。

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

机构信息

Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

出版信息

Immun Inflamm Dis. 2021 Dec;9(4):1529-1540. doi: 10.1002/iid3.506. Epub 2021 Sep 1.


DOI:10.1002/iid3.506
PMID:34469062
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8589399/
Abstract

BACKGROUND: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST. METHODS: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. RESULTS: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively. CONCLUSIONS: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

摘要

背景:最近的研究报告称,乌司奴单抗(UST)治疗克罗恩病(CD)的疗效在患者之间存在差异,但原因尚未阐明。本研究旨在基于对 UST 反应的 CD 患者的基因转录谱,建立一个预测模型。

方法:下载包含 86 例 CD 和 26 例正常样本的 GSE112366 数据集进行分析。首先确定差异表达基因(DEGs)。进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路分析。采用最小绝对收缩和选择算子回归分析来建立 UST 反应预测模型。

结果:共鉴定出 122 个 DEGs。GO 和 KEGG 分析显示,免疫反应途径在 CD 患者中显著富集。建立了一个包含 4 个基因(HSD3B1、MUC4、CF1 和 CCL11)的 UST 反应预测的多变量逻辑回归方程。在训练集和测试集中,患者的受试者工作特征曲线下面积分别为 0.746 和 0.734。

结论:本研究首次建立了 CD 患者 UST 反应的基因表达预测模型,为进一步研究提供了有价值的数据源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/02a67d2c88e4/IID3-9-1529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/9c1ec0c1b05c/IID3-9-1529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/2fdbe0c6db0e/IID3-9-1529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/bfbfb52f38ef/IID3-9-1529-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/935aff75ff06/IID3-9-1529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/02a67d2c88e4/IID3-9-1529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/9c1ec0c1b05c/IID3-9-1529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/2fdbe0c6db0e/IID3-9-1529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/bfbfb52f38ef/IID3-9-1529-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/935aff75ff06/IID3-9-1529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9a/8589399/02a67d2c88e4/IID3-9-1529-g005.jpg

相似文献

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Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

Immun Inflamm Dis. 2021-12

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[10]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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本文引用的文献

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