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

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...

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Autores principales:	Manrong He, Chao Li, Wanxin Tang, Yingxi Kang, Yongdi Zuo, Yufang Wang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Crohn's disease LASSO regression machine learning model ustekinumab Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/e1335762657345cea6a203eaf42048ef
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spelling	oai:doaj.org-article:e1335762657345cea6a203eaf42048ef2021-11-12T19:57:15ZMachine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease2050-452710.1002/iid3.506https://doaj.org/article/e1335762657345cea6a203eaf42048ef2021-12-01T00:00:00Zhttps://doi.org/10.1002/iid3.506https://doaj.org/toc/2050-4527Abstract 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.Manrong HeChao LiWanxin TangYingxi KangYongdi ZuoYufang WangWileyarticleCrohn's diseaseLASSO regressionmachine learning modelustekinumabImmunologic diseases. AllergyRC581-607ENImmunity, Inflammation and Disease, Vol 9, Iss 4, Pp 1529-1540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Crohn's disease LASSO regression machine learning model ustekinumab Immunologic diseases. Allergy RC581-607
spellingShingle	Crohn's disease LASSO regression machine learning model ustekinumab Immunologic diseases. Allergy RC581-607 Manrong He Chao Li Wanxin Tang Yingxi Kang Yongdi Zuo Yufang Wang Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
description	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.
format	article
author	Manrong He Chao Li Wanxin Tang Yingxi Kang Yongdi Zuo Yufang Wang
author_facet	Manrong He Chao Li Wanxin Tang Yingxi Kang Yongdi Zuo Yufang Wang
author_sort	Manrong He
title	Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
title_short	Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
title_full	Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
title_fullStr	Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
title_full_unstemmed	Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease
title_sort	machine learning gene expression predicting model for ustekinumab response in patients with crohn's disease
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/e1335762657345cea6a203eaf42048ef
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Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease

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