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Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic ta...

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Autores principales: Derakhshan Nazari, Mohammad Hossein, Shahrokh, Shabnam, Ghanbari-Maman, Leila, Maleknia, Samaneh, Ghorbaninejad, Mahsa, Meyfour, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623293/
https://www.ncbi.nlm.nih.gov/pubmed/37928018
http://dx.doi.org/10.1016/j.heliyon.2023.e21154
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author Derakhshan Nazari, Mohammad Hossein
Shahrokh, Shabnam
Ghanbari-Maman, Leila
Maleknia, Samaneh
Ghorbaninejad, Mahsa
Meyfour, Anna
author_facet Derakhshan Nazari, Mohammad Hossein
Shahrokh, Shabnam
Ghanbari-Maman, Leila
Maleknia, Samaneh
Ghorbaninejad, Mahsa
Meyfour, Anna
author_sort Derakhshan Nazari, Mohammad Hossein
collection PubMed
description Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils’ accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.
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spelling pubmed-106232932023-11-04 Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study Derakhshan Nazari, Mohammad Hossein Shahrokh, Shabnam Ghanbari-Maman, Leila Maleknia, Samaneh Ghorbaninejad, Mahsa Meyfour, Anna Heliyon Research Article Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils’ accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting. Elsevier 2023-10-18 /pmc/articles/PMC10623293/ /pubmed/37928018 http://dx.doi.org/10.1016/j.heliyon.2023.e21154 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Derakhshan Nazari, Mohammad Hossein
Shahrokh, Shabnam
Ghanbari-Maman, Leila
Maleknia, Samaneh
Ghorbaninejad, Mahsa
Meyfour, Anna
Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title_full Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title_fullStr Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title_full_unstemmed Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title_short Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study
title_sort prediction of anti-tnf therapy failure in ulcerative colitis patients by ensemble machine learning: a prospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623293/
https://www.ncbi.nlm.nih.gov/pubmed/37928018
http://dx.doi.org/10.1016/j.heliyon.2023.e21154
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