Computational Learning of microRNA-Based Prediction of Pouchitis Outcome After Restorative Proctocolectomy in Patients With Ulcerative Colitis.
deep learning
pouchitis
prognostic biomarker
Journal
Inflammatory bowel diseases
ISSN: 1536-4844
Titre abrégé: Inflamm Bowel Dis
Pays: England
ID NLM: 9508162
Informations de publication
Date de publication:
18 10 2021
18 10 2021
Historique:
received:
07
08
2020
pubmed:
21
2
2021
medline:
15
2
2022
entrez:
20
2
2021
Statut:
ppublish
Résumé
Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). However, inflammation often develops in the pouch, leading to acute or recurrent/chronic pouchitis (R/CP). MicroRNAs (miRNA) are used as accurate diagnostic and predictive biomarkers in many human diseases, including inflammatory bowel diseases. Therefore, we aimed to identify an miRNA-based biomarker to predict the occurrence of R/CP in patients with UC after colectomy and IPAA. We conducted a retrospective study in 3 tertiary centers in France. We included patients with UC who had undergone IPAA with or without subsequent R/CP. Paraffin-embedded biopsies collected from the terminal ileum during the proctocolectomy procedure were used for microarray analysis of miRNA expression profiles. Deep neural network-based classifiers were used to identify biomarkers predicting R/CP using miRNA expression and relevant biological and clinical factors in a discovery cohort of 29 patients. The classification algorithm was tested in an independent validation cohort of 28 patients. A combination of 11 miRNA expression profiles and 3 biological/clinical factors predicted the outcome of R/CP with 88% accuracy (area under the curve = 0.94) in the discovery cohort. The performance of the classification algorithm was confirmed in the validation cohort with 88% accuracy (area under the curve = 0.90). Apoptosis, cytoskeletal regulation by Rho GTPase, and fibroblast growth factor signaling were the most dysregulated targets of the 11 selected miRNAs. We developed and validated a computational miRNA-based algorithm for accurately predicting R/CP in patients with UC after IPAA.
Sections du résumé
BACKGROUND
Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). However, inflammation often develops in the pouch, leading to acute or recurrent/chronic pouchitis (R/CP). MicroRNAs (miRNA) are used as accurate diagnostic and predictive biomarkers in many human diseases, including inflammatory bowel diseases. Therefore, we aimed to identify an miRNA-based biomarker to predict the occurrence of R/CP in patients with UC after colectomy and IPAA.
METHODS
We conducted a retrospective study in 3 tertiary centers in France. We included patients with UC who had undergone IPAA with or without subsequent R/CP. Paraffin-embedded biopsies collected from the terminal ileum during the proctocolectomy procedure were used for microarray analysis of miRNA expression profiles. Deep neural network-based classifiers were used to identify biomarkers predicting R/CP using miRNA expression and relevant biological and clinical factors in a discovery cohort of 29 patients. The classification algorithm was tested in an independent validation cohort of 28 patients.
RESULTS
A combination of 11 miRNA expression profiles and 3 biological/clinical factors predicted the outcome of R/CP with 88% accuracy (area under the curve = 0.94) in the discovery cohort. The performance of the classification algorithm was confirmed in the validation cohort with 88% accuracy (area under the curve = 0.90). Apoptosis, cytoskeletal regulation by Rho GTPase, and fibroblast growth factor signaling were the most dysregulated targets of the 11 selected miRNAs.
CONCLUSIONS
We developed and validated a computational miRNA-based algorithm for accurately predicting R/CP in patients with UC after IPAA.
Identifiants
pubmed: 33609036
pii: 6145454
doi: 10.1093/ibd/izab030
doi:
Substances chimiques
Biomarkers
0
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1653-1660Informations de copyright
© 2021 Crohn’s & Colitis Foundation. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.