ArticleImmunity, inflammation and disease2021
Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.
Article in Immunity, inflammation and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical Significance of Mucin Signatures in Inflammatory Bowel Diseases: A Systematic Review of Their Expression Patterns, Polymorphisms, and Post-translational Modifications.Inflammatory bowel diseases · 2026Pooled it
- Multi-Omics Biomarkers for Predicting Efficacy of Biologic and Small-Molecule Therapies in Adults With Inflammatory Bowel Disease: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Review
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Exploratory transcriptomic analysis suggests candidate genes associated with loss of response to ustekinumab in Crohn's disease.Frontiers in genetics · 2026Article
- Artificial intelligence in inflammatory bowel disease: Current applications and future directions.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Predicting ustekinumab treatment response in Crohn's disease using pre-treatment biopsy images.Bioinformatics (Oxford, England) · 2025Article
- Identification of Potential Functional Modules and Diagnostic Genes for Crohn's Disease Based on Weighted Gene Co-expression Network Analysis and LASSO Algorithm.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care.Therapeutic advances in gastroenterology · 2025Review
- Development of Predictive Models for Long-Term Endoscopic Response to Ustekinumab in Crohn's Disease Based on Plasma Proteomics.Journal of inflammation research · 2025Article
- Intestinal mRNA expression profiles associated with mucosal healing in ustekinumab-treated Crohn's disease patients: bioinformatics analysis and prospective cohort validation.Journal of translational medicine · 2024Observational
- Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.International journal of molecular sciences · 2024Observational
- Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion.Annals of medicine · 2023Review
- Precision medicine and drug optimization in adult inflammatory bowel disease patients.Therapeutic advances in gastroenterology · 2023Review
- Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.Immunity, inflammation and disease · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent 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.
methodsThe 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.
resultsA 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.
conclusionsThis 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.
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