ArticleHuman cell2022
Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning.
Article in Human cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 17 citations in OpenAlex.
- Matriptase proteolysis selectively drives disassembly and internalization of the claudin-7 complex while preserving E-cadherin.Human cell · 2026Article
- FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis.MethodsX · 2025Article
- Clinical effects of phospholipase D2 in attenuating acute pancreatitis.World journal of gastroenterology · 2025Article
- DEFA5-producing CD4Frontiers in immunology · 2025Article
- Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker.Journal of inflammation research · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Identification of Cardiometabolic Protein Biomarkers for Acute Myocardial Infarction Using Olink Proteomics.Journal of inflammation research · 2025Article
- An Integrative Analysis of Transcriptome Combined with Machine Learning and Single-Cell RNA-Seq for the Common Biomarkers in Crohn's Disease and Kidney Stone Disease.Journal of inflammation research · 2025Article
- The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence and machine learning technologies in ulcerative colitis.Therapeutic advances in gastroenterology · 2024Review
- Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.PloS one · 2024Article
- Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis.Acta biochimica et biophysica Sinica · 2023Article
- Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning.Annals of translational medicine · 2023Article
- Article
- Increased Kremen2 predicts worse prognosis in colon cancer.Pathology oncology research : POR · 2023Article
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Ulcerative colitis (UC) is an immune-related disorder with enhanced prevalence globally. Early diagnosis is critical for the effective treatment of UC. However, it still lacks specific diagnostic signatures. The aim of our study was to explore efficient signatures and construct the diagnostic model for UC. Microarray data of GSE87473 and GSE48634, which were obtained from tissue biopsy samples, were downloaded from the Gene Expression Omnibus (GEO), and differently expressed genes (DEGs), GO, and KEGG analyses were performed. We constructed the PPI network via STRING database. The immune infiltration of the samples was evaluated using CIBERSORT methods combined with the LM22 feature matrix. The logistic regression model was constructed, with the expression of selected genes as the predictor variable, and the UC occurrence as the responsive variable. As a result, a total of 126 DEGs between the UC patients and normal counterparts were identified. The GO and KEGG analysis revealed that multiple biological processes, such as antimicrobial humoral immune response mediated by antimicrobial peptide and IL-17 signaling pathway, were enriched. The infiltration of eight immune cell types (B cells naive, Dendritic.cells.activated, Macrophages.M0, Macrophages.M2, Mast.cells.resting, Neutrophils, Plasma.cells, and T.cells.follicular.helper) was significantly different between patients with UC and normal counterparts. The top 50 most significant DEGs were selected for the construction of the PPI network. The average AUC of the logistic regression model in the fivefold cross-validation was 0.8497 in the training set, GSE87473. The AUC of another independent verification set of GSE48634 from the GEO database was 0.7208. In conclusion, we identified potential hub genes, including REG3A, REG1A, DEFA6, REG1B, and DEFA5, which might be significantly associated with UC progression. The logistic regression model based on the five genes could reliably diagnose UC patients.
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