ArticleAnnals of translational medicine2023
Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning.
Article in Annals of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Mechanistic Networks, Cellular Specificity, and Therapeutic Opportunities of Ferroptosis in Ulcerative Colitis.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The PAR-score based on PANoptosis genes predicts the progression of ulcerative colitis and the response to anti-TNF-α treatment.Genes & genomics · 2025Article
- Screening and experimental study of potential biomarkers for ulcerative colitis based on weighted gene co-expression network analysis and machine learning.Journal of translational medicine · 2025Article
- Significant correlation between ferroptosis-associated genes and ulcerative colitis: implications for diagnosis and treatment.European journal of medical research · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Mechanism of cell death and its application in the repair of inflammatory bowel disease by mesenchymal stem cells.Frontiers in immunology · 2025Review
- Bioinformatics and experimental unveiling of TIMP1 as a novel therapeutic target in colorectal cancer ferroptosis.Frontiers in oncology · 2025Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ulcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC. Methods: Gene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set. Results: Gene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes ( Conclusions: The previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.
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