ArticleBiochemistry research international2026
Candidate Biomarkers for Crohn's Disease: Hub Genes and Regulatory miRNAs Identified by Bioinformatics Analysis.
Article in Biochemistry research international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Candidate Biomarkers for Crohn's Disease: Hub Genes and Regulatory miRNAs Identified by Bioinformatics Analysis.Biochemistry research international · 2026Article
- Identification of COPZ1 as a Shared Candidate Ferroptosis-Related Hub Gene in Periodontitis and Inflammatory Bowel Disease.Human mutation · 2026Article
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Authors and funding
2 authors.
Funding
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Abstract
Background: Crohn's disease (CD) is a chronic, complex inflammatory condition that can affect the entire digestive tract, most commonly the terminal ileum. The exact cause of CD remains unknown. Bioinformatics was used in this study to identify the differentially expressed genes (DEGs) and microRNAs (miRNAs) that show potential as diagnostic and therapeutic agents in treating CD. Materials and Methods: Datasets were downloaded from the Gene Expression Omnibus database and filtered. DEGs between CD samples and healthy control samples were identified using the GEO2R tool (including GEOquery and Linear Models for Microarray Analysis), and Kyoto Encyclopedia of Genes and Genomes/Gene Ontology enrichment analyses were conducted as part of the study to gain deeper insights into the data. A network depicting protein-protein interactions was established and visualized using the STRING database and Cytoscape software, and hub genes were identified and extracted utilizing the cytoHubba program. Cytoscape and miRTarBase were used to construct the miRNA-hub gene regulatory network and predict the potential miRNAs associated with the DEGs. The hub genes were analyzed further using ROC curves and combined ROC curve analyses of the GSE179285, GSE186582, and GSE112366 datasets. A regularized LASSO regression model was constructed to reduce the risk of overfitting. Results: Three datasets (GSE179285, GSE186582, and GSE112366) were selected. A comprehensive analysis of the three datasets revealed 60 DEGs that showed significantly altered expression levels, including 44 upregulated genes and 16 downregulated genes. Ten different algorithms were randomly used, and three hub genes ( Conclusion: Three hub genes (
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