ArticleBioData mining2024
Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers.
Article in BioData mining, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrated transcriptomic, proteomic, and metabolomic analyses reveal molecular mechanisms underlying clutch length differences in goose ovaries.Poultry science · 2026Article
- Targeting RRBP1 reverses immune evasion and enhances immunotherapy efficacy via the CXCL10-CXCR3 axis in bladder cancer.Journal for immunotherapy of cancer · 2026Article
- Epithelial cell-based multi-omics integration identifies SPINK5 and SRI as novel diagnostic biomarkers for ulcerative colitis.Frontiers in pharmacology · 2026Article
- From dysbiosis to precision medicine: targeting the microbial-metabolic axis in IBD management.Frontiers in cellular and infection microbiology · 2026Review
- RRBP1 Inhibition Reduces Microglial M1 Polarization and Inflammation-Mediated Neuronal Loss and Oxidative Stress by Regulating ERK Pathway in Alzheimer's Disease.Molecular neurobiology · 2025Article
- Comprehensive multi-omics analysis of tandem duplicator phenotypes in non-small cell lung cancer.Frontiers in medicine · 2025Article
- Artificial intelligence-based multimodal model for the identification of ulcerative colitis with concomitant cytomegalovirus colitis.Therapeutic advances in gastroenterology · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundIn recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored.
methodThe xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model.
resultsIn this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC.
conclusionThis 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection.
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