Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers

Background In 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, t...

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Vydané v:BioData mining Ročník 17; číslo 1; s. 26 - 17
Hlavní autori: Chen, Yang, Zheng, Qingqing, Wang, Hui, Tang, Peiren, Deng, Li, Li, Pu, Li, Huan, Hou, Jianhong, Li, Jie, Wang, Li, Peng, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 27.08.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Adults
Algorithms
Bioinformatics
Biological markers
Biomarkers
Biomedical and Life Sciences
Clinical outcomes
CMV + UC
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Cytomegalovirus
Cytomegalovirus infections
Data mining
Data Mining and Knowledge Discovery
Development and progression
Diagnostic biomarkers
Diagnostic systems
Epstein-Barr virus
Gastroenterology
Gene expression
Genes
Genomes
Health aspects
Herpes viruses
Hospitals
Immune microenvironment
Immune system
Infection
Infections
Inflammatory bowel disease
Inflammatory bowel diseases
Integrative Analysis of Multi-Omics Data for Precision Medicine
Learning algorithms
Life Sciences
Machine learning
Mass spectrometry
Medical diagnosis
Medical research
Medicine, Experimental
Microenvironments
Modules
Morbidity
Mortality
Multi-omics
Next-generation sequencing
Pathology
Pediatrics
Peptides
Proteins
Proteomics
Scientific imaging
Software
Steroids
Transcriptomics
Ulcerative colitis
Viral infections
China
Yunnan China
Inflammatory bowel disease
Immune microenvironment
CMV + UC
Diagnostic biomarkers
Multi-omics
Machine learning
ISSN:1756-0381, 1756-0381
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Shrnutí:Background In 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. Method The 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. Results In 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. Conclusion This 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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ISSN:1756-0381
1756-0381
DOI:10.1186/s13040-024-00382-0