https://orcid.org/0000-0003-2329-4390
Abstract
Background
Ulcerative colitis is a recurrent autoimmune disease. At present, the pathogenesis of UC is not completely clear. Hence, the etiology and underlying molecular mechanism need to be further investigated.
Methods
Three sets of microarray datasets were included from the Gene Expression Omnibus database. The differentially expressed genes in two sets of datasets were analyzed using the R software, and the core genes of UC were screened using machine learning. The sensitivity and specificity of the core genes were evaluated with the receiver operating characteristic curve in another microarray dataset. Subsequently, the CIBERSORT tool was used to analyze the relationship between UC and its core genes and immune cell infiltration. To verify the relationship between UC and core genes and the relationship between core genes and immune cell infiltration in vivo.
Results
A total of 36 DEGs were identified. AQP8, HMGCS2, and VNN1 were determined to be the core genes of UC. These genes had high sensitivity and specificity in receiver operating characteristic curve analysis. According to the analysis of immune cell infiltration, neutrophils, monocytes, and macrophages were positively correlated with UC. AQP8, HMGCS2, and VNN1 were also correlated with immune cell infiltration to varying degrees. In vivo experiments verified that the expressions of neutrophils, monocytes, and macrophages increased in the UC colon. Furthermore, the expressions of AQP8 and HMGCS2 decreased, whereas that of VNN1 increased. Azathioprine treatment improved all the indicators to different degrees.
Conclusion
AQP8, HMGCS2, and VNN1 are the core genes of UC and exhibit different degrees of correlation with immune cells. These genes are expected to become new therapeutic targets for UC. Moreover, the occurrence and development of UC are influenced by immune cell infiltration.
Abbreviations
UC, ulcerative colitis; GEO, gene expression omnibus; LASSO, least absolute shrinkage and selection operator; SVM, support vector machine; GO, gene Ontology; KEGG, Kyoto encyclopedia of genes and genomes; ROC, receiver operating characteristic; AUC, area under ROC curve; DEGs, differentially expressed genes; GSEA, Gene Set Enrichment Analysis; DSS, dextran sulfate sodium; AZA, azathioprine; AQP, aquaporins; HMGCS2, mitochondrial 3-hydroxy-3-methylglutaryl CoA synthase 2; VNN1, vascular noninflammatory molecule 1; MC, mast cells.
Acknowledgments
This study benefited from the GEO website, and we thank the GEO website for generously sharing a large amount of data. The authors would like to thank all the reviewers who participated in the review, as well as MJEditor (www.mjeditor.com) for providing English editing services during the preparation of this manuscript.
Author Contributions
DPQ: responsible for project planning and implementation, review of manuscripts; DYZ: bioinformatics analysis, experimental validation, and manuscript drafting; LMY: article conception, data processing, and manuscript revision; QY: data review, revision and translation of manuscripts. All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.
Disclosure
This study has no conflict of interest.