Journal
HELIYON
Volume 8, Issue 11, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.heliyon.2022.e11905
Keywords
Differentially expressed genes; Gene expression omnibus; Kawasaki disease; Protein-protein interaction network; Random forest
Categories
Funding
- National Natural Science Foundation of China [81770316, 81970249]
- Qingdao Science and Technology Plan [20-3-4-47-nsh]
- Taishan Scholars Program of Shandong Province
Ask authors/readers for more resources
By using bioinformatics analysis, we identified abnormal methylated differentially expressed genes in Ka-wasaki disease and established a random forest diagnostic model. We identified 6 hub genes which improved our understanding of the molecular mechanism of KD. The constructed model can be used for accurate diagnosis and provide evidence for clinical diagnosis.
Objective: By using bioinformatics analysis, abnormal methylated differentially expressed genes (MDEGs) in Ka-wasaki disease (KD) were identified and a random forest diagnostic model for KD was established. Methods: The expression (GSE18606, GSE68004, GSE73461) and methylation (GSE109430) profiles was retrieved and download from Gene Expression Omnibus (GEO). We conducted enrichment analyses by using R software. In addition, we constructed a protein interaction network, and obtained 6 hub genes. We used expression profiles GSE100154 from GEO to verify the hub genes. Finally, we constructed a diagnostic model based on random forest. Results: We got a total of 55 MDEGs (43 hyper-methylated, low-expressing genes and 12 hypo-methylated, high -expressed genes). Six hub genes (CD2, IL2RB, IL7R, CD177, IL1RN, and MYL9) were identified by Cytoscape software. The area under curve (AUC) of the six hub genes was from 0.745 to 0.898, and the combined AUC was 0.967. The random forest diagnostic model showed that AUC was 0.901. Conclusion: The identification of 6 new hub genes improves our understanding of the molecular mechanism of KD, and the established model can be employed for accurate diagnosis and provide evidence for clinical diagnosis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available