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. 2022 Dec 21;12(1):22067.
doi: 10.1038/s41598-022-26689-8.

Identification of novel candidate genes and predicted miRNAs in atopic dermatitis patients by bioinformatic methods

LiangHong Chen  1   2   3 , Xin Qi  1   3 , JingYu Wang  1   3 , JiaLi Yin  1   3 , PeiHong Sun  1   3 , Yan Sun  4   5 , Yan Wu  1   3 , Li Zhang  1   3 , XingHua Gao  1   3
Affiliations

Identification of novel candidate genes and predicted miRNAs in atopic dermatitis patients by bioinformatic methods

LiangHong Chen et al. Sci Rep. .

Abstract

Atopic dermatitis (AD) is a common, chronic inflammatory dermatosis with relapsing eruptions. Our study used bioinformatics to find novel candidate differentially expressed genes (DEGs) and predicted miRNAs between AD patients and healthy controls. The Mesh term "atopic dermatitis" was retrieved to obtain DEGs in GEO datasets. DEGs between AD patients and healthy controls were analyzed using GEO2R. Overlapping DEGs between different datasets were obtained with use of Draw Venn software. GO and KEGG enrichment analyses were conducted by the use of DAVID. STRING and miRWalk were used to individually analyze PPI networks, interactions of candidate genes and predicted miRNAs. A total of 571 skin samples, as retrieved from 9 databases were assessed. There were 225 overlapping DEGs between lesioned skin samples of AD patients and that of healthy controls. Nineteen nodes and 160 edges were found in the largest PPI cluster, consisting of 17 up-regulated and 2 down-regulated nodes. Two KEGG pathways were identified, including the cell cycle (CCNB1, CHEK1, BUB1B, MCM5) and p53 (CCNB1, CHEK1, GTSE1) pathways. There were 56 nodes and 100 edges obtained in the miRNA-target gene network, with has-miR-17-5p targeted to 4 genes and has-miR-106b-5p targeted to 3 genes. While these findings will require further verification as achieved with experiments involving in vivo and in vitro modles, these results provided some initial insights into dysfunctional inflammatory and immune responses associated with AD. Such information offers the potential to develop novel therapeutic targets for use in preventing and treating AD.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow diagram of the analysis strategy. DEG differentially expressed gene, GO gene ontology, BP biological process, CC cell component, MF molecular function, KEGG Kyoto Encyclopedia of Genes and Genomes, PPI protein–protein interaction, LS lesioned skin samples of AD patients, HC healthy controls, NLS non-lesioned skin samples of AD patients.
Figure 2
Figure 2
Overlapping DEGs obtained among the different groups as determined using Draw Venn software. (a) lesioned skin samples of AD patients versus healthy controls; (b) non-lesioned skin samples of AD patients versus healthy controls.
Figure 3
Figure 3
GO analysis of DEGs obtained among the different groups as determined using DAVID. (a–c) lesioned skin samples of AD patients versus healthy controls; (d–f) non-lesioned skin samples of AD patients versus healthy controls.
Figure 4
Figure 4
PPI network analysis among the different groups as determined using STRING and Cytoscape (the largest cluster as obtained with MCODE). (a,b) lesioned skin samples of AD patients versus healthy controls; (c,d) non-lesioned skin samples of AD patients versus healthy controls. (e,f) lesioned skin versus non-lesioned skin samples of AD patients.
Figure 5
Figure 5
KEGG pathway. (a) Cell cycle pathway; (b) P53 pathway. Chk1 (CHEK1, checkpoint kinase 1), BubR1 (BUB1B, BUB1 mitotic checkpoint serine/threonine kinase B), Cyc B (cyclin B1, CCNB1), MCM5 (minichromosome maintenance complex component 5) and B99 (GTSE1, G2 and S-phase expressed 1).
Figure 6
Figure 6
Regulatory relationships between human genes and predicted miRNA analyses as determined using miRWalk 3.0. Red indicates up-regulated genes, green down-regulated genes and blue indicates predicted miRNAs.
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