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. 2022 Jul 14:9:917077.
doi: 10.3389/fmolb.2022.917077. eCollection 2022.

Identification of Effective Diagnostic Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis

Chenyang Li  1 , Yongping Lu  2 , Xiuping Han  1
Affiliations

Identification of Effective Diagnostic Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis

Chenyang Li et al. Front Mol Biosci. .

Abstract

Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis. Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the "Batch correction" and "RobustRankAggreg" methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the "cytoHubba" plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis. Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011). Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.

Keywords: atopic dermatitis; bioinformatics analysis; diagnostic biomarkers; immune cells infiltration; immunohistochemical verification.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flow chart summarizing the procedures used in this study.
FIGURE 2
FIGURE 2
Determination of DEGs. (A) The first 20 upregulated and downregulated DEGs of the three datasets merged using the “RRA” approach. (B) Heat map of the DEGs obtained via the “Batch correction” approach. (C) Volcano map of the DEGs obtained via the “Batch correction” approach. (D) Venn diagram of the intersection of the DEGs screened using the two methods. DEGs, differentially expressed genes; RRA, RobustRankAggreg.
FIGURE 3
FIGURE 3
Co-expression network assessment using WGCNA. (A) Clustering dendrograms of DEGs from “Batch correction.” (B) Assessment of the scale-free fitness index and mean connectivity for various soft-thresholding powers. (C) Dendrogram of all the genes clustered based on dissimilarity in the topological overlap. (D) Heatmap exhibiting the relationship between module eigengenes and clinical traits. Each row represents a color module, and every column represents a clinical trait. The correlation coefficient and corresponding p value are shown in each cell. (E) Scatter plot exhibiting genes in the blue module. WGCNA, weighted gene co-expression network analysis; DEGs, differentially expressed genes.
FIGURE 4
FIGURE 4
Determination of co-DEGs and functional enrichment analysis. (A) Venn diagram illustrating the co-DEGs screened from the intersection of the DEGs and the module genes with AD traits. (B) Remarkably enriched GO terms of co-DEGs. (C) Remarkably enriched KEGG cascades of co-DEGs. (D) Network illustrating terms occurring abundantly in co-DEGs. Every node designates an enriched term colored based on its cluster identity. (E) The same enriched network with nodes colored via p-value. (F) The top 20 pathways linked to co-DEGs based on a comprehensive enrichment assessment. co-DEGs, common differentially expressed genes; AD, atopic dermatitis; GO, gene ontology; KEGG, Kyoto encyclopedia of genes and genomes.
FIGURE 5
FIGURE 5
Determination of candidate hub genes. (A) The PPI network of co-DEGs. (B) Candidate hub genes were screened out using 12 algorithms. PPI, protein-protein interaction; co-DEGs, common differentially expressed genes.
FIGURE 6
FIGURE 6
The relative expression levels of 11 candidate hub genes, i.e., ((A) CCR7, (B) CCNA2, (C) CXCL10, (D) IRF7, (E) ISG15, (F) KIAA0101, (G) MKI67, (H) MMP1, (I) NCAPG, (J) RRM2, and (K) SERPINB3, validated using GSE120721. AD, atopic dermatitis; HC, healthy control.
FIGURE 7
FIGURE 7
The diagnostic effectiveness of nine hub genes ((A) CCR7, (B) CCNA2, (C) CXCL10, (D) IRF7, (E) ISG15, (F) MKI67, (G) MMP1, (H) NCAPG, and (I) RRM2 was validated using GSE120721.
FIGURE 8
FIGURE 8
Expression of (A) CCR7, (B) CXCL10, (C) IRF7, (D) MMP1, and (E) RRM2 in AD tissue and healthy tissue. Magnification, ×200. AD, atopic dermatitis; HC, healthy control.
FIGURE 9
FIGURE 9
Analysis of immune cell infiltration and the relationship of hub genes with differential immune cells in individuals with AD. (A) Relative fraction of 22 sub-populations of immune cells in AD samples. (B) The differences of 22 sub-populations of immune cells among the AD and HC tissues. (C) Correlation among four remarkable differential immune cells and five identified hub genes. (D) Remarkably related hub genes and immune cells screened by the criteria R > 0.4 and p < 0.05.
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