# Candidate Genes of Allergic Dermatitis Are Associated with Immune Response

Authors: Lei Jin; Lin Deng; Wanchun Wang
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

Allergic dermatitis (AD) is a common and burdensome inflammatory skin disease, and diagnosis is challenging. This study was conducted to identify candidate genes for AD diagnosis and underlying molecular mechanisms. Gene expression profiles were obtained from datasets GSE121212, GSE130588, and GSE157194. Use differential analysis to identify differentially expressed genes (DEGs) between AD and control. Use enrichment analysis to identify potential molecular dysregulation mechanisms. Comprehensive least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operator characteristic (ROC) curve, and logistic regression analysis are used to identify candidate genes. In addition, ssGSEA and ImmPort database were used to identify AD-related immune response abnormalities. In this study, a total of 60 common genes were identified. Enrichment analysis found that these genes are mainly involved in Th17 cell immune and complement and coagulation cascades. LASSO regression analysis identified 18 feature genes, and screened genes with AUC >0.75 were selected as candidate genes. Finally, PLA2G4D, IFI6, AGR3, IGFL1, SPRR3, ATP13A5, SERPINB13, KRT16, HAS3, and CH25H were recognized as candidate genes and may be able to diagnose AD. PLA2G4D, CH25H, and IFI6 may be risk factors for AD based on logistic analysis. Furthermore, we identified the abnormalities of immune response activation in AD patients. Interestingly, PLA2G4D, CH25H, and IFI6 had positive correlations with immune cells and signaling pathways. PLA2G4D, CH25H, and IFI6 may be candidate diagnostic genes for AD. This may be related to their promotion of abnormal immune activation, especially Th17 cell immune.


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## Body

## 1. Introduction

Allergic dermatitis (AD) is an inflammatory skin disease caused by skin contact with various allergens in the external environment [1]. Studies have shown that AD often occurs in children, with an incidence rate of 15–20%. Among adults, the incidence rate is 1–3%. In recent years, the incidence rate has increased by 2-3 times [2]. Common symptoms of AD patients are erythema, edema, papules, blisters, bullae, and even necrosis, accompanied by varying degrees of itching, pain, or burning [3, 4]. Children are common on the cheeks, and adult patients are common on the folds of the joints and on the back of the hands or on the scalp [5, 6].AD is related to the combined effects of heredity, environment, immunity, and other factors and has an obvious familial genetic tendency. Studies have reported some susceptible areas on chromosomes [7]. Currently, it is believed that various internal and external factors act on the immune cells of the body, including immune cells fixed in the skin and other nonfixed immune cells that dynamically enter the skin, causing immune abnormalities and ultimately leading to the occurrence of AD [8]. The occurrence of AD is related to disease factors, such as asthma, hay fever, allergic rhinitis, and food allergies [9]. Meanwhile, studies have found that the expression of IgE is significantly increased in AD patients, and the level of IgE is significantly correlated with the severity of the disease [10].So far, the diagnosis of AD is still based on its clinical symptoms, and no experimental diagnosis has become the gold standard for clinical diagnosis. Different countries or regions adopt different diagnostic criteria, and scholars are constantly revising and improving the diagnostic criteria, but the Hanifin and Rajka criteria and the Williams diagnostic criteria are still more commonly used [11, 12]. However, due to the complicated clinical manifestations of AD and more family history and concomitant diseases, the results of skin prick tests and total IgE levels are often inconsistent, and this method requires subjective judgment and is poor in reproducibility. Moreover, the abovementioned diagnostic methods cannot be effectively used as an early warning for the occurrence of AD.Up to now, researchers have conducted a lot of analyses and testing on AD. However, no effective diagnostic method has been identified. It is of great significance to seek superior diagnostic targets and establish diagnostic methods for the treatment and prevention of AD patients. Therefore, this study reanalyzes the differential genes of AD patients in the existing GSE121212, GSE130588, and GSE157194 databases in order to provide valuable genetic targets for future AD diagnosis and research.


## 2. Materials and Methods

### 2.1. Data Collection

GSE121212, GSE130588, and GSE157194 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The GSE121212 dataset included gene expression profiles of skin tissue from 27 AD patients and 38 healthy controls based on high throughput sequencing. The GSE130588 dataset included gene expression profiles of 42 lesional skin from AD patients and 38 nonlesional controls based on GPL570. The GSE157194 dataset included gene expression profiles of 57 lesional skin from AD patients and 54 nonlesional controls based on high throughput sequencing.


### 2.2. Difference Analysis

The differential analysis of gene expression between AD and controls was performed using DEseq2 R software package [13] for GSE121212 and GSE157194 datasets or limma R software package [14] for the GSE130588 dataset. Differentially expressed genes (DEGs) were then obtained by setting screening threshold with log2|FC| >1 and P<0.05. Furthermore, we identified the intersection genes of the three sets of DEGs as common genes.


### 2.3. Enrichment Analysis

The enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for common genes was performed using clusterProfiler R package [15]. The P value <0.05 was considered significantly enriched.


### 2.4. Screening of Key Genes

The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to primarily select feature genes using glmnet R package. The optimalλ was chosen corresponding to minimum cross-validation error. The variables were eliminated, ultimately retaining potential genes with nonzero coefficients. Then, receiver operator characteristic (ROC) curve was plotted, and area under the ROC curve (AUC) was calculated to evaluate the diagnostic role of feature genes. The logistic regression analysis was performed to draw forest plots.


### 2.5. Calculation of the Immune Score

The marker genes for immune cell types were obtained from Bindea et al. [16]. The infiltration level of immune cell type was calculated using single-sample gene set enrichment analysis (ssGSEA) by GSVA R software package. The immune-related pathways were obtained from the ImmPort database.


## 2.1. Data Collection

GSE121212, GSE130588, and GSE157194 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The GSE121212 dataset included gene expression profiles of skin tissue from 27 AD patients and 38 healthy controls based on high throughput sequencing. The GSE130588 dataset included gene expression profiles of 42 lesional skin from AD patients and 38 nonlesional controls based on GPL570. The GSE157194 dataset included gene expression profiles of 57 lesional skin from AD patients and 54 nonlesional controls based on high throughput sequencing.


## 2.2. Difference Analysis

The differential analysis of gene expression between AD and controls was performed using DEseq2 R software package [13] for GSE121212 and GSE157194 datasets or limma R software package [14] for the GSE130588 dataset. Differentially expressed genes (DEGs) were then obtained by setting screening threshold with log2|FC| >1 and P<0.05. Furthermore, we identified the intersection genes of the three sets of DEGs as common genes.


## 2.3. Enrichment Analysis

The enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for common genes was performed using clusterProfiler R package [15]. The P value <0.05 was considered significantly enriched.


## 2.4. Screening of Key Genes

The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to primarily select feature genes using glmnet R package. The optimalλ was chosen corresponding to minimum cross-validation error. The variables were eliminated, ultimately retaining potential genes with nonzero coefficients. Then, receiver operator characteristic (ROC) curve was plotted, and area under the ROC curve (AUC) was calculated to evaluate the diagnostic role of feature genes. The logistic regression analysis was performed to draw forest plots.


## 2.5. Calculation of the Immune Score

The marker genes for immune cell types were obtained from Bindea et al. [16]. The infiltration level of immune cell type was calculated using single-sample gene set enrichment analysis (ssGSEA) by GSVA R software package. The immune-related pathways were obtained from the ImmPort database.


## 3. Results

### 3.1. Differentially Expressed Genes in AD

In order to identify abnormally expressed genes related to AD, we performed a differential expression analysis of AD and control genes. In the GSE121212 dataset, we have identified 4651 DEGs (Figure1(a)). We have identified 554 DEGs in the GSE130588 dataset (Figure 1(b)). We identified 1962 DEGs in the GSE157194 dataset (Figure 1(c)). Among them, we obtained a total of 60 common genes (Figure 1(d)). The expression of common genes in AD and control is significantly different, and they may be abnormally expressed genes related to AD (Figure 1(e)).Figure 1
Identification of differentially expressed genes in AD. Differentially expressed genes between AD and controls were identified in the GSE121212 dataset (a), GSE130588 dataset (b), and GSE157194 dataset (c). Red are upregulated genes and blue are downregulated genes. (d) Common genes for the two groups of differentially expressed genes. A total of 60 common genes were found. (e) Heatmap of common genes in AD and control samples of the GSE157194 dataset. Red are upregulated genes and blue are downregulated genes.
(a)(b)(c)(d)(e)


### 3.2. Enrichment Analysis of Common Genes

In order to identify the potential molecular dysregulation mechanism of AD, we performed enrichment analysis on common genes. The GO results (Figure2(a)) suggested that defense response to bacterium, negative regulation of endopeptidase activity, and negative regulation of peptidase activity of biological processes (BP) were mainly enriched by common genes. The secretory granule lumen, cornified envelope, and specific granule lumen of the cellular components were also enriched. As well as serine-type endopeptidase inhibitor activity, endopeptidase inhibitor activity, and RAGE receptor binding of molecular function were mainly enriched. In addition, the IL-17 signaling pathway, Th17 cell differentiation, and complement and coagulation cascades of KEGG pathways were mainly signature enriched by common genes (Figure 2(b)).Figure 2
The GO and KEGG pathways enriched by common genes. (a) Top 10 biological functions of common genes significantly enriched. The BP, CC, and MF included in GO enrichment. (b) KEGG signaling pathways with significant involvement of common genes.
(a)(b)


### 3.3. Candidate Genes of AD

In order to identify diagnostic candidate genes that can predict the occurrence of AD, we performed LASSO regression analysis on common genes. Based on the bestλ value, we identified 18 feature genes could predict AD most accurately (Figures 3(a) and 3(b)). The AUC values for feature genes were calculated and found the AUC values were great than 0.5 (Figures 3(c)–3(e)). Among GSE121212, GSE130588, and GSE157194 datasets, the genes with AUC >0.75 were selected as candidate genes (Figure 3(f)). Finally, PLA2G4D, IFI6, AGR3, IGFL1, SPRR3, ATP13A5, SERPINB13, KRT16, HAS3, and CH25H were recognized as candidate genes and may be able to diagnose AD.Figure 3
The LASSO regression prediction model based on common genes. (a) Selection of parameter (λ) in the LASSO model according to optimal criteria. (b) Construction of LASSO model using 18 feature genes and their coefficients. The ROC curves of 18 feature genes in GSE121212 (c), GSE130588 (d), and GSE157194 (e) datasets predicting the occurrence of AD. AUC, area under ROC curve. (f) Intersection of feature genes with AUC >0.75 in GSE121212, GSE130588, and GSE157194 datasets.
(a)(b)(c)(d)(e)(f)According to the logistic regression analysis results, we found that PLA2G4D, CH25H, and IFI6 were risk factors in all GSE121212, GSE130588 and GSE157194 datasets (Figure4).Figure 4
The forest plots of candidate genes in GSE121212, GSE130588, and GSE157194 datasets.
(a)(b)


### 3.4. Immune Cells and Pathways in AD

To identify the infiltration of immune cells in AD patients, we analyzed the expression of marker genes for immune cells in AD and controls. The results found that there is a significant difference in the level of immune cell infiltration between AD and control (Figures5(a)–5(c)). By comparing the activation differences of immune-related signaling pathways between AD and control, we also identified abnormal pathways in AD (Figures 5(d)–5(f)). This further suggests that there are a large number of abnormal immune responses in AD patients. Subsequently, we conducted correlation analysis to evaluate the relationship between candidate genes and immune cells and signaling pathways. There is a positive correlation between PLA2G4D, CH25H, and IFI6 and the level of immune cell infiltration (Figure 6(a)). PLA2G4D, CH25H, and IFI6 are also positively correlated with the activation of immune-related signaling pathways (except for TGFb family member and TGFb family member receptor) (Figure 6(b)). This suggests that PLA2G4D, CH25H, and IFI6 as risk factors for AD may be related to the promotion of immune activation.Figure 5
Infiltration of immune cells and activation of immune-related pathways in AD. Differential infiltration of immune cells between AD and controls in GSE121212 (a), GSE130588 (b), and GSE157194 (c) datasets. Differences of immune-related functions in the ImmPort database between AD and controls in GSE121212 (d), GSE130588 (e), and GSE157194 (f) datasets.P∗<0.05; P∗∗<0.01; P∗∗∗<0.001.
(a)(b)(c)(d)(e)(f)Figure 6
Correction of candidate genes and immune cells and pathways in AD. (a) The correction of candidate genes and immune cells in AD for the GSE157194 dataset. (b) The correction of candidate genes and immune-related pathways in AD for the GSE157194 dataset.
(a)(b)


## 3.1. Differentially Expressed Genes in AD

In order to identify abnormally expressed genes related to AD, we performed a differential expression analysis of AD and control genes. In the GSE121212 dataset, we have identified 4651 DEGs (Figure1(a)). We have identified 554 DEGs in the GSE130588 dataset (Figure 1(b)). We identified 1962 DEGs in the GSE157194 dataset (Figure 1(c)). Among them, we obtained a total of 60 common genes (Figure 1(d)). The expression of common genes in AD and control is significantly different, and they may be abnormally expressed genes related to AD (Figure 1(e)).Figure 1
Identification of differentially expressed genes in AD. Differentially expressed genes between AD and controls were identified in the GSE121212 dataset (a), GSE130588 dataset (b), and GSE157194 dataset (c). Red are upregulated genes and blue are downregulated genes. (d) Common genes for the two groups of differentially expressed genes. A total of 60 common genes were found. (e) Heatmap of common genes in AD and control samples of the GSE157194 dataset. Red are upregulated genes and blue are downregulated genes.
(a)(b)(c)(d)(e)


## 3.2. Enrichment Analysis of Common Genes

In order to identify the potential molecular dysregulation mechanism of AD, we performed enrichment analysis on common genes. The GO results (Figure2(a)) suggested that defense response to bacterium, negative regulation of endopeptidase activity, and negative regulation of peptidase activity of biological processes (BP) were mainly enriched by common genes. The secretory granule lumen, cornified envelope, and specific granule lumen of the cellular components were also enriched. As well as serine-type endopeptidase inhibitor activity, endopeptidase inhibitor activity, and RAGE receptor binding of molecular function were mainly enriched. In addition, the IL-17 signaling pathway, Th17 cell differentiation, and complement and coagulation cascades of KEGG pathways were mainly signature enriched by common genes (Figure 2(b)).Figure 2
The GO and KEGG pathways enriched by common genes. (a) Top 10 biological functions of common genes significantly enriched. The BP, CC, and MF included in GO enrichment. (b) KEGG signaling pathways with significant involvement of common genes.
(a)(b)


## 3.3. Candidate Genes of AD

In order to identify diagnostic candidate genes that can predict the occurrence of AD, we performed LASSO regression analysis on common genes. Based on the bestλ value, we identified 18 feature genes could predict AD most accurately (Figures 3(a) and 3(b)). The AUC values for feature genes were calculated and found the AUC values were great than 0.5 (Figures 3(c)–3(e)). Among GSE121212, GSE130588, and GSE157194 datasets, the genes with AUC >0.75 were selected as candidate genes (Figure 3(f)). Finally, PLA2G4D, IFI6, AGR3, IGFL1, SPRR3, ATP13A5, SERPINB13, KRT16, HAS3, and CH25H were recognized as candidate genes and may be able to diagnose AD.Figure 3
The LASSO regression prediction model based on common genes. (a) Selection of parameter (λ) in the LASSO model according to optimal criteria. (b) Construction of LASSO model using 18 feature genes and their coefficients. The ROC curves of 18 feature genes in GSE121212 (c), GSE130588 (d), and GSE157194 (e) datasets predicting the occurrence of AD. AUC, area under ROC curve. (f) Intersection of feature genes with AUC >0.75 in GSE121212, GSE130588, and GSE157194 datasets.
(a)(b)(c)(d)(e)(f)According to the logistic regression analysis results, we found that PLA2G4D, CH25H, and IFI6 were risk factors in all GSE121212, GSE130588 and GSE157194 datasets (Figure4).Figure 4
The forest plots of candidate genes in GSE121212, GSE130588, and GSE157194 datasets.
(a)(b)


## 3.4. Immune Cells and Pathways in AD

To identify the infiltration of immune cells in AD patients, we analyzed the expression of marker genes for immune cells in AD and controls. The results found that there is a significant difference in the level of immune cell infiltration between AD and control (Figures5(a)–5(c)). By comparing the activation differences of immune-related signaling pathways between AD and control, we also identified abnormal pathways in AD (Figures 5(d)–5(f)). This further suggests that there are a large number of abnormal immune responses in AD patients. Subsequently, we conducted correlation analysis to evaluate the relationship between candidate genes and immune cells and signaling pathways. There is a positive correlation between PLA2G4D, CH25H, and IFI6 and the level of immune cell infiltration (Figure 6(a)). PLA2G4D, CH25H, and IFI6 are also positively correlated with the activation of immune-related signaling pathways (except for TGFb family member and TGFb family member receptor) (Figure 6(b)). This suggests that PLA2G4D, CH25H, and IFI6 as risk factors for AD may be related to the promotion of immune activation.Figure 5
Infiltration of immune cells and activation of immune-related pathways in AD. Differential infiltration of immune cells between AD and controls in GSE121212 (a), GSE130588 (b), and GSE157194 (c) datasets. Differences of immune-related functions in the ImmPort database between AD and controls in GSE121212 (d), GSE130588 (e), and GSE157194 (f) datasets.P∗<0.05; P∗∗<0.01; P∗∗∗<0.001.
(a)(b)(c)(d)(e)(f)Figure 6
Correction of candidate genes and immune cells and pathways in AD. (a) The correction of candidate genes and immune cells in AD for the GSE157194 dataset. (b) The correction of candidate genes and immune-related pathways in AD for the GSE157194 dataset.
(a)(b)


## 4. Discussion

In this study, we identified 60 common genes in the GSE121212, GSE130588, and GSE157194 databases. Through analysis, we finally determined that PLA2G4D, CH25H, and IFI6 can be used as candidate genes for AD diagnosis. Further studies have shown that PLA2G4D, CH25H, and IFI6 are related to the level of immune cell infiltration and the activation of immune-related signal pathways.In the past few decades, researchers have conducted a lot of analysis on the occurrence of AD, which has important implications for the diagnosis, prevention, and treatment of AD. At the same time, the occurrence of AD is accompanied by changes in gene function and forms a complex network of effects. This also provides a unique opportunity for the detection methods and systems for the diagnosis and prevention of AD. However, researchers have done a lot of analysis on the differential genes when AD occurs [17]. Up to now, there are still no specific detection markers and detection methods for AD. Therefore, the reanalysis of previous studies may give us a new understanding of the mechanism of AD. In our analysis, we identified a total of 60 common genes. The analysis of these genes may have a reference value for the diagnosis of AD in the future.On further analysis of these common genes, we found the significantly enriched IL-17 signaling pathway and Th17 cell differentiation signaling pathway. Current research shows that IL-17 is a type of cytokine produced by Th17 cells [18]. IL-17 can induce the production of proinflammatory cytokines, chemokines, and antimicrobial peptides [19]. At the same time, Th17 cells and cytokine IL-17 are also involved in allergic diseases [20]. IL-17 antagonists have been approved to treat patients with psoriasis [21]. In studies on AD, patients with AD and children have significantly higher levels of Th17 cells and IL-17 in peripheral blood [22]. Subsequently, Nick et al. showed that it was proved that the type of IL-17, IL-17C, is the central mediator of AD [23]. This also further suggests the role of the IL-17 signaling pathway in the occurrence of AD.Through further analysis, we finally determined that PLA2G4D, CH25H, and IFI6 can be used as potential detection targets for AD. CH25H is a rate-limiting enzyme, which mainly plays a rate-limiting role in the synthesis of oxysterol 7α, 25-dihydroxycholesterol from cholesterol [24]. Studies have shown that CH25H can exert its anti-inflammatory function by activating CD44+ CD4+T cells to migrate to inflamed tissues [25]. Michael’s research shows that the imbalance of CH25H expression can lead to chronic inflammation and cancer [26]. However, CH25H has no effect on the expression of IL-17 [27]. In the study of Vittorio et al., CH25H can be used as a diagnostic marker to distinguish allergic and irritant contact dermatitis [27]. PLA2G4D and IFI6 have been identified as biomarkers of psoriasis [28, 29]. Meanwhile, PLA2G4D can induce the expression of IL-17 [28]. The expression level of IFI6 is also closely related to the inflammatory response [30]. In addition, we found that PLA2G4D, CH25H, and IFI6 are related to the activation of immune-related signal pathways and immune cell infiltration.


## 5. Conclusion

In related studies, PLA2G4D and IFI6 are highly expressed in immune cells that infiltrate tissues [28, 31]. It is closely related to the infiltration of mast cells in inflamed tissues [31]. This also further shows that the occurrence of AD is closely related to immune infiltration.In summary, our results indicate that the occurrence of AD is related to abnormal immune activation. In addition, PLA2G4D, CH25H, and IFI6 can be used as biomarkers of AD.



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*Source: 8745722-2022-01-04.xml*