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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 40  |  Issue : 3  |  Page : 162-167

Identification of immune-related genes in atopic dermatitis, contact dermatitis, and psoriasis: A bioinformatics analysis


1 Department of Dermatology Clinnic, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin Province, China
2 Department of Pain, Jilin Academy of Chinese Medicine Sciences, Changchun, Jilin Province, China
3 College of Basic Medicine, Changchun University of Chinese Medicine, Changchun, Jilin Province, China

Date of Submission29-Nov-2021
Date of Decision22-Apr-2022
Date of Acceptance25-Apr-2022
Date of Web Publication05-Aug-2022

Correspondence Address:
Dr. Lei Gao
College of Basic Medicine, Changchun University of Chinese Medicine, 1035 Boshuo Road, Jingyue District, Changchun City, Jilin Province, 130117
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ds.ds_26_22

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  Abstract 


Background: The exact mechanisms and targeted therapies for atopic dermatitis (AD), contact dermatitis (CD), and psoriasis (PS) remain unknown. Objectives: This study aimed to identify the biomarkers related to immune and novel therapeutic drugs for AD, CD, and PS. Methods: The GSE153007 dataset including 12 AD, 9 CD, and 14 PS samples and 40 control samples, which was obtained from the Gene Expression Omnibus database and analyzed. The immune infiltration level of each sample was then evaluated using the single-sample gene set enrichment analysis (ssGSEA). Then, we screened for immune-related differentially expressed genes (DEGs) that overlapped. The Database for Annotation, Visualization, and Integrated Discovery database was used to perform the gene ontology (GO) biological process. Furthermore, using search tool for the retrieval of interaction gene (STRING), the protein-protein interaction (PPI) was predicted on immune-related DEGs. We also searched the DGIdb database for novel therapeutic drugs for AD, CD, and PS. Results: According to ssGSEA results, most immune cells were highly infiltrated in the disease group. GO analysis indicated that AD, CD, and PS were enriched in signal transduction, inflammatory response, immune response, and innate immune response. We further found hub genes related to AD (CD4, ITGAM), CD (CD8A, CD86), and PS (CD4, CD8A) from PPI network. Moreover, the drug prediction indicated that drugs targeting CSF1R was the most effective for AD, whereas drugs targeting FCGR3A and CD86 were more effective for CD and PS. Conclusion: These immune-associated genes such as FCGR3A, CD86, and CSF1R might be regarded as therapeutic targets for patients with AD, CD, and PS.

Keywords: Atopic dermatitis, contact dermatitis, immune infiltration, protein-protein interaction, psoriasis


How to cite this article:
Zhang L, Wang HL, Tian XQ, Liu WL, Hao Y, Gao L. Identification of immune-related genes in atopic dermatitis, contact dermatitis, and psoriasis: A bioinformatics analysis. Dermatol Sin 2022;40:162-7

How to cite this URL:
Zhang L, Wang HL, Tian XQ, Liu WL, Hao Y, Gao L. Identification of immune-related genes in atopic dermatitis, contact dermatitis, and psoriasis: A bioinformatics analysis. Dermatol Sin [serial online] 2022 [cited 2022 Nov 30];40:162-7. Available from: https://www.dermsinica.org/text.asp?2022/40/3/162/353417




  Introduction Top


Atopic dermatitis (AD), contact dermatitis (CD), and psoriasis (PS) are the type of inflammatory skin diseases that affect the millions of people worldwide, resulting in a lower quality of life.[1],[2] Inflammatory diseases of the skin, as we all know, make the human body more susceptible to bacterial and viral infections because the skin is the first natural barrier of the immune system.[3] Furthermore, AD, CD, and PS are associated with immune responses, which are triggered by adaptive immune responses as well as metal ions and chemicals that exert toxic effects.[4] The precise mechanisms and targeted therapies of these diseases need to be further explored. Therefore, it is critical to investigate the role of immune cells and their associated target genes in the development of effective therapies for patients suffering from AD, CD, and PS.

Recent evidence suggests that immune dysregulation is an important avenue for understanding the pathogenesis of AD, CD, and PS.[5],[6],[7] Previous research has shown that the interaction of various immune cells, such as monocytes, eosinophils, and T-cells, results in some inflammatory diseases such as AD and CD.[2] Furthermore, Lowes et al.[8] reported that PS was caused by innate immunity and T-lymphocyte infiltration. In this situation, it is critical to investigate and screen immune-related genes to develop effective therapies for AD, CD, and PS. Peng et al.[9] discovered that several novel genes including S100A7, S100A8, S100A9, and LCE3D could be potential therapeutic targets for AD. Garzorz-Stark et al. indicated that IL-23 and IL 17 are important immune-related cytokines that affect the development of CD and may be important targets for treating CD.[10] Furthermore, Luo et al.11] found that some PS-related genes such as SPRR genes, HSD11B1, IVL, and CXCL10, may be novel target genes for treating patients with PS. All of these findings suggest that novel immune-associated biomarkers are important in strategies for treating AD, CD, and PS. However, preventing the occurrence and recurrence of these skin diseases remains a challenge. Therefore, more effective immune biomarkers would aid in understanding the mechanism of immune response and the development of new drugs for patients with AD, CD and PS.

The goal of our research was to explore the novel immune-related biomarkers and its targeted drugs for AD, CD, and PS. We first used single-sample gene set enrichment analysis (ssGSEA) to calculate the proportion of immune cell infiltration in samples from AD, CD, and PS to identify immune-related differentially expressed genes (DEGs). The protein-protein interaction (PPI) networks were then predicted in order to identify the hub genes. In addition, we investigated drugs that target the hub genes in order to find targeted drugs for the treatment of AD, CD, and PS, and we provided the basis for the drug treatment of these three skin diseases.


  Materials and Methods Top


Data source and processing

The GSE153007 dataset was obtained from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)[12] database, which included 12 AD samples, 9 CD samples, 14 PS samples, and 40 control samples. The damaged area was subjected to a tissue biopsy. The median value was used to compare multiple probes that were aligned to the same gene. The probe ID was then exchanged for the official gene symbol using the GPL341 annotation file downloaded from Affymetrix. Furthermore, the normalize Between Arrays function[13] of limma (3.50.0) in R package[13] was used to standardize data. [Supplementary Table 1] (http://106.12.131.133:8888/down/xrgQp5hCnQ5i) contains information about the samples.

Immune infiltration analysis of chip dataset

We used the ssGSEA score[14] in R (version 4.1.0) to determine the level of immune infiltration in each sample dataset based on the expression level of immune cell-specific marker genes. The immune cell-related gene set was defined and the enrichment score of this gene set represented the density of tumor-infiltrating immune cells. Furthermore, we obtained 28 immune cell marker genes from the Charoentong et al.,[15] including 28 common immune cells and 782 genes.

Identification and functional enrichment analysis of immune-related differentially expressed genes

To identify the DEGs, the Wilcoxon rank-sum test was carried out to screen out the DEGs between disease and normal samples. The P value was corrected using the Benjamini-Hochberg (BH) method, and we used an adjusted P value threshold of <0.05. The Python language (version 3.6.8) was then used to define DEGs that overlap with immune cell-specific marker genes as immune-related DEGs, specifically, the “and” symbol in the python language was used to obtain the intersection of two sets. Moreover, we uploaded the identified immune-related genes of AD, CD, and PS from the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database (https://david.ncifcrf.gov/)[16] to investigate the functional enrichment of immune-related DEGs (P < 0.05), specifically on the biological process gene ontology (GO).

Construction of protein-protein internetwork

To investigate the interactions between immune-related DEGs in AD, CD, and PS, we predicted PPI networks for AD, CD, and PS using the STRING database.[17] The cutoff was set to the combined score >0.7. The PPI network was visualized using Cytoscape (version 3.8.0) and the cytoHubba plugin was used to rank genes within this network based on their degree centrality values.

Related drug predictions

To find targeted drugs for AD, we predicted the top ten genes with the highest degree to the DGIdb database (http://dgidb.genome.wustl.edu/)[18], which is a web resource for discovering drug-gene interactions. Preset filters included “Food and Drug Administration Approved” and “Immunotherapies,” while advanced filters include 22 databases, 43 gene categories, and 31 gene interaction types. The drug-targeted gene interaction pairs that were supported by the previous literatures were retained and visualized using Cytoscape (version 3.8.0).

Statistical analysis

We used Wilcoxon rank-sum test to identify DEGs between disease and normal groups. The P value was adjusted using the R language's “p. adjust” function, and the method was “BH.” The Wilcoxon rank-sum test was used to compare the enrichment scores of 28 immune cells between the disease and normal groups. R language (version 4.1.0) and Python (version 3.8.1) were used for all statistical analyses. A two-tailed P < 0.05 was considered a significant difference.


  Results Top


Immune infiltration landscape of allergic dermatitis, contact dermatitis, and psoriasis

We used ssGSEA to calculate the enrichment scores of 28 immune cells in each sample to compare the immune cell infiltration of AD, CD, PS, and normal skin tissues. [Figure 1] depicts the difference in immune cell infiltration between the three diseases and normal. We discovered that the majority of immune cells were highly infiltrated in the disease group, implying that AD, CD, and PS were all related to the increased immune cell infiltration [Figure 1]. Furthermore, the bubble chart showed the difference in 28 immune cell infiltration between disease groups [Figure S1], indicating that CD had the most types of highly infiltrating immune cells and PS had the fewest types of highly infiltrating immune cells.
Figure 1: The box plot of score of ssGSEA in immune cell infiltration. (a) atopic dermatitis; (b) contact dermatitis; (c) psoriasis. X-axis represents immune cell names, Y axis represents ssGSEA score. ssGSEA: Single-sample gene set enrichment analysis, *indicates P<0.05; **indicates P<0.01; ***indicates P<0.001; ns: indicates P>0.05.

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Identification and functional enrichment analysis of immune-related differentially expressed genes

We first screened the DEGs using Wilcoxon rank sum test and then extracted DEGs that overlapped with immune genes. [Supplementary Table 2] (http://106.12.131.133:8888/down/CrmASg4tcW1J) contains a list of all the DEGs associated with immunity. In addition, the biological process of immune-related DEGs enrichment in AD, CD, and PS using DAVID database. [Supplementary Table 3] (http://106.12.131.133:8888/down/kQLsCf80yFe2) displays the detailed results. We demonstrated that AD, CD, and PS were enriched in signal transduction, inflammatory response, immune response, and innate immune response [Figure 2]. These results suggested that AD, CD, and PS were all associated with inflammation and immune response.
Figure 2: The bar plot of biological process enriched by immune-related DEG. (a) atopic dermatitis; (b) contact dermatitis; (c) psoriasis. DEG: Differentially expressed genes.

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Construction of protein network and identification of hub genes

The PPI networks were predicted using the STRING database to investigate the protein interactions of immune-related DEGs.[12] Then, we used the cytoHubba plug-in to screen out the top 10 immune-related DEGs of AD, CD, and PS as the hub genes. As shown in [Figure 3], the hub genes of AD included CD4, ITGAM, and GRB2; the hub genes of CD were CD8A, CD86, ITGB1, and FCGR3A; the hub genes of PS were CD4, CD8A, GRB2, and ITGB1. CD2 was discovered in the PPI networks of AD, CD, and PS that involved inflammation.
Figure 3: The top 10 genes with the highest degree values were identified using CytoHubba. These genes were ranked in descending degree order from red to orange to yellow. (a) atopic dermatitis; (b) concatc dermatitis; (c) psoriasis.

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Drug prediction targeting hub genes

Using the DGIdb database, we look for drugs that target the hub gene of AD, CD, and PS. Then, we only keep the drug-genes interactions that have been validated by previous research. As shown in [Figure 4], the drug-gene network of AD identified seven candidate drugs, such as CSF1R (imatinib, sorafenib, sunitinib), CD2 (alefacept). The study discovered 22 candidate drugs for CD, including FCGR3A (etanercept, tocilizumab, prednisolone, adalimumab, rituximab, infliximab, trastuzumab, doxorubicin, indomethacin, thalidomide, and cyclosporine), ITGB2 (methylprednisolone, prednisone, colchicine, and cyclophosphamide). In addition, we discovered 17 PS-related drugs, such as FCGR3A (thakudimide, prednisolone, rituximab, doxorubicin, tocilizumab, cyclosporine, infliximab, trastuzumab, etanercept, indomethacin, and adalimumab).
Figure 4: The drug-hub gene interaction network. The blue triangle represents drugs, while the yellow circle represents the hub genes. (a) atopic dermatitis; (b) concatc dermatitis; (c) psoriasis.

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  Discussion Top


The most common immune-associated inflammatory skin diseases include AD, CD, and PS.[19] The discovery of novel biomarkers for AD, CD, and PS may provide a rationale for the treatment of these skin diseases. Using the ssGSEA method, we investigated the immune cell infiltration of AD, CD, and PS, indicating that these diseases were associated with high immune cell infiltration. Then, using the Wilcoxon rank-sum test and the GSE153007 dataset from the GEO database, we screened for immune-related DEGs. According to the GO function analysis, skin diseases were enriched in immune-related GO terms, such as signal transduction, inflammatory response, immune response, and innate immune response. Moreover, we discovered several hub genes and predicted hub gene-drug networks. These findings could pave the way for a new approach for the effective treatment of these skin diseases.

Previous research has suggested that these skin diseases (AD, CD, and PS) are caused by the interaction of various immune cells (mast cells, eosinophils, T-cells, and a small number of macrophages) and their chemokines.[19] In our study, we discovered that the majority of immune cells, such as mast cells, immature B-cells, and regulatory T-cells, were infiltrated in the disease group, indicating that AD, CD, and PS were associated with immune cell infiltration. Besides, we investigated the GO analysis of immune-related DEGs, which indicated that skin diseases were significantly associated with inflammation and immune response. These findings indicated that immune cells and immune-related GO terms played critical roles in the development of skin diseases.

Based on the top 10 DEGs, we built the PPI networks for AD, CD, and PS. CD2 was increasingly involved in inflammation response among the hub genes of three skin diseases. CD2 molecules may enhance cell adhesion, and signal transduction by promoting antigens recognition by T-cells.[20] In addition, a previous study suggested that CD2 could be a potential target for preventing the development of pro-inflammatory Th1 cells in the skin.[21] As a result, we hypothesized that CD2 could be an important biomarker for treating patients with AD, CD, and PS. In addition, we built hub gene-drug networks of AD, CD, and PS. The results indicated that drugs targeting CSF1R were the most effective for AD, while drugs targeting FCGR3A and CD86 were more effective for CD and PS. CSF1R inhibitors were identified as a novel type of immune-modulatory drug that could reduce the clearance of extracellular matrix components and improve skin diseases.[23] CSF1R was also involved in immune response, which could be a key target for skin disease[24],[22] Macrophages may trigger skin inflammation by disrupting the integrity of the basement membrane through the CSF1R.[23] Furthermore, high FCGR3A expression in MARCO macrophages was closely related to severe skin disease.[24] CD86, a co-stimulatory molecule on dendritic cells, is a critical event in initiating an antigen-specific immune response.[25] Previous researches have reported that CD86 might be a promising biomarker for the treatment of allergic or AD.[26],[27] Previous research has also linked abnormal CD86 expression to CD,[28] as well as PS.[29] All of these perspectives suggested that CSF1R, FCGR3A, and CD86 could be effective therapies for patients with AD, CD, and PS.

However, our study still had some limitations. First, the AD, CD, and PS samples were all small. Our study only analyzed the immune cell infiltration using ssGSEA, which should be validated using tissue-based flow cytometry. Moreover, we have screened out some drugs, some of which may not be combined with clinical practice at present. In the future, we will combine clinical practice to conduct in-depth research on drugs related to these three skin diseases. Finally, immune-related DEGs such as CSF1R, FCGR3A, and CD86 should be tested using quantitative real-time polymerase chain reaction or immunohistochemistry.


  Conclusion Top


In conclusion, we discovered that some immune-related genes such as FCGR3A, CD86, and CSF1R were considered as novel biomarkers for AD, CD, and PS for the treatment of patients with AD, CD, and PS. Furthermore, we discovered that some targeted drugs, such as imatinib, etanercept, methylprednisolone, and thalidomide may pave the way for the development of effective therapies for patients with AD, CD, and PS.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Supplementary Materials





 
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