eISSN: 2299-0046
ISSN: 1642-395X
Advances in Dermatology and Allergology/Postępy Dermatologii i Alergologii
Current issue Archive Manuscripts accepted About the journal Editorial board Reviewers Abstracting and indexing Subscription Contact Instructions for authors Ethical standards and procedures
SCImago Journal & Country Rank
 
6/2022
vol. 39
 
Share:
Share:
more
 
 
Original paper

Integrated bioinformatics-based identification of potential diagnostic biomarkers associated with atopic dermatitis

Guanghua Chen
1
,
Jia Yan
2

1.
Department of Dermatology, Children’s Hospital of Chongqing Medical University, National Clinical Research Centre for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
2.
Digestive Department, University-Town Hospital of Chongqing Medical University, Chongqing, China
Adv Dermatol Allergol 2022; XXXIX (6): 1059-1068
Online publish date: 2022/03/27
Article file
- Integrated.pdf  [0.50 MB]
Get citation
ENW
EndNote
BIB
JabRef, Mendeley
RIS
Papers, Reference Manager, RefWorks, Zotero
AMA
APA
Chicago
Harvard
MLA
Vancouver
 
 

Introduction

Atopic dermatitis (AD) is a chronic and refractory skin disease with typical clinical manifestations of repeated itching, skin erythema, dryness, thickening, and swelling [13]. About 15–30% of children and 2–10% of adults worldwide are affected by atopic dermatitis [4, 5]. With the maturity of gene sequencing technology, studies have shown that there are abnormally expressed genes in the skin of patients with atopic dermatitis, but the relative importance of each gene is still unclear. Therefore, it is necessary to determine the sensitive and specific biomarkers that affect the progression of atopic dermatitis and clarify the relevant molecular mechanisms to provide a predictive prognosis and develop new targeted treatment strategies.

Gene expression microarrays have been widely applied in atopic dermatitis research and represent an important new tool for use in the identification of disease-related molecules associated with atopic dermatitis. recently, comprehensive analysis of microarray data from multiple centres has become a popular research area. Mining hub genes through bioinformatics analysis is a new method to study the pathogenesis of complex diseases [6, 7]. In this study, we downloaded 3 gene datasets (GSE121212, GSE5667, and GSE120721) from the GEO database, and screened and compared them. Differentially expressed genes (DEGs) were identified by comparing the diseased skin lesions of patients with atopic dermatitis vs. normal skin of healthy patients in each dataset. A series of analyses of these differentially expressed genes, including GO enrichment, KEGG pathway analysis, PPI network analysis, and cell test, predicted 9 hub genes and related pathways.

Material and methods

Data source

The original datasets of GSE121212 [8], GSE5667 [9, 10], and GSE120721 [11] were downloaded from the National Centre of Biotechnology Information (NCBI)-GEO database (https://www.ncbi.nlm.nih.gov/geo/), which includes a large number of tumour and non-tumour sequencing and microarray-based datasets. We compared lesions from AD patients with normal patients’ skin.

Identification of DEGs

The differentially expressed genes(DEGs)of GSE5667, GSE120721 were performed by default setting of the GEO2R tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/), an online analytic tool provided by the GEO database,was employed to perform differential expression analysis. Firstly, enter GSE5667 and GSE120721 in the GEO accession box respectively. Secondly, we divided samples in each above dataset into two groups, namely skin lesional AD group (AD) and skin normal health group (Nor). Then, differential expression analysis was done between “AD” group and “Nor” group. Because GEO2R could not analyze GSE121212, we used the “DESeq2” package of R software to analyze the differential expression for GSE121212 in the same grouping method. DEGs that with |log2 FC| > 1 and p < 0.05 were considered statistically significant were visualized using the “Pheatmap” package of R software. Scatter charts were plotted using GraphPad Prism 7.0.

GO and KEGG pathway analyses

We used the “ClusterProfiler-GOplot and ggplot2” R package to analyse and visualize the GO and KEGG pathway enrichment of the obtained DEGs [12, 13] (p-value < 0.05 and FDR < 0.01 were considered statistically significant). For GO analyses, enriched biological processes (BPs), molecular functions (MFs), and cellular components (CCs) were assessed.

Protein-protein interaction (PPI) network and hub gene analyses

To understand interactions among DEGs, the Search Tool for the Retrieval of Interacting Genes (STRING) [14] (https://string-db.org) constructed PPI networks by importing upregulated DEGs. Cytoscape (v 3.8.2) was used to visualize the network, while the cyto-Hubba plugin was used to rank genes within this network based upon their score. Hub genes, which were considered to be involved in playing pivotal regulatory roles in the PPI network, were those with the top 9 highest score values.

Results

Identification of DEGs

The GSE121212, GSE5667, and GSE120721 gene expression datasets were downloaded from the GEO datasets obtained from the GEO database. In total, 4231 DEGs were identified in the GSE121212 dataset (1884 upregulated, 2347 downregulated), 1381 were identified in the GSE5667 dataset (811 upregulated, 570 downregulated), and 5020 were identified in the GSE120721 dataset (2395 upregulated, 2625 downregulated). The top 100 DEGs with the highest p-values are presented in Figure 1. Ninety-one upregulated DEGs and 55 downregulated DEGs were shared between these 3 datasets, as identified through Venn diagram analyses (Figure 2, Table 1).

Table 1

Common upregulated/downregulated DEGs identified among GSE121212, GSE5667, and GSE120721

TypeGene name
Upregulated DEGsXAF1, ULBP2, TNFRSF10A, TMPRSS4, TEX101, TACC3, SOCS3, SLC4A7, SLAMF8, SLAMF7, SGIP1, SERPINB4, SERPINB3, SERPINB13, SELPLG, SCO2, SAMD9, S100A9, S100A8, S100A7A, S100A7, RRM2, RHOH, RGS1, PTPRC, PRSS53, PI3, PARP9, P2RY2, P2RY10, OAS3, OAS2, OAS1, NABP1, MPZL2, MMP19, MMP12, MFHAS1, LILRB2, LCP2, LCK, KYNU, KRT16, KLHDC7B, IL7R, IL4R, IL32, IL27RA, IKZF1, IGFL1, IGFBP3, IFI27, HPSE, HAS3, GZMB, GPR171, FOXM1, FOSL1, FGD2, FCHSD1, EPSTI1, DSC2, CTSC, CTLA4, CST7, COTL1, COL6A6, COL4A4, CLEC7A, CIITA, CDH3, CD83, CD52, CD36, CD274, CD1C, CD1B, CCL5, CCL22, CCL17, CCL13, CA2, C10orf99, BIRC3, BATF3, APOL6, APOL2, ANGPTL4, AKR1B10, ADAMDEC1, ADAM8
Downregulated DEGsSORBS1, MSMB, KRT15, PEG3, ID4, IL37, CHP2, IL20RA, CYP39A1, SCEL, GPD1, F3, BTC, RHOBTB3, CPE, WIF1, FBLN1, RERGL, OGN, GLRB, GPM6A, FHOD3, MMP16, BCAR3, GPLD1, AGR2, HSD11B1, TF, ADH1B, SPINK1, HMGCS2, RAB3B, CYP2J2, TMEM255A, KRT77, WNT2B, CNKSR2, CNTN4, CLDN23, HRCT1, KANK4, C14orf132, GAN, SLC46A2, KLF15, TMEM132C, RASSF6, ENPP5, FGFBP2, SOX6, CHRM1, MGST1, PDK4, RORC, THRSP
Figure 1

Differentially expressed genes (DEGs) in the 3 datasets. A–C – The upregulated DEGs (red dots) and downregulated DEGs (blue dots) of each dataset were identified with the use of criteria of p < 0.05 and |log2FC| > 1. D–F – Expression heatmaps of the top 100 DEGs in the 3 datasets, as determined based upon p-values

/f/fulltexts/PDIA/46718/PDIA-39-46718-g001_min.jpg
Figure 2

Identification of shared DEGs. A – DEGs upregulated in all 3 datasets. B – DEGs downregulated in all 3 datasets

/f/fulltexts/PDIA/46718/PDIA-39-46718-g002_min.jpg

GO and KEGG pathway analyses

GO analyses revealed these DEGs to be enriched in biological processes including immune system process, immune response, defence response, leukocyte activation, response to biotic stimulus, molecular functions including 2’–5’-oligoadenylate synthetase activity, Toll-like receptor binding, RAGE receptor binding, enzyme inhibitor activity, chemokine receptor binding and cellular components including the extracellular region, secretory granule, plasma membrane part, cytoplasmic vesicle, and intracellular vesicle. These DEGs were also enriched in KEGG pathways, including influenza A, amoebiasis, primary immunodeficiency, cytokine-cytokine receptor interaction, and IL-17 signalling pathway (Table 2, Figure 3). Enrichment results of these analyses are compiled in Figure 4.

Table 2

GO and KEGG pathway enrichment analyses for module genes. The top 5 terms were selected based upon p-value rankings when > 5 terms were enriched for a given category

IDDescriptionCountP-valueGene ID
BPGO:0002376Immune system process569.7877E-23XAF1/ULBP2/TNFRSF10A/SOCS3/SLAMF8/SLAMF7/SERPINB4/SERPINB3/SELPLG/S100A9/S100A8/S100A7/RHOH/RGS1/PTPRC/PI3/PARP9/OAS3/OAS2/OAS1/MMP12/MFHAS1/LILRB2/LCP2/LCK/KYNU/KRT16/IL7R/IL4R/IL32/IL27RA/IKZF1/IFI27/HPSE/GZMB/GPR171/CTSC/CTLA4/CST7/COTL1/CLEC7A/CIITA/CD83/CD36/CD274/CD1C/CD1B/CCL5/CCL22/CCL13/CA2/C10orf99/BIRC3/BATF3/ADAMDEC1/ADAM8
BPGO:0006955Immune response482.7208E-22XAF1/ULBP2/SOCS3/SLAMF8/SLAMF7/SERPINB4/SERPINB3/S100A9/S100A8/S100A7/RGS1/PTPRC/PI3/PARP9/OAS3/OAS2/OAS1/MMP12/MFHAS1/LILRB2/LCP2/LCK/KYNU/KRT16/IL7R/IL4R/IL32/IL27RA/IFI27/HPSE/GZMB/CTSC/CTLA4/CST7/COTL1/CLEC7A/CIITA/CD83/CD36/CD274/CD1C/CD1B/CCL5/CCL22/CCL13/BIRC3/ADAMDEC1/ADAM8
BPGO:0006952Defence response373.0894E-16XAF1/ULBP2/SOCS3/SLAMF8/SLAMF7/SERPINB4/S100A9/S100A8/S100A7/PTPRC/PARP9/OAS3/OAS2/OAS1/MMP12/MFHAS1/LILRB2/KYNU/KRT16/IL4R/IL32/IL27RA/IFI27/GZMB/FOSL1/CTSC/CST7/COTL1/CLEC7A/CIITA/CD83/CD36/CCL5/C10orf99/BIRC3/APOL2/ADAM8
BPGO:0045321Leukocyte activation311.8885E-15ULBP2/SLAMF8/SLAMF7/SERPINB3/SELPLG/S100A9/S100A8/S100A7/RHOH/PTPRC/MFHAS1/LILRB2/LCP2/LCK/IL7R/IL4R/IL27RA/IKZF1/HPSE/CTSC/CTLA4/CST7/COTL1/CLEC7A/CD83/CD36/CD274/CD1C/CCL5/BATF3/ADAM8
BPGO:0009607Response to biotic stimulus272.0661E-14SLAMF8/S100A9/S100A8/S100A7/RGS1/PTPRC/PI3/PARP9/OAS3/OAS2/OAS1/MMP12/LILRB2/IL4R/IL27RA/IFI27/FOSL1/COTL1/CLEC7A/CD36/CD274/CCL5/CCL22/CCL13/C10orf99/BIRC3/BATF3
CCGO:0005576Extracellular region436.22E-08ULBP2/TEX101/SERPINB3/SERPINB13/S100A9/S100A8/S100A7/PTPRC/PRSS53/PI3/OAS3/OAS1/MMP19/MMP12/LILRB2/LCK/KRT16/IL7R/IL4R/IL32/IGFL1/IGFBP3/HPSE/HAS3/DSC2/CTSC/CST7/COTL1/COL6A6/COL4A4/CD52/CD36/CD274/CCL5/CCL22/CCL13/CA2/C10orf99/APOL6/APOL2/ANGPTL4/AKR1B10/ADAMDEC1
CCGO:0030141Secretory granule142.01E-05TMPRSS4/TEX101/SERPINB3/S100A9/S100A8/S100A7/PTPRC/LILRB2/HPSE/GZMB/CTSC/COTL1/CD36/ADAM8
CCGO:0044459Plasma membrane part252.84E-05ULBP2/TEX101/SLC4A7/SGIP1/SELPLG/RHOH/RGS1/PTPRC/P2RY2/LILRB2/LCP2/LCK/IL7R/IL4R/IL27RA/GZMB/FOSL1/FGD2/CTLA4/CD83/CD52/CD36/CD1C/CA2/ADAM8
CCGO:0031410Cytoplasmic vesicle243.95E-05TMPRSS4/TEX101/SLC4A7/SGIP1/SERPINB3/S100A9/S100A8/S100A7/PTPRC/LILRB2/IL7R/HPSE/GZMB/FGD2/DSC2/CTSC/CTLA4/CST7/COTL1/CD36/CD274/CD1C/CD1B/ADAM8
CCGO:0097708Intracellular vesicle244.04E-05TMPRSS4/TEX101/SLC4A7/SGIP1/SERPINB3/S100A9/S100A8/S100A7/PTPRC/LILRB2/IL7R/HPSE/GZMB/FGD2/DSC2/CTSC/CTLA4/CST7/COTL1/CD36/CD274/CD1C/CD1B/ADAM8
MFGO:00017302’-5’-oligoadenylate synthetase activity31.17E-07OAS3/OAS2/OAS1
MFGO:0035325Toll-like receptor binding31.37E-05S100A9/S100A8/CD36
MFGO:0050786RAGE receptor binding31.88E-05S100A9/S100A8/S100A7
MFGO:0004857Enzyme inhibitor activity93.83E-05SOCS3/SERPINB4/SERPINB3/SERPINB13/RHOH/PI3/PARP9/CST7/ANGPTL4
MFGO:0042379Chemokine receptor binding48.48E-05CCL5/CCL17/CCL13/C10orf99
KEGGhsa05164Influenza A82.27E-05TNFRSF10A/TMPRSS4/SOCS3/OAS3/OAS2/OAS1/CIITA/CCL5
KEGGhsa05146Amoebiasis67.62E-05SERPINB4/SERPINB3/SERPINB13/COL4A4/CD1C/CD1B
KEGGhsa05340Primary immunodeficiency40.0001388PTPRC/LCK/IL7R/CIITA
KEGGhsa04060Cytokine-cytokine receptor interaction90.00019408TNFRSF10A/IL7R/IL4R/IL32/IL27RA/CCL5/CCL22/CCL17/CCL13
KEGGhsa04657IL-17 signalling pathway50.00047875S100A9/S100A8/S100A7/FOSL1/CCL17

[i] BP – biological processes, CC – cellular component, MF – molecular functions, KEGG – Kyoto Encyclopaedia of Genes and Genomes.

Figure 3

GO term enrichment analysis results. FDR results for the top 15 GO terms, including the top 9 BPs, CCs, and MFs

/f/fulltexts/PDIA/46718/PDIA-39-46718-g003_min.jpg
Figure 4

KEGG pathway enrichment results. A, Bp-value results for DEGs and the top 5 enriched KEGG pathways

/f/fulltexts/PDIA/46718/PDIA-39-46718-g004_min.jpg

PPI Network Construction and Hub Gene Identification

The STRING database was used to construct a DEG PPI network (Figure 5), and the top 9 hub genes with the highest score values were determined by using cytoHubba from Cytoscape v. 3.8.2. These hub genes were PTPRC-CTLA4-CD274-CD1C-IL7R-GZMB-CCL5-CD83, and CCL22 (Table 3, Figure 6).

Table 3

MCC of the top 9 genes in the top module

GeneFull nameScoreUp or Down
PTPRCProtein tyrosine phosphatase receptor type C54018Up
CTLA4Cytotoxic T-lymphocyte associated protein 453938Up
CD274CD274 molecule53120Up
CD1CCD1c molecule51966Up
IL7RInterleukin 7 receptor51768Up
GZMBGranzyme B47546Up
CCL5C-C motif chemokine ligand 546135Up
CD83CD83 molecule46080Up
CCL22C-C motif chemokine ligand 2245362Up
Figure 5

A DEG PPI network constructed using the STRING database

/f/fulltexts/PDIA/46718/PDIA-39-46718-g005_min.jpg
Figure 6

The top 9 genes with the highest degree values were identified using CytoHubba. These genes were ranked in descending score order from red to orange to yellow

/f/fulltexts/PDIA/46718/PDIA-39-46718-g006_min.jpg

Discussion

Atopic dermatitis (AD) is an immune-mediated chronic pruritus and inflammatory skin disease. Recurrence and remission usually occur alternately during the disease. The incidence rate of atopic dermatitis (AD) is increasing year by year. In addition to the negative impact on physical and mental health, the cost of AD also brings a huge burden to individuals and society [15]. Although dozens of medications are available for relief of the symptoms of this disease, no cure for AD currently exists. Therefore, identification of key molecules that play key roles in the pathogenesis of the disease for potential development of therapeutic targets represents an important area of investigation.

In this study, through the analysis of 3 AD-related datasets, 143 DEGs were screened, including 91 up-regulated genes and 55 down-regulated genes. Because phenotypic changes in AD patients are mostly related to gene up-regulation, this study focused on further analysis of up-regulated genes. In our GO enrichment results, upregulated genes were mainly concentrated in biological processes, including immune system process, immune response, defence response, leukocyte activation, response to biotic stimulus are the enrichment results of top 5. The results from our KEGG pathway analysis revealed that a high enrichment in viral infection and inflammatory response pathways were present in upregulated genes. These pathways were divided into 3 groups: environmental information processes, human diseases, and organismal systems.

AD is a heterogeneous disease [1619], which is categorized as extrinsic type or intrinsic type based on an increased or normal level of serum IgE, respectively, without considering the age or gender of the patients. Most AD patients (about 80%) are exogenous or allergic. It is characterized by high serum IgE levels and the presence of specific IgE related to environmental and food allergies. Exogenous AD with skin dysfunction is related to filaggrin mutation-and external irritation or allergens can be transcutaneously sensitized due to skin dysfunction, resulting in increased serum IgE. Thus, extrinsic AD is a prototype of skin damage-induced Th2-type dermatitis. Patients with extrinsic AD have age of onset dependence-related risks, such as food allergies, allergic rhinitis, and allergic asthma [20]. About 20% of AD cases classified as endogenous or non-allergic are characterized by normal IgE levels or lack of allergen-specific IgE [21]. On the contrary, Th2 cells are highly activated by external stimuli such as metal ions or haptens (small molecules capable of binding to proteins and changing their immunogenicity).

Atopic dermatitis is currently mainly driven by three main pathological factors: disruption of the skin barrier, altered Th2 cell response, and pruritus [15]. The biological functions of the top 5 highly related genes obtained by GO enrichment and the KEGG pathway are also consistent with the 3 main pathological factors of AD.

The PPi network was constructed by the STRING database, and the Cytoscape software calculated the hub genes. With these analyses, 9 top hub genes, included PTPRC, CTLA4, CD274, CD1C, IL7R, GZMB, CCL5, CD83, and CCL22, were identified.

PTPRC is a member of the protein tyrosine phosphatase (PTP) family. PTPs are known to be signalling molecules that regulate a variety of cellular processes including cell growth, differentiation, mitosis, and oncogenic transformation. This gene encodes CD45 and functions as a signalling gatekeeper in T-cells. Tt is reported that this gene is associated with infection and immune deficiencies [22]. PTPRC and COMMD10 of coding and splicing variants are involved in memory T cell differentiation, and genetic variation controlling T helper cell subsets with crucial roles in protection against infection and susceptibility to autoimmune disease [23]. Unfortunately, studies on PTPRC in AD are still very limited. This situation reminds us that PTPRC may be a key gene in the pathogenesis of psoriasis, which has long been neglected and deserves intensive study.

CTLA4 plays an essential role in the function of the regulatory T cells that control Th1 and Th2 immune responses. Borrego et al. reported the polymorphisms of CTLA4 are also associated with severe asthma [24], and the most severe AD cases are related to bronchial obstruction, so this gene may be a marker of moderate to severe AD.

The immunosuppressive performance of CD274 has been reported in many malignant tumour diseases, and related drugs have also been developed as immunotherapy targets. However, in skin-related diseases, Tanaka et al. reported that PD-L1 (CD274) plays predominant roles in Th1 and Th17 type immunity, whereas PD-L2 is involved in Th2-type immunity [25]. One study indicated that progression from acute to chronic AD lesions was associated with intensification along a progression of inflammatory mediators rather than distinct immunological mechanisms [26]. In AD patients, Th2-mediated responses are more prominent in the acute phase, whereas Th1-mediated responses are more prominent in chronic AD disease [27]. In our study, the CD274 score ranked high, echoing the previous reports, so we can conduct further studies on this gene in AD to understand its functional mechanism.

CD1C is the subgroup of CD1, which is structurally and functionally similar to MHC class I and II molecules, usually as a marker of the Th2-promoting subtype (mDC2). There is no report to directly verify the direct mechanism of CD1C in AD patients [28].

IL7R plays a role in AD by encoding a heterodimeric receptor complex composed of IL7Ra and TSLP [29]. Berna et al. sequenced TSLP and IL7R in AD patients and confirmed both genes are up-regulation, meanwhile, they had conducted NGS of the TSLP and IL7R, founding that IL7R variants may modulate the effect of TSLP variants which are associated with more persistent AD [30], which is consistent with our hub gene prediction [30].

GZMB encodes granzyme B, which has a pro-apoptotic effect. New research shows that the protease plays extracellular roles involving the proteolytic cleavage of extracellular matrix, cell adhesion proteins, and basement membrane proteins. Kamata et al. [31] reported GZMB was significantly higher in AD patients than in healthy controls. Correlation analyses showed that GZMB positively correlated with serum levels of GRP, an itch-related peptide, and dermatitis severity markers such as thymus and activation-regulated chemokine in patients with AD.They considered that GZMB may reflect the degree of dermatitis and pruritus in AD patients.

CD83 can encode a single-pass type I membrane protein and member of the immunoglobulin superfamily of receptors. The protein binds to dendritic cells and inhibits their maturation in a soluble form. He et al. construct a transcriptome of tape strips from lesional and nonlesional, the skin of adults with moderate-to-severe AD and psoriasis studies confirmed the overexpression of CD83, which is consistent with our screening results [32]. However, there is currently less research on the mechanism of CD83 in AD, encourages further study of therapeutics targeting this gene in AD [32].

CCL22 and CCL5 are both motif chemokine mRNAs and chemokines form a superfamily of secreted proteins involved in immunoregulatory and inflammatory processes. Studies have shown that by testing the human peripheral blood mononuclear cells (PBMCs) of AD patients and healthy controls, CCL22 and the expression of CCL5 increased in the AD group [33].

Study limitations: Because of the molecular complexity of atopic dermatitis, the present study could not provide sufficient information on the systematic functions of the 9 identified hub genes in the pathogenesis of atopic dermatitis. Further extensive studies on this issue are warranted in the future.

Conclusions

Our study was based on the GEO database and identified important hub genes and signalling pathways for AD. These findings will help us to further understand the molecular mechanism of AD and may provide useful bioinformatics information for future research on atopic dermatitis.

Acknowledgments

We would like to thank the National Centre of Biotechnology Information (NCBI)-GEO database (https://www.ncbi.nlm.nih.gov/geo/).

Conflict of interest

The authors declare no conflict of interest.

References

1 

Weidinger S, Beck LA, Bieber T, et al. Atopic dermatitis. Nat Rev Dis Primers 2018; 4: 1.

2 

Thomsen SF. Atopic dermatitis: natural history, diagnosis, and treatment. ISRN Allergy 2014; 2014: 354250.

3 

Mayba JN, Gooderham MJ. Review of atopic dermatitis and topical therapies. J Cutan Med Surg 2017; 21: 227-36.

4 

DaVeiga SP. Epidemiology of atopic dermatitis: a review. Allergy Asthma Proc 2012; 33: 227-34.

5 

Mansouri Y, Guttman-Yassky E. Immune pathways in atopic dermatitis, and definition of biomarkers through broad and targeted therapeutics. J Clin Med 2015; 4: 858-73.

6 

Yu C, Chen J, Ma J, et al. Identification of key genes and signaling pathways associated with the progression of gastric cancer. Pathol Oncol Res 2020; 26: 1903-19.

7 

Yang Y, Zhong Z, Ding Y, et al. Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure. Genes Dis 2018; 5: 349-57.

8 

Tsoi LC, Rodriguez E, Degenhardt F, et al. Atopic dermatitis is an IL-13–dominant disease with greater molecular heterogeneity compared to psoriasis. J Invest Dermatol 2019; 139: 1480-9.

9 

Plager DA, Leontovich AA, Henke SA, et al. Early cutaneous gene transcription changes in adult atopic dermatitis and potential clinical implications. Exp Dermatol 2007; 16: 28-36.

10 

Plager DA, Kahl JC, Asmann YW, et al. Gene transcription changes in asthmatic chronic rhinosinusitis with nasal polyps and comparison to those in atopic dermatitis. PLoS One 2010; 5: e11450.

11 

Esaki H, Ewald DA, Ungar B, et al. Identification of novel immune and barrier genes in atopic dermatitis by means of laser capture microdissection. J Allergy Clin Immun 2015; 135: 153-63.

12 

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R Package for comparing biological themes among gene clusters. Omics J Integr Biology 2012; 16: 284-7.

13 

Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics 2015; 31: 2912-4.

14 

Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 2017; 45 (Database issue): D362-8.

15 

Weidinger S, Novak N. Atopic dermatitis. Lancet 2016; 387: 1109-22.

16 

Kabashima K, Nomura T. Revisiting murine models for atopic dermatitis and psoriasis with multipolar cytokine axes. Curr Opin Immunol 2017; 48: 99-107.

17 

Guttman-Yassky E, Krueger JG. Atopic dermatitis and psoriasis: two different immune diseases or one spectrum? Curr Opin Immunol 2017; 48: 68-73.

18 

Muraro A, Lemanske RF, Hellings PW, et al. Precision medicine in patients with allergic diseases: airway diseases and atopic dermatitis – PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology. J Allergy Clin Immun 2016; 137: 1347-58.

19 

Sokolowska M, Akdis CA. Highlights in immune response, microbiome and precision medicine in allergic disease and asthma. Curr Opin Immunol 2017; 48: iv-ix.

20 

Gupta J, Johansson E, Bernstein JA, et al. Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry. J Allergy Clin Immun 2016; 138: 676-99.

21 

Tokura Y. Extrinsic and intrinsic types of atopic dermatitis. J Dermatol Sci 2010; 58: 1-7.

22 

Chin A, Balasubramanyam S, Davis CM. Very elevated IgE, atopy, and severe infection: a genomics-based diagnostic approach to a spectrum of diseases. Case Rep Immunol 2021; 2021: 2767012.

23 

Lagou V, Garcia-Perez JE, Smets I, et al. Genetic architecture of adaptive immune system identifies key immune regulators. Cell Rep 2018; 25: 798-810.e6.

24 

Borrego LM, Arroz MJ, Videira P, et al. Regulatory cells, cytokine pattern and clinical risk factors for asthma in infants and young children with recurrent wheeze. Clin Exp Allergy 2009; 39: 1160-9.

25 

Tanaka R, Ichimura Y, Kubota N, et al. Differential involvement of programmed cell death ligands in skin immune responses. J Invest Dermatol 2022; 142: 145-54.

26 

Sims JT, Chang CY, Higgs RE, et al. Insights into adult atopic dermatitis heterogeneity derived from circulating biomarker profiling in patients with moderate-to-severe disease. Exp Dermatol 2021; 30: 1650-61.

27 

Lee Y, Choi HK, N’deh KPU, et al. Inhibitory effect of Centella asiatica extract on DNCB-induced atopic dermatitis in HaCaT cells and BALB/c mice. Nutrients 2020; 12: 411.

28 

Hayashi Y, Ishii Y, Hata-Suzuki M, et al. Comparative analysis of circulating dendritic cell subsets in patients with atopic diseases and sarcoidosis. Respir Res 2013; 14: 29.

29 

Fornasa G, Tsilingiri K, Caprioli F, et al. Dichotomy of short and long thymic stromal lymphopoietin isoforms in inflammatory disorders of the bowel and skin. J Allergy Clin Immunol 2015; 136: 413-22.

30 

Berna R, Mitra N, Lou C, et al. Thymic stromal lymphopoietin and IL7R variants are associated with persistent atopic dermatitis. J Invest Dermatol 2020; 141: 446-50.e2.

31 

Kamata Y, Kimura U, Matsuda H, et al. Relationships among plasma granzyme B level, pruritus and dermatitis in patients with atopic dermatitis. J Dermatol Sci 2016; 84: 266-71.

32 

He H, Bissonnette R, Wu J, et al. Tape strips detect distinct immune and barrier profiles in atopic dermatitis and psoriasis. J Allergy Clin Immun 2021; 147: 199-212.

33 

Yu Y, Lin D, Cai X, et al. Enhancement of chemokine mRNA expression by Toll-like receptor 2 stimulation in human peripheral blood mononuclear cells of patients with atopic dermatitis. Biomed Res Int 2020; 2020: 1497175.

Copyright: © 2022 Termedia Sp. z o. o. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
 
Quick links
© 2023 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.