Bioscience Biotechnology Research Communications

An International  Peer Reviewed Refereed Open Access Journal

P-ISSN: 0974-6455 E-ISSN: 2321-4007

Bioscience Biotechnology Research Communications

An Open Access International Journal

Sachita Singh1,3, Saumya Choudhary1, 4, Dibyabhaba Pradhan2 and Arun Kumar Jain1*

1 Biomedical Informatics Centre, National Institute of Pathology-ICMR, New Delhi, India

2ICMR-AIIMS Computational Genomics Centre, Indian Council of Medical Research, New Delhi, India

3Faculty of Health Sciences, Symbiosis International (Deemed University), Pune, India.

4Department of Molecular and Cellular Engineering, Sam Higginbottom University of Agriculture, Prayagraj, India.

Corresponding author email:


Article Publishing History


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Psoriasis is a chronic inflammatory skin disorder with a complex genetic background. To date, the psoriasis heritability is not completely known. Histopathologically psoriatic lesions are characterized by abnormal keratinocyte differentiation. This differentiation is believed to be led by T-cell infiltrates that creates a cytokine storm inducing overexpression of inflammatory mediators. The gene expression profile, GSE75343 was retrieved from NCBI-GEO database to explore the dysregulated genes between lesional, non-lesional and healthy skin biopsy samples. using affy package in R software. Further, gene ontology analysis, KEGG based pathway analysis, interaction analysis between statistically significant DEGs, cluster analysis and pathway enrichment analysis of biologically significant clusters was conducted using cytoscape software (3.7.2) & its plugin.

A total of 174 DEGs (115 upregulated and 79 downregulated) were recruited from LS-NL experimental design satisfying the cut-off criterion. Amongst many immune mediated pathways Cytokine- cytokine & chemokine pathways are crucial pathways associated with disease initiation and development. S100A7, CCL20, IL36RN, CXCL8, IFIH1, MX1, LCN2, ARG1, CXCL1, LEP, STAT1, IRF7, FASN, ISG15 and OAS1 are the top 15 proposed potential biomarkers. The identified hubs could be validated against confirmed patients to get more clear understanding of their role in psoriasis pathogenesis.


Chemokines, Cytokines, Cytoscape, Hub targets, Psoriasis.

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Singh S, Choudhary S, Pradhan D, Jain A. K. Whole Transcriptome Profile Reveals Chemokines and Cytokines Pathways as Potential Target for Psoriasis Drug Discovery. Biosc.Biotech.Res.Comm. 2023;16(3).

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Singh S, Choudhary S, Pradhan D, Jain A. K. Whole Transcriptome Profile Reveals Chemokines and Cytokines Pathways as Potential Target for Psoriasis Drug Discovery. Biosc.Biotech.Res.Comm. 2023;16(3). Available from: <a href=”“></a>


Psoriasis is one of the most common chronic multifactorial skin disorder with global prevalence of 0.2-11.4% in adults and 0-2.1% in children (Egeberg and Thyssen, 2019; Zhang et al. 2019).  Psoriasis is a worldwide prevalent disease, diverges within different parts of the world depending on varying geographical areas. Off late psoriasis has expanded its manifestation beyond skin and has been reported as a multisystemic disorder being independent precursor for cardiovascular diseases, psoriatic arthritis, atherosclerosis, diabetes and insulin resistance, hypertension, metabolic syndrome, myocardial infarction and obesity burdening the quality of life (Menter et al. 2018; Huang et al. 2019; Choudhary et al. 2020).

Over the last decade, there has been extensive utilization of omics-based technologies to identify potential therapeutic targets. As a result, at genome, transcriptome, proteome, and metabolome levels, numerous vital biomarkers for psoriasis have been uncovered. These discoveries have provided new insights into the underlying molecular mechanisms and signaling pathways in psoriasis pathogenesis. These findings have offered fresh perspectives on the fundamental molecular mechanisms and signaling pathways involved in psoriasis pathogenesis (Tang et al., 2014; Singh et al., 2019).

The etiology of psoriasis is little known however histopathologically psoriasis lesion is majorly characterized by hyperkeratotic plaques, resulting due to infiltration of effector leukocytes leading to epidermal hyperproliferation accompanied by abnormal keratinocyte differentiation, inflammation, acanthosis and angiogenesis. Th17 cells and their secreted cytokines, interleukin (IL)-17, and IL-22, in synergy with interferon (IFN)-γ and tumour necrosis factor alpha (TNF-α), is believed to form a cluster during the early phase of the disease initiation. Hence, these T-cell infiltrate leads to a cytokine storm and further induces overexpression of inflammatory mediators. Amongst various psoriasis mechanisms, pathways mediated by IL-36 cytokines are believed to be essentially relevant (Lowes, Suárez-Fariñas and Krueger, 2014; Arakawa et al. 2015; Singh et al. 2019).

IL-36 cytokines belong to IL-1 family with IL-36alpha, IL-36beta, and IL-36gamma as three agonist and IL-36 receptor antagonist (known as IL-36Ra or IL-36RN) and IL-38 as two antagonists with opposite functions and competitively binding to IL-36R/IL-1R/AcP complex. Several authors have reported the over expression of IL-36 in psoriasis lesions at both early & late phase of the disease and is found to be localized at epidermis (Towne and Sims, 2012; Boutet et al., 2016; Mercurio et al., 2018). In psoriasis, IL-36 RN is believed to comprehend the pathogenic role of IL-36 axis in psoriasis development. In this backdrop, our study considered GEO profile, GSE75343 from GPL571 to understand the role of cytokine-cytokine and cytokine-chemokine pathways and associated differentially expressed genes in psoriasis. Further the study intends to identify the top priority biomarkers and other functionally relevant biological processes in psoriasis.


Microarray Data Information: Gene expression profile, GSE75343 was retrieved from NCBI-GEO database, deposited from Rockefeller University, USA. The GSE record was generated on GPL571 platform (Affymetrix Human Genome U133A 2.0 Array) (Ruano et al., 2016).

Data Pre-Processing and Identification of DEGs: The raw data files used for the analysis included .cel files, imported into R, version 4.0.3. Background correction, normalization and log 2 transformation was performed based on robust multiarray average (RMA) method embedded in the Affy package in Bioconductor version 3.8 ( (Gautier et al., 2004). Bioconductor package of GEOquery and limma was used to identify differentially expressed genes between normal healthy skin, lesional and non-lesional skin of psoriasis patients. Benjamini and Hochberg method was applied to calculate the false discovery rate (FDR) (Chen et al., 2016). Statistically significant DEGs between different sample groupings viz., lesional-non-lesional (LS-NL), lesion-normal (LS-NS) and non-lesional-normal skin (NL-NS) samples were defined with p<0.05 and [log FC] >=2 as the cut off criterion. For any gene with multiple corresponding probes, the mean expression value of all probes was considered as its final expression value (Wang and Zheng, 2014).

Gene Ontology and Pathway Enrichment Analysis: The Candidate DEGs were analyzed in cluster Profiler package in Bioconductor version 3.4.4 ( This package automates the process of biological term classification including biological process (BP), cellular component (CC) and molecular function (MF) (Yu et al., 2012). It also annotates genes to pathways using Kyoto Encyclopaedia of Genes and Genomes (KEGG) (Kanehisa et al., 2017).

Gene- Disease Network: The DEGs were annotated based on gene-disease association data from knowledge bases viz. genome wide association study catalogue (GWAS), genetics association database (GAD), comparative toxicogenomics database (CTD), ClinVar, and UniProt through online tools DisGeNET and Target Validation. The gene–disease network was inspected by integrating the data into Cytoscape v3.7.2 (Piñero et al., 2017; Carvalho-Silva et al., 2019).

Integration of Protein–Protein Interaction (PPI) Network, Module Analysis, Significant Candidate Genes and Pathway Identification: Protein-protein Interaction was constructed using STRING database in Cytoscape v3.7.2 (Szklarczyk et al., 2019; Shannon et al., 2003). Hub genes were identified using Cytohubba plugin using all the 12 parameters, i.e., MCC, DMNC, MNC, Degree, EPC, Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, Stress, and Clustering Coefficient (Yang et al., 2018). The genes were considered to be high priority hub targets if they were present in more than 6 parameters (Nayak et al., 2019). Molecular complex detection (MCODE) was used to screen the modules of PPIs network with degree cut-off=10, node score cut-off=0.2, k-core=2, and maximum depth=100. The gene ontology analysis of top three modules was performed by STRING Enrichment plugin. FDR<0.05 was set as the cut off criteria for gene ontology (GO) analysis.


Psoriasis has nearly affected 3% of the population worldwide with no permanent therapy. Hence it is important to recognize important molecules with crucial role in pathogenesis with therapeutic potential. In the present microarray study, lesional, non-lesional and normal samples were analysed using limma package in R, 4.0.3.

Data Pre-processing and Identification of DEGs: The retrieved expression profile GSE57343 from GPL571 is based on the data deposited in NCBI-GEO from Rockefeller University, USA. GSE75343 had 45 samples including 10 normal skin (NS) tissue sample, 22 lesional skin (LS) and 13 non-lesional skin (NL). With FDR<0.05 and [log FC] >=2 as cut-off criterion, 174 DEGs (115 upregulated and 79 downregulated); 373 DEGs (201 upregulated and 172 downregulated) and 373 DEGs (201 upregulated and 172 downregulated) were obtained between different sample groupings including LS-NL, LS-NS and NL-NS respectively (Figure 1A, 1B and 1C).

Amongst these differentially expressed genes 131 DEGs from LS-NL group were shared between LS-NS and NS-NL, furthermore all 373 DEGs were shared between LS-NS & NS-NL. Moreover, it is interesting to note that all 373 DEGs obtained amongst LS-NS & NS-NL were identically expressed (Figure 2).

To understand and comprehend psoriasis and its progression, the differentially expressed genes among LS-NL were considered. Top 50 dysregulated expressed genes were visualized as heatmap (Figure 3). The result identified several DEGs and pathways believed to be functionally relevant. In total 173 DEGs were identified between LS-NL, 374 between LS-NS and NS-NL individually. It is interesting to note that both LS-NS and NS-NL shared all 374 DEGs between them. Among 173 LS-NL DEGs, 43 DEGs were unique with respect to other two groups.

DEGs Gene Ontology Analysis in Lesional – Non-lesional Psoriasis: Gene ontology analysis was used to understand and visualize the functional profiles of DEGs (GO and KEGG). The up-regulated and down-regulated DEGs were classified into three categories explaining biological process, molecular function and cellular components. Among upregulated DEGs, defense response to other organism, neutrophil activation involved in immune response, neutrophil degranulation, epidermis development, epidermal cell differentiation and keratinocyte differentiation are primarily enriched biological processes while endopeptidase activity, receptor ligand activity, cytokine activity, RAGE receptor binding, chemokine receptor binding and CCR chemokine receptor binding are vital enriched molecular functions. Further, secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, specific granule and specific granule lumen are the top enriched cellular components (Table 1).

Table 1. The top 10 significant enriched analysis of up-regulated genes in lesional psoriasis skin

ID GO Enriched Term FDR


Gene Count Category
GO:0006631 fatty acid metabolic process 5.16E-13 17 BP
GO:0072330 monocarboxylic acid biosynthetic process 8.95E-10 14 BP
GO:0046394 carboxylic acid biosynthetic process 1.16E-08 14 BP
GO:0016053 organic acid biosynthetic process 1.16E-08 14 BP
GO:0016042 lipid catabolic process 5.17E-09 13 BP
GO:1901615 organic hydroxy compound metabolic process 6.43E-06 12 BP
GO:0044242 cellular lipid catabolic process 7.25E-09 11 BP
GO:0030258 lipid modification 7.25E-09 11 BP
GO:0008202 steroid metabolic process 9.46E-07 11 BP
GO:0006633 fatty acid biosynthetic process 7.90E-09 10 BP
GO:0048037 cofactor binding 0.001165963 9 MF
GO:0016614 oxidoreductase activity, acting on CH-OH group of donors 0.000494932 6 MF
GO:0050662 coenzyme binding 0.028034967 5 MF
GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 0.019853107 4 MF
GO:0019842 vitamin binding 0.021213538 4 MF
GO:0016747 transferase activity, transferring acyl groups other than amino-acyl groups 0.045693548 4 MF
GO:0033293 monocarboxylic acid binding 0.021213538 3 MF
GO:0003996 acyl-CoA ligase activity 0.019853107 2 MF
GO:0004467 long-chain fatty acid-CoA ligase activity 0.019853107 2 MF
GO:0015645 fatty acid ligase activity 0.021213538 2 MF
GO:0044438 microbody part 3.69E-05 6 CC
GO:0044439 peroxisomal part 3.69E-05 6 CC
GO:0005777 Peroxisome 9.67E-05 6 CC
GO:0042579 Microbody 9.67E-05 6 CC
GO:0005782 peroxisomal matrix 0.000397 4 CC
GO:0031907 microbody lumen 0.000397 4 CC
GO:0001725 stress fiber 0.008641 3 CC
GO:0097517 contractile actin filament bundle 0.008641 3 CC
GO:0005778 peroxisomal membrane 0.008641 3 CC
GO:0031903 microbody membrane 0.008641 3 CC

Similarly, amongst the downregulated DEGs fatty acid metabolic process, monocarboxylic acid biosynthetic process, carboxylic acid biosynthetic process, organic acid biosynthetic process and lipid catabolic process are the top 5 enriched biological processes while cofactor binding, oxidoreductase activity-acting on CH-OH group of donors’ coenzyme binding, vitamin binding and transferase activity, transferring acyl groups other than amino-acyl groups are highly enriched molecular functions. Furthermore, microbody part peroxisomal part, stress fibre, contractile actin filament bundle and lipid droplet are topmost enriched cellular components (Table 2).

Table 2. The top 10 significant enriched analysis of down-regulated genes in lesional psoriasis skin

ID GO Enriched Term FDR


Gene Count Category
GO:0098542 defense response to other organisms 2.81E-12 24 BP
GO:0042119 neutrophil activation 1.11E-09 21 BP
GO:0002446 neutrophil mediated immunity 5.89E-09 20 BP
GO:0002283 neutrophil activation involved in immune response 4.51E-09 20 BP
GO:0043312 neutrophil degranulation 4.51E-09 20 BP
GO:0008544 epidermis development 2.21E-06 16 BP
GO:0043588 skin development 6.89E-07 16 BP
GO:0009615 response to virus 2.53E-07 15 BP
GO:0009913 epidermal cell differentiation 3.16E-06 14 BP
GO:0030216 keratinocyte differentiation 6.75E-07 14 BP
GO:0004175 endopeptidase activity 5.42E-05 22 MF
GO:0004252 serine-type peptidase activity 2.46E-05 22 MF
GO:0030414 peptidase inhibitor activity 0.01166427 20 MF
GO:0061134 peptidase regulator activity 0.023446559 19 MF
GO:0004867 serine-type endopeptidase inhibitor activity 0.004038224 19 MF
GO:0002020 protease binding 0.019596 19 MF
GO:0005125 cytokine activity 0.038523 18 MF
GO:0050786 RAGE receptor binding 3.45E-05 17 MF
GO:0042379 chemokine receptor binding 0.00695 17 MF
GO:0048020 CCR chemokine receptor binding 0.011677 17 MF
GO:0034774 secretory granule lumen 1.31E-09 17 CC
GO:0060205 cytoplasmic vesicle lumen 1.31E-09 17 CC
GO:0031983 vesicle lumen 1.31E-09 17 CC
GO:0042581 specific granule 1.64E-05 9 CC
GO:0035580 specific granule lumen 1.60E-07 8 CC
GO:0005775 vacuolar lumen 0.000240936 8 CC
GO:0001533 cornified envelope 2.79E-07 7 CC
GO:0005766 primary lysosome 0.000708625 7 CC
GO:0042582 azurophil granule 0.000708625 7 CC
GO:0035578 azurophil granule lumen 0.000400823 6 CC

Gene ontology analysis of upregulated DEGs reported the molecular function viz., proteolytic enzymes, cytokine and chemokine receptor activity to be highly enriched in psoriasis identified genes. Henry et al. (2011) and  Chularojanamontri et al. (2019) in their study pointed that proteasome system is engrossed with psoriasis pathogenesis with involvement of cytokine and chemokine mediated genes (TLTF, TMPRSS11D, IL36G, CXCL8, CXCL1, IL36RN, CXCL13, TYMP, NAMPT and PPY) which is in concordance with the finding of the present study. Further, Nickoloff and Nestle. (2004); Mabuchi et al. (2012); Singh et al (2013); Meephansan et al. (2017) in their studies highlighted the importance of chemokine receptors and their corresponding chemokine ligands in psoriasis, role of similar receptor & ligands was also implicated in the present gene ontology analysis. Further, the involvement of IL-17 signalling pathway, NOD-like receptor signalling pathway, Cytokine-cytokine receptor interaction, Epstein-Barr virus infection, RIG-I-like receptor signalling pathway in up-streamed DEGs is indicative of the fact that psoriasis is immune mediated disorder (Ainali et al., 2012; Keermann et al., 2015; Melero et al., 2018; Choudhary et al., 2021).

S100A7, CCL20, IL36RN, CXCL8, IFIH1, MX1, LCN2, ARG1, CXCL1, LEP, STAT1, IRF7, FASN, ISG15 and OAS1 are top 15 hub genes associated with psoriasis. It is crucial to comprehend the role of these hub genes in disease initiation and progression. S100A7, also known as Psoriasin, is a gene implicated in cell differentiation. Its expression in close proximity to the suprabasal differentiating layer within the epidermal region strongly indicates its involvement in keratinocyte differentiation in psoriasis. A similar role of S100A7 was confirmed in the studies conducted by Ekman et al. (2017) and Rangaraj et al. (2017). Numerous hub targets such as, CCL20, IL36RN, CXCL8, IFIH1, MX1, CXCL1, STAT1, IRF7 and ISG15 can be classified as cytokines, its mediators or interferons.

The dysregulation of these genes in psoriasis transcriptome has been reported by several researchers, including Swindell et al (2017); Albanesi et al (2018) and Bai et al (2018). Like our findings, Elnabawi et al. (2020) suggested that CCL20 could serve as pro-inflammatory biomarkers and potentially contribute to enhancing vascular health in inflammatory diseases. Further, the interplay between IL-17 and TNFα is significant to the enhance the mRNA expression of pro-psoriatic genes CXCL8, CXCL1, S100A7 in keratinocytes (Benezeder and Wolf, 2019). Further, Albanesi et al. (2018) in their study highlighted crosstalk’s between cytokines, cytokines mediators and interferons to be responsible for unceasing inflammation and proliferation.

Signalling Pathway Enrichment Analysis: Influenza A, IL-17 signalling pathway, NOD-like receptor signalling pathway, Cytokine-cytokine receptor interaction and Epstein-Barr virus infection pathways are the top 5 enriched pathways amongst upregulated DEGs (Figure 4A) similarly downregulated DEGs were found to high enriched in PPAR signalling pathway, Peroxisome, Fatty acid metabolism, Carbon metabolism and AMPK signalling pathway (Figure 4B).

Genes–Disease Network of DEGs: The identified lesional skin DEGs were mapped to the validated disease genes in DisGeNET and target validation human genetic disorder databases. This analysis confirmed presence of 130 psoriasis associated DEGs (75.14%) in our datasets indicating that the identified DEGs are appropriate to signify the disease. The gene-disease network for 20 highest ranking genes found it to be associated with several other dermal disorder including parakeratosis, psoriasiform eczema, rosacea, atopic dermatitis, with several other respiratory, inflammatory and autoimmune disorders (Figure 5).

Key Candidate Genes and Pathway Identification with DEGs Protein-Protein Interaction (PPI) and Module Analysis: One hundred seventy-three (173) DEGs with 168 nodes and 474 edges were imported from PPI network complex. The nodes denote genes and the edges denote the interaction between genes. Cytohubba plugin was used for hub gene identification, the top 15 hub genes identified are illustrated in Figure 6.

Top 2 clusters with MCODE score greater than 10 were further analysed and pathway enrichment analysis of the modules found them to be functionally related. Influenza A, NOD-like receptor signalling pathway and RIG-I-like receptor signalling pathway are the most enriched pathway among cluster 1 whereas Cytokine-cytokine receptor interaction, Chemokine signalling pathway and IL-17 signalling pathway are the most enriched pathway in cluster 2 indicating the relevance of cytokine and cytokine-chemokine mediated pathways in psoriasis (Table 3).

Table 3. Signalling pathway enrichment analysis of top clusters in lesiona psoriasis skin

Cluster Description Number of Genes Enriched Enriched Gene FDR
Cluster 1 Measles 7 OAS3, IFIH1, OAS2, STAT1, IRF7, MX1, OAS1 5.04E-12
Influenza A 7 OAS3, IFIH1, OAS2, STAT1, IRF7, MX1, OAS1 2.45E-11
Herpes simplex infection 6 OAS3, IFIH1, OAS2, STAT1, IRF7, OAS1 3.26E-09
Hepatitis C 5 OAS3, OAS2, STAT1, IRF7, OAS1 4.26E-08
NOD-like receptor signalling pathway 5 OAS3, OAS2, STAT1, IRF7, OAS1 1.34E-07
RIG-I-like receptor signalling pathway 3 IFIH1, ISG15, IRF7 2.19E-05
Human papillomavirus infection 4 OASL, STAT1, ISG15, MX1 8.39E-05
Hepatitis B 3 IFIH1, STAT1, IRF7 1.70E-04
Toll-like receptor signaling pathway 2 STAT1, IRF7 0.0028
Kaposi’s sarcoma-associated herpesvirus infection 2 STAT1, IRF7 0.0086
Cluster 2 Cytokine-cytokine receptor interaction 6 CCL27, CXCL13, LEP, CCL20, CXCL1, CXCL2 1.01E-05
Chemokine signaling pathway 5 CCL27, CXCL13, CCL20, CXCL1, CXCL2 2.30E-05
IL-17 signaling pathway 4 CCL20, LCN2, CXCL1, CXCL2 3.55E-05
PPAR signaling pathway 3 FABP4, PLIN1, LPL 6.00E-04
TNF signaling pathway 3 CCL20, CXCL1, CXCL2 0.0015
Legionellosis 2 CXCL1, CXCL2 0.0092
Regulation of lipolysis in adipocytes 2 FABP4, PLIN1 0.0092
Rheumatoid arthritis 2 CCL20, CXCL1 0.0166
Amoebiasis 2 ARG1, CXCL1 0.0166
Salmonella infection 2 CXCL1, CXCL2 0.0166
AMPK signalling pathway 2 FASN, LEP 0.0238
NOD-like receptor signalling pathway 2 CXCL1, CXCL2 0.0402
Kaposi’s sarcoma-associated herpesvirus infection 2 CXCL1, CXCL2 0.0446

The module analysis was done to understand the pathogenesis of the disease. The top 2 clusters identified Influenza A, NOD-like receptor signalling pathway, RIG-I-like receptor signalling pathway, Cytokine-cytokine receptor interaction, Chemokine signalling pathway and IL-17 signalling pathway as crucial pathways in disease pathogenesis. The reported result is in concordance with findings of indicating the involvement of cytokines, chemokines, in disease progression and subsequently alterations in these pathways could help in disease control (Ainali et al., 2012; Keermann et al., 2015; Melero et al., 2018; Choudhary et al., 2020).


The present study described the comprehensive gene expression profile of control healthy tissue samples, non-lesional and lesional tissue of psoriasis patients. The study identified 173 LS-NL DEGs of which S100A7, CCL20, IL36RN, CXCL8, IFIH1, MX1, LCN2, ARG1, CXCL1, LEP, STAT1, IRF7, FASN, ISG15 and OAS1 were identified as top 15 genes that could be denoted as hub targets. Involvement of these DEGs in Gene ontology enrichment (Molecular function, biological processes and cellular components) and pathway analysis establishes its essential role as therapeutic drug targets and biomarkers for psoriasis with further experimental validation.

Ethics Approval and Consent to Participate: None Required

Human and Animal Rights: None violated

Conflict of Interest: There is no conflict of interest to declare.


The authors duly acknowledge facilities provided by Biomedical Informatics Centre, ICMR-National Institute of Pathology, New Delhi.


Ainali, C., Valeyev, N., Perera, G., Williams, A., Gudjonsson, J.E., Ouzounis, C.A., Nestle, F.O. and Tsoka, S., (2012). Transcriptome classification reveals molecular subtypes in psoriasis. BMC genomics, 13(1), pp.1-15.

Albanesi, C., Madonna, S., Gisondi, P. and Girolomoni, G., (2018). The interplay between keratinocytes and immune cells in the pathogenesis of psoriasis. Frontiers in Immunology, 9, p.1549.

Arakawa, A., Vollmer, S., Besgen, P., Galinski, A., Summer, B., Kawakami, Y., Wollenberg, A., Dornmair, K., Spannagl, M., Ruzicka, T. and Thomas, P., (2018). Unopposed IL-36 activity promotes clonal CD4+ T-cell responses with IL-17A production in generalized pustular psoriasis. Journal of Investigative Dermatology, 138(6), pp.1338-1347.

Bai, F., Zheng, W., Dong, Y., Wang, J., Garstka, M.A., Li, R., An, J. and Ma, H., (2018). Serum levels of adipokines and cytokines in psoriasis patients: a systematic review and meta analysis. Oncotarget, 9(1), p.1266.

Benezeder, T. and Wolf, P., (2019), November. Resolution of plaque-type psoriasis: what is left behind (and reinitiates the disease). In Seminars in Immunopathology (Vol. 41, pp. 633-644). Springer Berlin Heidelberg.

Boehncke, W.H., (2018). Systemic inflammation and cardiovascular comorbidity in psoriasis patients: causes and consequences. Frontiers in immunology, 9, p.579.

Boutet, M.A., Bart, G., Penhoat, M., Amiaud, J., Brulin, B., Charrier, C., Morel, F., Lecron, J.C., Rolli-Derkinderen, M., Bourreille, A. and Vigne, S., (2016). Distinct expression of interleukin (IL)-36 α, β and γ, their antagonist IL-36Ra and IL-38 in psoriasis, rheumatoid arthritis and Crohn’s disease. Clinical & Experimental Immunology, 184(2), pp.159-173.

Carvalho-Silva, D., Pierleoni, A., Pignatelli, M., Ong, C., Fumis, L., Karamanis, N., Carmona, M., Faulconbridge, A., Hercules, A., McAuley, E. and Miranda, A., (2019). Open Targets Platform: new developments and updates two years on. Nucleic acids research, 47(D1), pp.D1056-D1065.

Chen, W., Xie, K., Liu, X. and Chen, H., (2016). Identification of key pathways and genes in psoriasis via gene microarray analysis. Molecular Medicine Reports, 13(3), pp.2327-2337.

Choudhary, S., Pradhan, D., Khan, N.S., Singh, H., Thomas, G. and Jain, A.K., (2020). Decoding psoriasis: integrated bioinformatics approach to understand hub genes and involved pathways. Current Pharmaceutical Design, 26(29), pp.3619-3630.

Choudhary, S., Patel, R., Pradhan, D., Deval, R., Singh, H., Thomas, G. and Jain, A.K., (2020). Psoriasis and cardiovascular disorders: association or epiphenomenon? Meta-analysis of observational studies. 3 Biotech, 10, pp.1-15.

Choudhary, S., Pradhan, D., Pandey, A., Khan, M., Lall, R., Ramesh, V., Puri, P., Jain, A.K. and Thomas, G., (2020). The association of metabolic syndrome and psoriasis: a systematic review and meta-analysis of observational study. Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets-Immune, Endocrine & Metabolic Disorders), 20(5), pp.703-717.

Choudhary, S., Anand, R., Pradhan, D., Bastia, B., Kumar, S.N., Singh, H., Puri, P., Thomas, G. and Jain, A.K., (2021). Transcriptomic landscaping of core genes and pathways of mild and severe psoriasis vulgaris. International Journal of Molecular Medicine, 47(1), pp.219-231.

Chularojanamontri, L., Charoenpipatsin, N., Silpa-Archa, N., Wongpraparut, C. and Thongboonkerd, V., (2019). Proteomics in psoriasis. International Journal of Molecular Sciences, 20(5), p.1141.

Egeberg, A. and Thyssen, J.P., (2019). Factors associated with patient-reported importance of skin clearance among adults with psoriasis and atopic dermatitis. Journal of the American Academy of Dermatology, 81(4), pp.943-949.

Ekman, A.K., Vegfors, J., Eding, C.B. and Enerbäck, C., (2017). Overexpression of psoriasin (S100A7) contributes to dysregulated differentiation in psoriasis. Acta dermato-venereologica, 97(4), pp.441-448.

Elnabawi, Y.A., Garshick, M.S., Tawil, M., Barrett, T.J., Fisher, E.A., Sicco, K.L., Neimann, A.L., Scher, J.U., Krueger, J. and Berger, J.S., (2021). CCL20 in psoriasis: A potential biomarker of disease severity, inflammation, and impaired vascular health. Journal of the American Academy of Dermatology, 84(4), pp.913-920.

Gautier, L., Cope, L., Bolstad, B.M. and Irizarry, R.A., (2004). affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20(3), pp.307-315.

Henry, L., Le Gallic, L., Garcin, G., Coux, O., Jumez, N., Roger, P., Lavabre‐Bertrand, T., Martinez, J., Meunier, L. and Stoebner, P.E., (2011). Proteolytic activity and expression of the 20S proteasome are increased in psoriasis lesional skin. British Journal of Dermatology, 165(2), pp.311-320.

Huang, P.L., (2009). A comprehensive definition for metabolic syndrome. Disease models & mechanisms, 2(5-6), pp.231-237.

Huang, Y.H., Kuo, C.F., Huang, L.H. and Hsieh, M.Y., (2019). Familial aggregation of psoriasis and co-aggregation of autoimmune diseases in affected families. Journal of Clinical Medicine, 8(1), p.115.

Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. and Morishima, K., (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic acids research, 45(D1), pp.D353-D361.

Keermann, M., Kõks, S., Reimann, E., Prans, E., Abram, K. and Kingo, K., (2015). Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN. BMC genomics, 16, pp.1-11.

Lowes, M.A., Suarez-Farinas, M. and Krueger, J.G., (2014). Immunology of psoriasis. Annual review of immunology, 32, pp.227-255.

Mabuchi, T., Chang, T.W., Quinter, S. and Hwang, S.T., (2012). Chemokine receptors in the pathogenesis and therapy of psoriasis. Journal of dermatological science, 65(1), pp.4-11.

Mahil, S.K., Catapano, M., Di Meglio, P., Dand, N., Ahlfors, H., Carr, I.M., Smith, C.H., Trembath, R.C., Peakman, M., Wright, J. and Ciccarelli, F.D., (2017). An analysis of IL-36 signature genes and individuals with IL1RL2 knockout mutations validates IL-36 as a psoriasis therapeutic target. Science translational medicine, 9(411), p.eaan2514.

Meephansan, J., Subpayasarn, U., Komine, M. and Ohtsuki, M., (2017). Pathogenic role of cytokines and effect of their inhibition in psoriasis. IntechOpen: Rijeka, Croatia, pp.41-57.

Melero, J.L., Andrades, S., Arola, L. and Romeu, A., (2018). Deciphering psoriasis. A bioinformatic approach. Journal of Dermatological Science, 89(2), pp.120-126.

Menter, M.A., Armstrong, A.W., Gordon, K.B. and Wu, J.J., (2018). Common and not-so-common comorbidities of psoriasis. Semin Cutan Med Surg, 37(2S), pp.48-51.

Mercurio, L., Morelli, M., Scarponi, C., Eisenmesser, E.Z., Doti, N., Pagnanelli, G., Gubinelli, E., Mazzanti, C., Cavani, A., Ruvo, M. and Dinarello, C.A., (2018). IL-38 has an anti-inflammatory action in psoriasis and its expression correlates with disease severity and therapeutic response to anti-IL-17A treatment. Cell death & disease, 9(11), p.1104.

Nayak, S., Pradhan, D., Singh, H. and Reddy, M.S., (2019). Computational screening of potential drug targets for pathogens causing bacterial pneumonia. Microbial pathogenesis, 130, pp.271-282.

Nickoloff, B.J. and Nestle, F.O., (2004). Recent insights into the immunopathogenesis of psoriasis provide new therapeutic opportunities. The Journal of clinical investigation, 113(12), pp.1664-1675.

Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F. and Furlong, L.I., (2016). DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research, p.gkw943.

Rangaraj, A., Ye, L., Sanders, A.J., Price, P.E., Harding, K.G. and Jiang, W.G., (2017). Molecular and cellular impact of Psoriasin (S100A7) on the healing of human wounds. Experimental and Therapeutic Medicine, 13(5), pp.2151-2160.

Ruano, J., Suárez-Fariñas, M., Shemer, A., Oliva, M., Guttman-Yassky, E. and Krueger, J.G., (2016). Molecular and cellular profiling of scalp psoriasis reveals differences and similarities compared to skin psoriasis. PLoS One, 11(2), p.e0148450.

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T., (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11), pp.2498-2504.

Singh, S., Pradhan, D., Puri, P., Ramesh, V., Aggarwal, S., Nayek, A. and Jain, A.K., (2019). Genomic alterations driving psoriasis pathogenesis. Gene, 683, pp.61-71.

Singh, T.P., Lee, C.H. and Farber, J.M., (2013). Chemokine receptors in psoriasis. Expert opinion on therapeutic targets, 17(12), pp.1405-1422.

Swindell, W.R., Sarkar, M.K., Liang, Y., Xing, X., Baliwag, J., Elder, J.T., Johnston, A., Ward, N.L. and Gudjonsson, J.E., (2017). RNA-seq identifies a diminished differentiation gene signature in primary monolayer keratinocytes grown from lesional and uninvolved psoriatic skin. Scientific reports, 7(1), p.18045.

Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P. and Jensen, L.J., (2019). STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research, 47(D1), pp.D607-D613.

Takeshita, J., Grewal, S., Langan, S.M., Mehta, N.N., Ogdie, A., Van Voorhees, A.S. and Gelfand, J.M., (2017). Psoriasis and comorbid diseases: epidemiology. Journal of the American Academy of Dermatology, 76(3), pp.377-390.

Towne, J.E. and Sims, J.E., (2012). IL-36 in psoriasis. Current opinion in pharmacology, 12(4), pp.486-490.

Wang, Y. and Zheng, T., (2014). Screening of hub genes and pathways in colorectal cancer with microarray technology. Pathology & Oncology Research, 20, pp.611-618.

Yang, Y., Zhong, Z., Ding, Y., Zhang, W., Ma, Y. and Zhou, L., (2018). Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure. Genes & Diseases, 5(4), pp.349-357.

Yu, G., Wang, L.G., Han, Y. and He, Q.Y., (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics: a journal of integrative biology, 16(5), pp.284-287.

Zhang, Y.J., Sun, Y.Z., Gao, X.H. and Qi, R.Q., (2019). Integrated bioinformatic analysis of differentially expressed genes and signalling pathways in plaque psoriasis. Molecular medicine reports, 20(1), pp.225-235.