Analysis of communal Pathogenesis and Immune Infiltration Characteristics Between Psoriasis and Pulmonary Arterial Hypertension

doi:10.21203/rs.3.rs-2020972/v1

Background

Increasingly evidence has shown pulmonary arterial hypertension (PAH) was predisposed to occur in psoriasis, however, the common mechanism of this phenomenon is still not fully clarified. This study aims to further explore the molecular mechanisms of this complication.

Methods

Four datasets were downloaded from the Gene Expression Omnibus (GEO) database based on the study inclusion/exclusion criteria. After screening the communal DEGs, modules, and hub genes of psoriasis and PAH, subsequent bioinformatic analyses, consisting of function annotation analysis, co-expression analysis, drug-gene interaction prediction, and mRNA–miRNA regulation network construction were conducted. Moreover, Immune cell infiltration analysis and correlation analysis were performed to further uncover the related immune pathogenesis in psoriasis and PAH.

Results

170 communal DEGs, 4 modules, and 6 hub genes were identified between GSE15197 and GSE30999, and the expression of hub genes was verified in the GSE41662 and GSE113439 respectively. The function annotation analysis of these genes mainly enriched in the Immune System and associated signal transduction, and the immune cell infiltration analysis highlighted the existence of the overlap in terms of mast cells between PAH and psoriasis.

Conclusions

The analysis of communal DEGs, modules, and hub genes underlined the potential role of the immune system and associated signal transduction in the common pathogenesis of psoriasis and PAH, and immune Infiltration analysis of two diseases provide us with new perspectives and exploring direction. Moreover, six hub genes (MYO5A, CDT1, ASPM, ACTR2, PTPN11, and SOST) may be used as biomarkers or therapeutic targets in psoriasis and PAH.

psoriasis

pulmonary arterial hypertension

hub genes

immune infiltration

mast cell

Psoriasis, a chorion, painful, and no-curse inflammatory skin disease affect over 60 million children and adults worldwide (1), and the prevalence of psoriasis is known in 19% of countries (2, 3). Pulmonary arterial hypertension (PAH) is commonly defined by hemodynamics, namely a mean pulmonary arterial pressure higher than 20 mmHg at rest(4). Idiopathic Pulmonary Arterial Hypertension (IPAH), is a subtype of PAH, which specific causes are unknown but is usually known as correlated with genetics and is available to target therapy (5). Intriguingly, genetic components are essential for psoriasis as well, and the most convincing evidence is that the concordance rate of dizygotic twins is about 20% and approximately 70% for monozygotic twins (6). Nowadays, increasing evidence has shown that psoriasis is not just restricted to the skin, it is also observed with many comorbidities, including psoriasis arthritis, metabolic syndrome, PAH, and so on (7–9). Gunes et al performed a study on 47 psoriatic patients and 20 healthy comparisons and found that psoriatic patients suffered significantly higher mild PAH than controls (31.9% vs 0%, p = 0.003.) (10). Young et al make a retrospective cohort study of Kaiser Permanente Southern California members and covered that patients with severe psoriasis had a higher risk of developing PAH (9). Hence, there may be latent biological crosstalk between Psoriasis and PAH, yet the potential common molecular mechanism is still far from elucidated, they could become potential biomarkers or therapeutic directions.

Pathologically, psoriasis is commonly known as the chronic process of the interaction between highly proliferating keratinocytes and infiltrating activated immune cells. Clinically, with patient administration of immune suppressive agents, like denileukin difititox and immunosuppressive biological agents, their diseases were significantly ameliorating to some extent. Molecularly, the lymphocytes, particularly Th1 and Th17 cells, macrophages, and innate immune cells infiltrated the skin heavily, which is also a hallmark of psoriatic lesions (11). For the infiltrating of immune cells, researchers just seem as the consequence of the hyper-proliferative keratinocytes initially. Recently, increasingly cellular and molecular studies have identified that psoriasis is a largely T lymphocyte-mediated disease, and the innate immune cells and pathogenic T cells activation were the main course of the skin inflammation and hyperproliferation of keratinocytes. It is strikingly noticeable that immune cell infiltration is also essential in the pathogenesis of IPAH. Failure to resolve inflammation and altered immune processes were identified as the contribution to the development of IPAH (12). Also, IL-17-mediated vasculopathy and coagulopathies both in vivo and in vitro may increase the cardiovascular risk in patients with psoriasis(13, 14). Xu et al investigated the immune landscape of IPAH and explored that the upregulation of the transforming growth factor-β (TGF-β) signaling pathway and Wnt signaling pathway was related to the IPAH(15). However, whether there is a common immune pathway in psoriasis associated with IPAH was unknown.

Therefore, identifying the genetic factors that are susceptible to the pathogenesis of both psoriasis and IPAH is indispensable. In addition, digging out how immune-mediated the phenotype of psoriasis is complicated with IPAH is also warranted. Our studies focused on the hub genes associated with the pathogenesis of psoriasis complicated with IPAH and used multiple bioinformatics to analyze the relationship between psoriasis and IPAH. Thus, expected to provide new insights into the biological mechanisms of both two diseases and a new direction for precision medicine to cluster particular patients with a biological profile capable to manifest cardiovascular comorbidities such as pulmonary hypertension.

1.1 Data source

GEO (http://www.ncbi.nlm.nih.gov/geo) is a public database containing high-throughput sequencing and microarray datasets, which was created by the US National Center for Biotechnology Information (16). Psoriasis and IPAH microarray datasets were screened based on strict inclusion/exclusion criteria in GEO. Inclusion criteria were as follows: [1] sporadic Psoriasis or IPAH; [2] datasets consist of patients and healthy controls, and should include the largest possible sample size; [3] the test specimens in the datasets should be taken from human tissues. Exclusion criteria include: Patients had participated in a clinical trial for drugs or other treatments. Finally, 4 gene-expression datasets were obtained from GEO among inclusion/exclusion criteria. GSE30999(85 lesional skin and 85 non-lesional skin) (17) and GSE41662(24 lesional skin and 24 non-lesional skin) (18) were selected as the Psoriasis datasets. In the IPAH group, GSE15197 (18 patients with IPAH and 13 controls) (19) and GSE113439 (6 patients with IPAH and 11 controls) (20) were chosen. GSE30999 and GSE15197 were used to screen DEGs, while GSE41662 and GSE113439 were used for subsequent gene validation.

1.2 Identification of DEGs

The GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is a network tool that come with GEO and works based on the Limma package and GEO query (21). With the conditions of P < 0.05 and |logFC| > 1, Psoriasis and IPAH DEGs were identified via GEO2R respectively. After that, the overlapping DEGs of the Psoriasis and IPAH datasets were detected and visualized by VEEN diagrams.

1.3 Enrichment analyses of communal DEGs

Gene Ontology (GO) enrichment analyses were performed for the overlapping DEGs to analyze the communal biological functions based on DAVID 6.8 (https://david.ncifcrf.gov/)(22). The pathway enrichment of the overlapping DEGs from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome, and Panther) were analyzed via KOBAS version 3.0. P < 0.05 was statistically significant(23).

1.4 Protein-protein interaction network construction and module analysis

Protein-protein interaction (PPI) network was used to reveal the interactions of proteins and identify the core protein modules and genes. We predict and construct the PPI network of the overlapping DEGs based on the STRING (http://string-db.org, version 11.0) database with a combined score > 0.4(24). The PPI network was visualized via Cytoscape (version 3.7.0) Software(25). Subsequently, the MCODE plugin was adopted to identify densely connected modules from the PPI network with the criteria of K-core = 2, degree cutoff = 2, max depth = 100, and node score cutoff = 0.2(26). The functional annotation of genes in each module was conducted based on Metascape (https://metascape.org).

1.1 Hub gene selection, analyses, and validation

The hub genes were identified via the CytoHubb, a Cytoscape plugin was used to select the hub proteins or genes in the PPI network(27). We randomly adopt 5 of the 12 algorithms in CytoHubba and take the intersection of the 5 algorithms’ results to determine the hub gene. Subsequently, we conduct function annotation of hub genes based on Metascape(28), and further pathway enrichment was obtained by KOBAS 3.0. Moreover, a co-expression network of hub genes was predicted and constructed by GeneMANIA (http://www.genemania.org/), which is a credible bioinformatic tool for analyzing gene list function and internal associations disinterment(29). After that, we predicted the subcellular localization of proteins encoded by the hub genes via the WoLF PSORT platform(30). Based on UniProt and the GO database, WoLF PSORT could predict the subcellular localization from the amino acid sequences of hub genes by sorting signals, functional motifs, and amino acid composition. Finally, the drug-gene of hub genes were predicted by DGIdb 3.0 (http://www.dgidb.org/) and visualized by Cytoscape(31). The hub gene expressions were verified in GSE41662 and GSE113439 respectively. A comparison between the two sets of data was performed with the t-test. P < 0.05 was statistically significant.

1.2 Construction of the TF–mRNA–miRNA regulatory network

Mirwalk, which is mainly centered on miRNA-target interactions(32), was adopted to predict the miRNA of the hub gene in this research with the strict condition that the predicted miRNA could be verified by other databases or experiments. TRRUST database is a convenient database that contains the regulatory relationships between TFs and targets. We predict the TFs of the hub gene based on the TRRUST database in this research(33). After the prediction of TF-mRNA-miRNA, the regulatory network was constructed and visualized by Cytoscape software.

1.3 Evaluation of immune cell infiltration

Based on the CIBERSORT algorithm, the difference in immune infiltration of 22 immune cell subgroups in IPAH samples and control samples was analyzed. We draw violin diagrams to visualize the outcome using the ggplot2 package. After that, we analyze and visualize the difference in immune infiltration of 22 immune cell subgroups in psoriasis LS samples and NS samples in the same way.

1.4 Correlation analysis of hub genes and infiltrating immune cells

Spearman correlation analysis was conducted on hub genes and infiltrating immune cells using the ggstatsplot package, and the results were visualized by the ggplot2 package.

2.1 Identification of DEGs

The study design flow chart is shown in Figure 1. After standardization of the microarray results, DEGs in Psoriasis and IPAH were selected. Compared to the controls, 2294 genes (1192 upregulated and 1102 downregulated genes) in GSE15197 were identified as DEGs in patients with IPAH (Figure 2A). While in the Psoriasis group, there were 2866 DEGs in the GSE30999 dataset, including 1649 upregulated and 1217 downregulated genes (Figure 2B). We take the intersection and obtain 170 communal DEGs (90 upregulated and 80 downregulated genes) between DEGs in GSE15197 and GSE30999 (Figure 2C, D).

2.2 Enrichment analyses of communal DEGs in psoriasis and IPAH

To unearth the potential communal biological roles of Psoriasis and IPAH, the GO enrichment analysis of the overlapping DEGs was conducted (Figure 3A, B). Results were divided into three functional categories, including biological processes (BP), cell component (CC), and molecular function (MF). In the BP group, DEGs were significantly enriched in cardiac right ventricle morphogenesis (GO:0003215), positive regulation of canonical Wnt signaling pathway (GO:0090263), and face morphogenesis (GO:0060325), T cell differentiation (GO:0030217) and signal transduction (GO:0007165). As for CC, DEGs were mainly involved in the dendrite (GO:0030425), cell junction (GO:0030054), postsynaptic membrane (GO:0045211), and extracellular region (GO:0005576), and cytoplasmic vesicle (GO:0031410). In terms of MF, DEGs were mainly enriched in protein transporter activity (GO:0008565), SMAD binding (GO:0046332), ubiquitin-protein ligase activity (GO:0061630), co-receptor binding (GO:0039706), and protein heterodimerization activity (GO:0046982). Meanwhile, according to the pathway enrichment analysis performed by KOBAS 3.0, the overlapping DEGs were particularly enriched in Immune System, Adaptive Immune System, Cytokine Signaling in the Immune system, and Signal Transduction (Figure 3C).

2.3 Protein-protein interaction network construction and module analysis

Based on the STRING database, the PPI network of communal DEGs was constructed and visualized via Cytoscape, consisting of 83 nodes and 104 edges (Figure 4). Three important modules were obtained from the PPI network by the MCODE plugin in Cytoscape, and functional annotation of genes in each module was conducted based on Metascape (Figure 5). The genes in module A mainly enriched in negative regulation of mitotic cell cycle phase transition (GO:1901991) and cellular response to DNA damage stimulus (GO:0006974). As for module B, genes were significantly enriched in Negative regulation of TCF-dependent signaling by WNT ligand antagonists (R-HAS-3772470) and negative regulation of canonical Wnt signaling pathway (GO:0090090). While in terms of module C, genes were mainly involved in PID TCR Pathway (M34).

2.4 Hub gene selection, analysis, and validation

Five algorithms (Betweenness, BottleNeck, DMNC, EcCentricity, Radiality) in the CytoHubba plugin were adopted to identify hub genes in this article, and the top 20 genes were selected by each classification method are listed in Table 1. Subsequently, 6 hub genes (MYO5A, CDT1, ASPM, ACTR2, PTPN11, and SOST) were determined by overlapping the top 20 genes in the above five algorithms (Figure 6).

To reveal the biological role and mechanism of 6 hub genes, function annotation was performed by Metascape and further pathway enrichment analyses were conducted based on KOBAS 3.0 (Figure 7A, B). Similar to the outcome of DEGs enrichment analysis, hub genes were found to be associated with the immune system and immune-related signaling pathways. In addition, a network of hub genes and their co-expression genes was constructed by the GeneMAINA platform (Figure 7C). Six hub genes showed a complex PPI network with co-expression of 89.77%, prediction of 8.97%, and shared protein domains of 1.26%. the functions of the network also emphasized the important role of the immune system. Based on the WoLF PSORT platform, the subcellular localization of proteins encoded by the 6 hub genes was predicted. CDT1 and PTPN11 could exist in the nucleus, MYO5A could exist in the cytosol, ASPM and ACTR2 are located in both cytosol and nucleus and SOST could be in extracellular areas. After that, 10 drug-gene interaction pairs were predicted on the grounds of the DGIdb database, consisting of 3 hub genes (PTPN11, SOST, and ACTR2) and 10 drugs (Figure 7D).

To ensure the reliability and accuracy of the bioinformatics analysis results, GSE41662 and GSE113439 were used to verify the expression of hub genes in Psoriasis and IPAH samples by independent testing analysis respectively. Compared with non-lesional skin tissue, the expression of 6 hub genes in lesional skin tissue was significantly higher. In IPAH lung tissue, MYO5A, ASPM, ACTR2, and PTPN11 were significantly upregulated compared to in normal lung tissue, However, SOST was not seemed to change significantly and CDT1 shows a downregulated trend (Figure 8).

2.5 Construction of the TF-mRNA-miRNA regulatory network

Based on the Mirwalk database, a total of 80 miRNAs were found with the condition that predicted miRNA could be verified by other databases or experiments. 5 TFs that could regulate the expression of hub genes were predicted on the grounds of the TRRUST database. After the prediction of TFs and miRNAs, the TF-mRNA-miRNA regulatory network was constructed and visualized using Cytoscape software (Figure 9).

2.6 Evaluation of immune cell infiltration

On the ground of the CIBERSORT algorithm, we analyzed the immune infiltration of 22 immune cell subgroups difference in IPAH samples and control samples (Figure 10A). The violin chart shows that compared with the normal control sample, there are more CD8 T cells, and activated Mast cells in the IPAH samples, but fewer T regulatory cells, resting mast cells, and neutrophils. As for psoriasis (Figure 11A), there are more CD4 memory-activated T cells, T follicular helper cells, T gamma delta cells, M1 macrophages, resting dendritic cells, activated dendritic cells, and neutrophils in lesional tissue compared to non-lesional tissue, but fewer plasma cells, CD8 T cells, CD4 naïve T cells, resting NK cells, activated NK cells, M2 macrophages and resting mast cells. The analysis outcome of Immune Cell Infiltration seems to exist overlap in terms of mast cells between IPAH and psoriasis.

2.7 Correlation analysis of hub genes and infiltrating immune cells

Correlation heatmap of the 22 types of immune cells revealed that resting Mast cells had a significant positive correlation with activated dendritic cells and resting NK cells and had a significant negative correlation with activated T regulatory cells in IPAH (Figure 10B). As for psoriasis (Figure 11B), resting mast cells had a significant positive correlation with M0 macrophages and activated mast cells, and had a significant negative correlation with resting dendritic cells, CD4 naïve T cells, B memory cells, T follicular helper cells, M1 macrophages, and T gamma delta cells.

In terms of IPAH (Figure 10C), Correlation analysis showed that SOST was positively correlated with mast cells activated; CDT1 was negatively correlated with plasma cells and resting dendritic cells; PTPN11 was negatively correlated with M1 macrophages and B memory cells; ASPM was negatively correlated with resting dendritic cells, but was positively correlated with CD4 naïve T cells; MYO5A was negatively correlated with resting dendritic cells, but was positively correlated with T follicular helper cells; ACTR2 was negatively correlated with resting dendritic cells and B memory cells, but was positively correlated with resting CD4 memory T cells and M0 macrophages. As for psoriasis (Figure 11C), SOST, MYO5A, CDT1, and ASPM were negatively correlated with plasma cells, resting dendritic cells, resting NK cells, and CD4 naive T cells, but were positively correlated with naïve B cells, M0 macrophages, B memory cells, T regulatory cells, and T gamma delta cells, resting T memory cells. Besides, MYO5A was also negatively correlated with CD8 T cells and positively correlated with T follicular helper cells and eosinophils; CDT1 was also positively correlated with monocytes and eosinophils; ASPM was also negatively correlated with CD8 T cells; ACTR2 was negatively correlated with M1 macrophages, resting dendritic cells and CD4 naïve T cells, but was positively correlated with monocytes, B memory cells, B naïve cells, M0 macrophages, activated CD4 memory T cells and T gamma delta cells; PTPN11 was negatively correlated with activated mast cells, resting dendritic cells, CD8 T cells and plasma cells, but was positively correlated with M0 macrophages and T regulatory cells.

In order to uncover the underlying common pathogenetic mechanisms of psoriasis and IPAH, we identified 170 overlapping DEGs between GSE30999 and GSE15197 and construct the PPI network of the communal DEGs. After that, 4 modules and 6 hub genes were determined based on the plugins and algorithms in Cytoscape. Subsequent analysis was conducted on the hub genes to explore the communal biological mechanism and reveal the potential target, including function annotation analysis, co-expression analysis, drug-gene interaction prediction, and mRNA-miRNA regulation network construction. Moreover, we verify the expression of hub genes in GSE41662 and GSE113439 respectively, and we retain some doubts about the critical role of SOST and CDT1 in IPAH. Cause the gene function annotations of communal DEGs, genes in 4 module and hub genes were all pointed to the immune system and associated signal transduction, Immune cell infiltration analysis and correlation analysis of hub genes and infiltrating immune cells were performed to further uncover the related immune pathogenesis in psoriasis and IPAH.

Both psoriasis and IPAH pathogenesis are inseparable from inflammation and immunity. Psoriasis is associated with several comorbidities including obesity, metabolic system, coronary artery disease, Type 2 diabetes Mellitus, elevated risk of myocardial infarction, PAH, and so on(9). Mleczko et al suggested that systemic inflammation in psoriasis may be associated with the development of comorbidities(34). However, based on our analysis, the GO and pathway analysis of communal DEGs and hub genes highlighted the important position of the immune system. Therefore, we perform immune infiltration analysis of psoriasis and IPAH. We found in the IPAH, that the CD8⁺T cells and activating mast cell proportion were higher than in control. On the contrary, the T regulatory cells (Tregs), resting mast cells, and neutrophils were lower. For psoriasis, the proportion of immune cells varied greatly. Remarkably, the proportion of resting mast cells is also lower than the control. Consistently, zhang et al used the CIBERSORT algorithm observed a dramatic change in immune cell profiles in psoriatic skin compared with healthy skin, and the resting mast cell is almost eliminated in psoriatic skin(35). Meanwhile, bartelds et al used a rat model of flow-associated PAH mimicking IPAH associated with congenital heart disease, identifying an increased expression of mast cell markers and confirming the increased presence of mast cells with histology(36). After further exploration, they presented that mast cell stabilization with cromolyn attenuated pulmonary vascular remodeling and lower activity of chymase was related to more favorable pulmonary vascular remodeling and right ventriculus hemodynamics(37). Intriguingly, early mast cell stabilization may attenuate psoriatic skin in psoriasis and pulmonary vascular remodeling in IPAH.

MYO5A (myosin VA), one of three myosin V heavy-chain genes, is a member of the myosin gene superfamily. Previous studies have demonstrated that MYO5A is responsible for transporting various endoplasmic reticulum-derived vesicles(38). Liu et al have reported that myosin Va plays a critical role in the mast cell intracellular vesicle trafficking, and this is the target of Zoledronate to interfere with the mast cell release of secretory granules(39). Our analysis pointed out that MYO5A is upregulated in the psoriasis skin and IPAH, although little direct evidence shows that MYO5A is essential in both, the indirect evidence suggests that MYO5A took part in the mast cell secretory granules releasing, which may provide a new sight for investigators to research seriously.

PTPN11 (protein tyrosine phosphatase non-receptor type 11), encodes the nonreceptor protein tyrosine phosphatase SHP2, which is expressed universally and worked as a scaffolding adaptor protein and protein tyrosine phosphatase (PTP)(40). SHP2 integrated growth and differentiation signals from the receptor tyrosine kinases (RTKs) into the RAS/MAPK cascade in cancer(41).In the skin, SHP2 is inactive in the basal lamina, however, activated as a phosphatase to elicit downstream signaling when combined with a phosphotyrosine protein(42). Noonan syndrome is a congenital heart disease mainly caused by the acquisition of mutations in SHP2 function. Reportedly, a 55-year-old woman with it was shown to have annular pustular psoriasis(43), which prompted psoriasis may have some unknown association with IPAH in SHP2. Zhu et al declared the critical role of SHP2 in psoriasis pathogenesis: SHP2 dephosphorylated TLR7 at Tyr1024 to promote its ubiquitination in macrophages and trafficking to the endosome(44). Meanwhile, Sharma et al point out that SHP2 signaling downstream of KIT is essential for mast cell survival and homeostasis in mice(45). For the IPAH, Courboulin A et al found SHP2 as the target of miR-204 and maintains pulmonary arterial smooth muscle cells (PASMCs) proliferation and anti-apoptotic characteristics(46). In addition, Cheng et al identified SHP2 as indispensable in the development of PAH through MCT-induced PAH rats, including contributing to maladaptive remodeling of RV, inhibition of SHP2 effectively reducing the pulmonary vascular remodeling, and restraining Platelet-derived growth factor-induced proliferation and migration of human PASMCs(47). Intriguingly, this may owe to the SHP2 functions in defects in cytochrome c oxidase activity, reactive oxygen species, and mitochondrial membrane potential in fibroblasts(48).

ASPM (Abnormal spindle-like microcephaly-associated) gene encodes a spindle protein, which is a centrosomal protein and plays a crucial role in mitotic spindle regulation, neurogenesis, and brain size regulation(49, 50). Studies had been shown that ASPM was associated with multiple types of cancers(51–55). In our studies, we explored the ASPM overexpression in IPAH and psoriasis, since ASPM promotes homologous recombination-mediated DNA repair(56), overexpression of ASPM may restain the cells programmed cell death to influence both IPAH and psoriasis. Inhibition of ASPM expression increased DNA damage and sensitization of cells to ionizing irradiation, which may be the potential therapeutic strategies and early diagnosis of the disease.

ACTR2 (Actin-related protein 2) is a unique component of the human actin-related protein 2/3 complex (Arp2/3 complex). Arp2/3 complex, a key component of actin cytoskeleton rearrangement, plays an important role in nucleating new actin filaments and forming branched actin networks(57). Van der Kammen et al reported that depleting the Arp2/3 complex in mouse epidermis affects epidermal morphogenesis and homeostasis by keratinocyte’s shape and transcriptome changing(58). Meanwhile, Bolger-Munro et al indicated the inhibition of the Arp2/3 complex decreases the BCR signaling and micro-cluster merge events, and the latter may more efficiently shield activated BCRs from negative regulators(59). Coherently, Zamanian et al conducted a clinical trial and found that the B-cell depletion therapy is a potentially effective and safe adjuvant treatment for PAH(60). Similarly, human psoriasis treatment with recombinant IL-10 could improve disease symptoms, and IL-10 is produced by B cells(61). Thus, ACTR2 may facilitate psoriasis and IPAH by promoting the BCR signaling pathway. Nevertheless, the detailed mechanism urgently needs to be proved deeply.

The highlight of this article is to explore the pathogenesis and Immune Infiltration Characteristics in Psoriasis complicated with PAH. However, there are still some limitations: despite this microarray data analysis having a certain sample size and the expression of hub genes have been verified in other gene expression datasets, this article belongs to a retrospective study which still needs external verification to further verify our conclusion. Moreover, the biological roles of these genes still need to be further verified in in-vivo and in-vitro models.

Summarily, the analysis of communal DEGs, modules, and hub genes highlighted the potential role of the immune system and associated signal transduction in the common pathogenesis of psoriasis and IPAH, and immune Infiltration analysis of two diseases provides us with new perspectives and exploring direction. Moreover, a total of six hub genes (MYO5A, CDT1, ASPM, ACTR2, PTPN11, and SOST) have been screened, which could be used as biomarkers or therapeutic targets.

Ethical Approval

Not applicable.

Competing interests

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

Authors’ contributions

All authors made contributions to conception and design. Zuoxiang wang and wengxing su acquired, analyzed, and interpreted the data. Qingyue xia drafted the article and revised it critically. Dan Luo and wengxing su gave final approval of the version and fund support. All authors agreed to submit to the current journal and account for all aspects of the work.

Funding

This work was sponsored by the National Natural Science Foundation of China (81972961).

Availability of data and materials

All datasets came from the GEO database.

Acknowledgments

We would like to sincerely thank the Core Facility of the First Affiliated Hospital of Nanjing Medical University for the help in this work.

Michalek IM, Loring B, John SM. A Systematic Review of Worldwide Epidemiology of Psoriasis. J Eur Acad Dermatol Venereol (2017) 31(2):205–12. Epub 2016/08/31. doi: 10.1111/jdv.13854.
Parisi R, Iskandar IYK, Kontopantelis E, Augustin M, Griffiths CEM, Ashcroft DM, et al. National, Regional, and Worldwide Epidemiology of Psoriasis: Systematic Analysis and Modelling Study. BMJ (2020) 369:m1590. Epub 2020/05/30. doi: 10.1136/bmj.m1590.
Eder L, Widdifield J, Rosen CF, Cook R, Lee KA, Alhusayen R, et al. Trends in the Prevalence and Incidence of Psoriasis and Psoriatic Arthritis in Ontario, Canada: A Population-Based Study. Arthritis Care Res (Hoboken) (2019) 71(8):1084–91. Epub 2018/09/02. doi: 10.1002/acr.23743.
Simonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M, et al. Haemodynamic Definitions and Updated Clinical Classification of Pulmonary Hypertension. Eur Respir J (2019) 53(1). Epub 2018/12/14. doi: 10.1183/13993003.01913-2018.
Kovacs G, Dumitrescu D, Barner A, Greiner S, Grunig E, Hager A, et al. Definition, Clinical Classification and Initial Diagnosis of Pulmonary Hypertension: Updated Recommendations from the Cologne Consensus Conference 2018. Int J Cardiol (2018) 272S:11 – 9. Epub 2018/09/17. doi: 10.1016/j.ijcard.2018.08.083.
Bowcock AM. The Genetics of Psoriasis and Autoimmunity. Annu Rev Genomics Hum Genet (2005) 6:93–122. Epub 2005/08/30. doi: 10.1146/annurev.genom.6.080604.162324.
Azfar RS, Gelfand JM. Psoriasis and Metabolic Disease: Epidemiology and Pathophysiology. Curr Opin Rheumatol (2008) 20(4):416–22. Epub 2008/06/06. doi: 10.1097/BOR.0b013e3283031c99.
Nestle FO, Kaplan DH, Barker J. Psoriasis. N Engl J Med (2009) 361(5):496–509. Epub 2009/07/31. doi: 10.1056/NEJMra0804595.
Choi YM, Famenini S, Wu JJ. Incidence of Pulmonary Arterial Hypertension in Patients with Psoriasis: A Retrospective Cohort Study. Perm J (2017) 21:16–073. Epub 2017/07/06. doi: 10.7812/TPP/16-073.
Gunes Y, Tuncer M, Calka O, Guntekin U, Akdeniz N, Simsek H, et al. Increased Frequency of Pulmonary Hypertension in Psoriasis Patients. Arch Dermatol Res (2008) 300(8):435–40. Epub 2008/05/01. doi: 10.1007/s00403-008-0859-9.
Gran F, Kerstan A, Serfling E, Goebeler M, Muhammad K. Current Developments in the Immunology of Psoriasis. Yale J Biol Med (2020) 93(1):97–110. Epub 2020/04/01.
Rabinovitch M, Guignabert C, Humbert M, Nicolls MR. Inflammation and Immunity in the Pathogenesis of Pulmonary Arterial Hypertension. Circ Res (2014) 115(1):165–75. Epub 2014/06/22. doi: 10.1161/CIRCRESAHA.113.301141.
Gangwar RS, Gudjonsson JE, Ward NL. Mouse Models of Psoriasis: A Comprehensive Review. J Invest Dermatol (2022) 142(3 Pt B):884 – 97. Epub 2021/12/27. doi: 10.1016/j.jid.2021.06.019.
Xu J, Yang Y, Yang Y, Xiong C. Identification of Potential Risk Genes and the Immune Landscape of Idiopathic Pulmonary Arterial Hypertension Via Microarray Gene Expression Dataset Reanalysis. Genes (Basel) (2021) 12(1). Epub 2021/01/23. doi: 10.3390/genes12010125.
Guignabert C, Humbert M. Targeting Transforming Growth Factor-Beta Receptors in Pulmonary Hypertension. Eur Respir J (2021) 57(2). Epub 2020/08/21. doi: 10.1183/13993003.02341-2020.
Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: Ncbi Gene Expression and Hybridization Array Data Repository. Nucleic Acids Res (2002) 30(1):207–10. Epub 2001/12/26. doi: 10.1093/nar/30.1.207.
Suarez-Farinas M, Li K, Fuentes-Duculan J, Hayden K, Brodmerkel C, Krueger JG. Expanding the Psoriasis Disease Profile: Interrogation of the Skin and Serum of Patients with Moderate-to-Severe Psoriasis. J Invest Dermatol (2012) 132(11):2552–64. Epub 2012/07/06. doi: 10.1038/jid.2012.184.
Bigler J, Rand HA, Kerkof K, Timour M, Russell CB. Cross-Study Homogeneity of Psoriasis Gene Expression in Skin across a Large Expression Range. PLoS One (2013) 8(1):e52242. Epub 2013/01/12. doi: 10.1371/journal.pone.0052242.
Rajkumar R, Konishi K, Richards TJ, Ishizawar DC, Wiechert AC, Kaminski N, et al. Genomewide Rna Expression Profiling in Lung Identifies Distinct Signatures in Idiopathic Pulmonary Arterial Hypertension and Secondary Pulmonary Hypertension. Am J Physiol Heart Circ Physiol (2010) 298(4):H1235-48. Epub 2010/01/19. doi: 10.1152/ajpheart.00254.2009.
Mura M, Cecchini MJ, Joseph M, Granton JT. Osteopontin Lung Gene Expression Is a Marker of Disease Severity in Pulmonary Arterial Hypertension. Respirology (2019) 24(11):1104–10. Epub 2019/04/10. doi: 10.1111/resp.13557.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. Ncbi Geo: Archive for Functional Genomics Data Sets–Update. Nucleic Acids Res (2013) 41(Database issue):D991-5. Epub 2012
/11/30
. doi: 10.1093/nar/gks1193.
Huang da W, Sherman BT, Lempicki RA. Systematic and Integrative Analysis of Large Gene Lists Using David Bioinformatics Resources. Nat Protoc (2009) 4(1):44–57. Epub 2009/01/10. doi: 10.1038/nprot.2008.211.
Wu J, Mao X, Cai T, Luo J, Wei L. Kobas Server: A Web-Based Platform for Automated Annotation and Pathway Identification. Nucleic Acids Res (2006) 34(Web Server issue):W720-4. Epub 2006/07/18. doi: 10.1093/nar/gkl167.
Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. String V9.1: Protein-Protein Interaction Networks, with Increased Coverage and Integration. Nucleic Acids Res (2013) 41(Database issue):D808-15. Epub 2012/12/04. doi: 10.1093/nar/gks1094.
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: New Features for Data Integration and Network Visualization. Bioinformatics (2011) 27(3):431–2. Epub 2010/12/15. doi: 10.1093/bioinformatics/btq675.
Bader GD, Hogue CW. An Automated Method for Finding Molecular Complexes in Large Protein Interaction Networks. BMC Bioinformatics (2003) 4:2. Epub 2003/01/15. doi: 10.1186/1471-2105-4-2.
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. Cytohubba: Identifying Hub Objects and Sub-Networks from Complex Interactome. BMC Syst Biol (2014) 8 Suppl 4:S11. Epub 2014/12/19. doi: 10.1186/1752-0509-8-S4-S11.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape Provides a Biologist-Oriented Resource for the Analysis of Systems-Level Datasets. Nat Commun (2019) 10(1):1523. Epub 2019/04/05. doi: 10.1038/s41467-019-09234-6.
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The Genemania Prediction Server: Biological Network Integration for Gene Prioritization and Predicting Gene Function. Nucleic Acids Res (2010) 38(Web Server issue):W214-20. Epub 2010/07/02. doi: 10.1093/nar/gkq537.
Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, et al. Wolf Psort: Protein Localization Predictor. Nucleic Acids Res (2007) 35(Web Server issue):W585-7. Epub 2007/05/23. doi: 10.1093/nar/gkm259.
Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, et al. Dgidb 3.0: A Redesign and Expansion of the Drug-Gene Interaction Database. Nucleic Acids Res (2018) 46(D1):D1068-D73. Epub 2017/11/21. doi: 10.1093/nar/gkx1143.
Sticht C, De La Torre C, Parveen A, Gretz N. Mirwalk: An Online Resource for Prediction of Microrna Binding Sites. PLoS One (2018) 13(10):e0206239. Epub 2018/10/20. doi: 10.1371/journal.pone.0206239.
Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. Trrust V2: An Expanded Reference Database of Human and Mouse Transcriptional Regulatory Interactions. Nucleic Acids Res (2018) 46(D1):D380-D6. Epub 2017/11/01. doi: 10.1093/nar/gkx1013.
Mleczko M, Gerkowicz A, Krasowska D. Chronic Inflammation as the Underlying Mechanism of the Development of Lung Diseases in Psoriasis: A Systematic Review. Int J Mol Sci (2022) 23(3). Epub 2022/02/16. doi: 10.3390/ijms23031767.
Zhang Y, Shi Y, Lin J, Li X, Yang B, Zhou J. Immune Cell Infiltration Analysis Demonstrates Excessive Mast Cell Activation in Psoriasis. Front Immunol (2021) 12:773280. Epub 2021/12/11. doi: 10.3389/fimmu.2021.773280.
van Albada ME, Bartelds B, Wijnberg H, Mohaupt S, Dickinson MG, Schoemaker RG, et al. Gene Expression Profile in Flow-Associated Pulmonary Arterial Hypertension with Neointimal Lesions. Am J Physiol Lung Cell Mol Physiol (2010) 298(4):L483-91. Epub 2009/12/22. doi: 10.1152/ajplung.00106.2009.
Bartelds B, van Loon RLE, Mohaupt S, Wijnberg H, Dickinson MG, Boersma B, et al. Mast Cell Inhibition Improves Pulmonary Vascular Remodeling in Pulmonary Hypertension. Chest (2012) 141(3):651–60. Epub 2011/09/24. doi: 10.1378/chest.11-0663.
Tabb JS, Molyneaux BJ, Cohen DL, Kuznetsov SA, Langford GM. Transport of Er Vesicles on Actin Filaments in Neurons by Myosin V. J Cell Sci (1998) 111 (Pt 21):3221–34. Epub 1998/10/09. doi: 10.1242/jcs.111.21.3221.
Liu S, Sahid MNA, Takemasa E, Maeyama K, Mogi M. Zoledronate Modulates Intracellular Vesicle Trafficking in Mast Cells Via Disturbing the Interaction of Myosinva/Rab3a and Sytaxin4/Vamp7. Biochem Pharmacol (2018) 151:18–25. Epub 2018/02/20. doi: 10.1016/j.bcp.2018.02.013.
Neel BG, Gu H, Pao L. The 'Shp'ing News: Sh2 Domain-Containing Tyrosine Phosphatases in Cell Signaling. Trends Biochem Sci (2003) 28(6):284–93. Epub 2003/06/27. doi: 10.1016/S0968-0004(03)00091-4.
Kerr DL, Haderk F, Bivona TG. Allosteric Shp2 Inhibitors in Cancer: Targeting the Intersection of Ras, Resistance, and the Immune Microenvironment. Curr Opin Chem Biol (2021) 62:1–12. Epub 2021/01/09. doi: 10.1016/j.cbpa.2020.11.007.
Barford D, Neel BG. Revealing Mechanisms for Sh2 Domain Mediated Regulation of the Protein Tyrosine Phosphatase Shp-2. Structure (1998) 6(3):249–54. Epub 1998/04/29. doi: 10.1016/s0969-2126(98)00027-6.
Catharino A, Daiha E, Carvalho C, Martinez D, Lima RB, D'Acri A, et al. Possible Correlations between Annular Pustular Psoriasis and Noonan Syndrome. J Eur Acad Dermatol Venereol (2016) 30(12):e195-e6. Epub 2015/12/17. doi: 10.1111/jdv.13521.
Zhu Y, Wu Z, Yan W, Shao F, Ke B, Jiang X, et al. Allosteric Inhibition of Shp2 Uncovers Aberrant Tlr7 Trafficking in Aggravating Psoriasis. EMBO Mol Med (2022) 14(3):e14455. Epub 2021/12/23. doi: 10.15252/emmm.202114455.
Sharma N, Kumar V, Everingham S, Mali RS, Kapur R, Zeng LF, et al. Sh2 Domain-Containing Phosphatase 2 Is a Critical Regulator of Connective Tissue Mast Cell Survival and Homeostasis in Mice. Mol Cell Biol (2012) 32(14):2653–63. Epub 2012/05/09. doi: 10.1128/MCB.00308-12.
Courboulin A, Paulin R, Giguere NJ, Saksouk N, Perreault T, Meloche J, et al. Role for Mir-204 in Human Pulmonary Arterial Hypertension. J Exp Med (2011) 208(3):535–48. Epub 2011/02/16. doi: 10.1084/jem.20101812.
Cheng Y, Yu M, Xu J, He M, Wang H, Kong H, et al. Inhibition of Shp2 Ameliorates Monocrotaline-Induced Pulmonary Arterial Hypertension in Rats. BMC Pulm Med (2018) 18(1):130. Epub 2018/08/09. doi: 10.1186/s12890-018-0700-y.
Lee I, Pecinova A, Pecina P, Neel BG, Araki T, Kucherlapati R, et al. A Suggested Role for Mitochondria in Noonan Syndrome. Biochim Biophys Acta (2010) 1802(2):275–83. Epub 2009/10/20. doi: 10.1016/j.bbadis.2009.10.005.
Bikeye SN, Colin C, Marie Y, Vampouille R, Ravassard P, Rousseau A, et al. Aspm-Associated Stem Cell Proliferation Is Involved in Malignant Progression of Gliomas and Constitutes an Attractive Therapeutic Target. Cancer Cell Int (2010) 10:1. Epub 2010/02/10. doi: 10.1186/1475-2867-10-1.
Buchman JJ, Durak O, Tsai LH. Aspm Regulates Wnt Signaling Pathway Activity in the Developing Brain. Genes Dev (2011) 25(18):1909–14. Epub 2011/09/23. doi: 10.1101/gad.16830211.
Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, et al. Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies Aspm as a Molecular Target. Proc Natl Acad Sci U S A (2006) 103(46):17402–7. Epub 2006/11/09. doi: 10.1073/pnas.0608396103.
Lin SY, Pan HW, Liu SH, Jeng YM, Hu FC, Peng SY, et al. Aspm Is a Novel Marker for Vascular Invasion, Early Recurrence, and Poor Prognosis of Hepatocellular Carcinoma. Clin Cancer Res (2008) 14(15):4814–20. Epub 2008/08/05. doi: 10.1158/1078-0432.CCR-07-5262.
Peng L, Liu Z, Xiao J, Tu Y, Wan Z, Xiong H, et al. Microrna-148a Suppresses Epithelial-Mesenchymal Transition and Invasion of Pancreatic Cancer Cells by Targeting Wnt10b and Inhibiting the Wnt/Beta-Catenin Signaling Pathway. Oncol Rep (2017) 38(1):301–8. Epub 2017/06/07. doi: 10.3892/or.2017.5705.
Pai VC, Hsu CC, Chan TS, Liao WY, Chuu CP, Chen WY, et al. Aspm Promotes Prostate Cancer Stemness and Progression by Augmenting Wnt-Dvl-3-Beta-Catenin Signaling. Oncogene (2019) 38(8):1340–53. Epub 2018/09/30. doi: 10.1038/s41388-018-0497-4.
Xia C, Xu X, Ding Y, Yu C, Qiao J, Liu P. Abnormal Spindle-Like Microcephaly-Associated Protein Enhances Cell Invasion through Wnt/Beta-Catenin-Dependent Regulation of Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer Cells. J Thorac Dis (2021) 13(4):2460–74. Epub 2021/05/21. doi: 10.21037/jtd-21-566.
Xu S, Wu X, Wang P, Cao SL, Peng B, Xu X. Aspm Promotes Homologous Recombination-Mediated DNA Repair by Safeguarding Brca1 Stability. iScience (2021) 24(6):102534. Epub 2021/06/19. doi: 10.1016/j.isci.2021.102534.
Gavrin A, Jansen V, Ivanov S, Bisseling T, Fedorova E. Arp2/3-Mediated Actin Nucleation Associated with Symbiosome Membrane Is Essential for the Development of Symbiosomes in Infected Cells of Medicago Truncatula Root Nodules. Mol Plant Microbe Interact (2015) 28(5):605–14. Epub 2015/01/22. doi: 10.1094/MPMI-12-14-0402-R.
van der Kammen R, Song JY, de Rink I, Janssen H, Madonna S, Scarponi C, et al. Knockout of the Arp2/3 Complex in Epidermis Causes a Psoriasis-Like Disease Hallmarked by Hyperactivation of Transcription Factor Nrf2. Development (2017) 144(24):4588 – 603. Epub 2017/11/09. doi: 10.1242/dev.156323.
Bolger-Munro M, Choi K, Scurll JM, Abraham L, Chappell RS, Sheen D, et al. Arp2/3 Complex-Driven Spatial Patterning of the Bcr Enhances Immune Synapse Formation, Bcr Signaling and B Cell Activation. Elife (2019) 8. Epub 2019/06/04. doi: 10.7554/eLife.44574.
Zamanian RT, Badesch D, Chung L, Domsic RT, Medsger T, Pinckney A, et al. Safety and Efficacy of B-Cell Depletion with Rituximab for the Treatment of Systemic Sclerosis-Associated Pulmonary Arterial Hypertension: A Multicenter, Double-Blind, Randomized, Placebo-Controlled Trial. Am J Respir Crit Care Med (2021) 204(2):209–21. Epub 2021/03/03. doi: 10.1164/rccm.202009-3481OC.
Weiss E, Mamelak AJ, La Morgia S, Wang B, Feliciani C, Tulli A, et al. The Role of Interleukin 10 in the Pathogenesis and Potential Treatment of Skin Diseases. J Am Acad Dermatol (2004) 50(5):657–75; quiz 76 – 8. Epub 2004/04/21. doi: 10.1016/j.jaad.2003.11.075.

Table 1. the top 20 genes in 5 algorithms respectively of Cytoscape

Betweenness	BottleNeck	DMNC	EcCentricity	Radiality
FGF2	FGF2	UHRF1	ACTR2	BMPR1A
ACTR2	ACTR2	CLSPN	FGF2	FGF2
BMPR1A	BMPR1A	FANCI	MYO5A	ACTR2
PTPN11	PTPN11	E2F7	BMPR1A	PTPN11
ASPM	ASPM	CDT1	ASPM	ASPM
MYO5A	MYO5A	ASPM	IQGAP3	IQGAP3
TNNT2	TNNT2	RCC1	GRPEL1	MYO5A
CDT1	AGTR1	FRZB	SOST	SMURF2
RCC1	GPR160	DKK1	SMURF2	TNNT2
STX6	STX6	SOST	UHRF1	CHL1
CHL1	EXOC5	DKK2	CENPW	SOST
EXOC5	SOST	CASQ2	RCC1	DKK1
AGTR1	RCC1	MYO5A	E2F7	GRPEL1
SOST	CDT1	IQGAP3	CLSPN	AGTR1
KIF1B	LNPEP	ACTR2	FANCI	EXOC5
LNPEP	DKK1	RALA	CDT1	FYB
FYB	CHL1	RBM20	PTPN11	PRKCQ
IQGAP3	FYB	FYB	GUF1	CDT1
DKK1	KIF1B	PTPN11	STX6	PML
CASQ2	CASQ2	MBNL2	EXOC5	DKK2

Table 2 Hub genes full name and function

No.		Gene symbol	Full name	Function
1	MYO5A		myosin VA	Processive actin-based motor that can move in large steps approximating the 36-nm pseudo-repeat of the actin filament. Involved in melanosome transport. Also mediates the transport of vesicles to the plasma membrane. It may also be required for some polarization processes involved in dendrite formation
2	CDT1		chromatin licensing and DNA replication factor 1	Required for both DNA replication and mitosis; Required also for mitosis by promoting stable kinetochore-microtubule attachments
3	ASPM		Assembly factor for spindle microtubules	Involved in mitotic spindle regulation and coordination of mitotic processes.
4	ACTR2		Actin-related protein 2	ATP-binding component of the Arp2/3 complex, a multiprotein complex that mediates actin polymerization upon stimulation by a nucleation-promoting factor
5	PTPN11		protein tyrosine phosphatase non-receptor type 11	Acts downstream of various receptor and cytoplasmic protein tyrosine kinases to participate in the signal transduction from the cell surface to the nucleus
6	SOST		sclerostin	Negative regulator of bone growth that acts through inhibition of Wnt signaling and bone formation

No competing interests reported.

Analysis of communal Pathogenesis and Immune Infiltration Characteristics Between Psoriasis and Pulmonary Arterial Hypertension

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

1 Materials And Methods

1.1 Data source

1.2 Identification of DEGs

1.3 Enrichment analyses of communal DEGs

1.4 Protein-protein interaction network construction and module analysis

1.1 Hub gene selection, analyses, and validation

1.2 Construction of the TF–mRNA–miRNA regulatory network

1.3 Evaluation of immune cell infiltration

1.4 Correlation analysis of hub genes and infiltrating immune cells

2 Result

2.1 Identification of DEGs

2.2 Enrichment analyses of communal DEGs in psoriasis and IPAH

2.3 Protein-protein interaction network construction and module analysis

2.4 Hub gene selection, analysis, and validation

2.5 Construction of the TF-mRNA-miRNA regulatory network

2.6 Evaluation of immune cell infiltration

2.7 Correlation analysis of hub genes and infiltrating immune cells

3 Discussion

4 Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1