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. 2026 Jan 13;37(1):23. doi: 10.1007/s00335-026-10194-8

Exploration of shared gene signatures and molecular mechanisms between psoriasis and COVID-19: evidence from transcriptome data

Rong-Jing Dong 1,2,#, Lu-Hui Yang 1,#, Yu-Ye Li 1,✉, You-Wang Lu 3,✉
PMCID: PMC12799721  PMID: 41530290

Abstract

Emerging evidence suggests that the onset or worsening of psoriasis can occur following COVID-19 infection or vaccination. Additionally, individuals with psoriasis may be more susceptible to COVID-19. However, the underlying mechanisms driving this phenomenon remain unclear. This study aims to explore the potential shared mechanisms and the complex interplay between psoriasis and COVID-19. Gene expression profiles for COVID-19 (GSE162183) and psoriasis (GSE54456) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were identified, followed by functional annotation, protein–protein interaction (PPI) network construction, and hub gene identification. Validation of hub genes was performed using independent datasets (GSE152075 and GSE157103 for COVID-19; GSE121212 and GSE78097 for psoriasis). Receiver operating characteristic (ROC) curves were used to assess the predictive value of the hub genes. Gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted, and expression patterns of the hub genes were further explored using a single-cell RNA sequencing dataset. A total of 66 common DEGs (all upregulated) were identified. The influenza A and NOD-like receptor signaling pathways were enriched in both COVID-19 and psoriasis. OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 were identified as hub genes after validation, with all are under the curve (AUC) > 0.7 for COVID-19 and AUC > 0.9 for psoriasis. Upregulation of these hub genes was associated with increased infiltration of neutrophils and Th17 cells. Single-cell analysis showed that the hub genes were primarily expressed in epithelial cells in COVID-19 and keratinocytes in psoriasis. This study reveals shared pathogenic mechanisms between psoriasis and COVID-19 and provides insights into how COVID-19 may exacerbate psoriasis. The identified common pathways, hub genes, and associated cell types offer valuable guidance for future research and potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00335-026-10194-8.

Keywords: Psoriasis, COVID-19, Type I interferon, Immune infiltration, Single-cell analysis, Bioinformatics

Introduction

Psoriasis is a chronic, multifactorial, immune-mediated skin disease that affects approximately 0.5–11.4% of the global population (Griffiths et al. 2021). Its complex pathogenesis, particularly in genetically predisposed individuals, involves the dysregulation of various immune cell types, including type 1 T helper cell (Th1), Th17, and Th22 lymphocytes, neutrophils, dendritic cells, and others. These immune cells, along with pro-inflammatory cytokines such as those in the IL-17/IL-23 axis and TNF-α, contribute to abnormal keratinocyte proliferation, ultimately resulting in scaly erythematous plaques, papules, and/or plaques on the skin (Griffiths and Barker 2007). A growing number of research has also demonstrated the critical role of infectious factors in the onset and progression of psoriasis (Zhou and Yao 2022). Studies have shown that certain vaccines, such as the diphtheria vaccine, as well as immune adjuvants, can trigger or exacerbate psoriasis (Macias and Cunha 2013; Gunes et al. 2015) Moreover, increasing evidence suggests that both exogenous infections, such as streptococcal or viral infections, and alterations in the host skin or gut microbiome may initiate or worsen the disease (Takeshita et al. 2018; Dupire et al. 2019; Yan et al. 2017). Therefore, the contribution of infectious factors to the occurrence, recurrence, and exacerbation of psoriasis should not be underestimated.

Amid the ongoing outbreak of coronavirus disease (COVID-19), caused by Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection, the clinical course of many chronic inflammatory skin disorders has been notably affected, presenting new challenges for concurrent management (Criado et al. 2022). An increasing number of reports have documented new-onset psoriasis, as well as flare-ups or exacerbation of existing psoriasis, following either COVID-19 infection or COVID-19 vaccination (Krajewski et al. 2021; Sotiriou et al. 2021; Nasiri et al. 2020; Miladi et al. 2021; Wu et al. 2022). Additionally, patients with pre-existing psoriasis appear to be more susceptible to SARS-CoV-2 infection (Kutlu and Metin 2020). This suggests that COVID-19 and psoriasis may share overlapping pathogenic mechanisms.

The heightened inflammatory milieu in COVID-19, which characterized by elevated levels of interleukins (IL-1 and IL-6), tumor necrosis factor (TNF), and interferon-inducible protein 10 (IP-10), may contribute to the worsening of psoriasis (Huang et al. 2020; Ozaras et al. 2020). Moreover, the Th17/IL-17 axis, a central driver of psoriasis pathogenesis, has also been implicated in severe COVID-19 cases (Wu and Yang 2020). Increasing attention has been drawn to the role of type I interferonopathies, which may serve as a shared pathogenic link between inflammatory disorders and COVID-19 (Picard and Belot 2017; Zhang et al. 2021). In our previous research, we observed that upregulation of cutaneous interferon-stimulated genes (ISGs) in keratinocytes plays a significant role in psoriasis pathology (Lu et al. 2022), further supporting a potential convergence in the molecular pathways of psoriasis and COVID-19.

To investigate the potential common mechanisms underlying psoriasis and COVID-19, as well as the reasons why COVID-19 may trigger or exacerbate psoriasis, we analyzed two publicly available datasets (GSE54456 and GSE162183). First, we identified differentially expressed genes (DEGs) in psoriasis, COVID-19, and healthy controls, respectively. We then screened for intersecting DEGs shared between the two diseases and performed KEGG pathway enrichment analysis and co-expression network construction. Additionally, protein-protein interaction (PPI) networks were established to identify key hub genes, resulting in the identification of eight hub genes. The identification and functional analysis of these hub genes aim to elucidate the molecular mechanisms by which COVID-19 may trigger or worsen psoriasis, ultimately providing a foundation for the treatment of patients with coexisting COVID-19 and psoriasis.

Materials and methods

Data sources and preprocessing

Transcriptome datasets related to psoriasis were retrieved from the Gene Expression Omnibus (GEO) database. These included GSE54456 dataset (95 psoriatic lesion samples and 84 normal skin samples) (Li et al. 2014), GSE121212 dataset (28 psoriatic lesion samples and 38 normal skin samples) (Tsoi et al. 2019), GSE78097 dataset (27 psoriatic lesion samples and 6 normal skin samples) (Kim et al. 2016), and GSE162183 dataset (lesional skin from 3 psoriasis patients and matched skin from 3 healthy donors) (Gao et al. 2021).

COVID-19-related transcriptomic data were also obtained, including GSE163151 (D’Erme et al. 2015) (138 COVID-19 nasopharyngeal (NP) swabs and 93 negative control samples), GSE152075 dataset (Lieberman et al. 2020) (430 COVID-19 NP swabs and 54 negative control samples), GSE157103 dataset(Overmyer et al. 2021) (100 COVID-19 plasma and leukocyte samples and 26 negative control samples) (see Table 1). Additionally, single-cell RNA sequencing (scRNA-seq) data of COVID-19 samples were obtained from the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP1289/).

Table 1.

The information of the GEO datasets in this study

GEO accession Diagnose Treatment Case Normal Platforms Severity Pubmed ID
GSE54456 psoriasis vulgaris None 58 58 GPL9052 NA* 24441097
GSE121212 psoriasis vulgaris None 85 85 GPL16791 moderate to severe 30641038
GSE7809 psoriasis vulgaris None 14 14 GPL570 Mild and severe 17895395
GSE162183 psoriasis vulgaris None 3 3 GPL24676 NA 33958582
GSE163151 COVID-19 None 138 93 GPL24676 Mild and severe 33536218
GSE152075 COVID-19 None 430 54 GPL18573 Mild and severe 32898168
GSE157103 COVID-19 None 100 26 GPL24676 Mild and severe 33096026
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Differentially expressed genes (DEGs) analysis

Differentially expressed genes (DEGs) between psoriatic and normal skin tissues were identified using the “DESeq2” R package. Genes with an absolute log2 fold change (|log2FC|) ≥ 1 and an adjusted p-value < 0.05 were considered statistically significant. The common DEGs across datasets were determined using the “ggvenn” R package.

GO and KEGG pathway enrichment analysis

The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the “clusterProfiler” package (version 4.4.1) to explore the biological functions of the identified DEGs. GO terms and KEGG pathways with a false discovery rate (FDR) < 0.05 were considered significantly enriched.

Construction of protein-protein interaction network and hub gene screening

The identified DEGs were imported into the STRING database (version 11.5; https://string-db.org/) to construct a protein–protein interaction (PPI) network, with a minimum required interaction score set to > 0.9. The CytoHubba plugin (http://apps.cytoscape.org/cytohubba) in Cytoscape was then used to analyze the topological features of the nodes within the PPI network. Seven algorithms, including Closeness, MCC, Degree, MNC, Radiality, Stress, and EPC, were applied to identify hub genes. The overlapping hub genes identified by these algorithms were visualized using the “VennDiagram” R package.

Validation of hub gene expression

The expression levels of the identified hub genes were validated using independent datasets: GSE121212 and GSE163151 for psoriasis, and GSE152075 and GSE157103 for COVID-19. Comparisons between groups were performed using the “ggpubr” R package. A p-value < 0.05 was considered statistically significant.

Receiver operating characteristic (ROC) analysis

Receiver operating characteristic (ROC) curves were generated using the “plotROC” package (Sachs 2017) to evaluate the predictive performance of the identified hub genes.

Immune infiltration analysis

Single-sample gene set enrichment analysis (ssGSEA), a rank-based method, was used to calculate enrichment scores representing the relative abundance of specific immune cell types in each sample. ssGSEA scores were used to quantify immune cell infiltration in tissues from both COVID-19 and psoriasis datasets. Spearman correlation analysis was performed to assess the relationships between the expression levels of the eight hub genes and the infiltration levels of 28 immune cell types, providing insight into the association between hub genes and immune responses.

Single cell RNA-seq (scRNA-seq) data acquiring and processing

The COVID-19 scRNA-seq dataset was obtained from the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP1289/), while the psoriasis scRNA-seq dataset (GSE162183) was retrieved from the Gene Expression Omnibus (GEO) database. Data preprocessing and dimensionality reduction were performed using the “Seurat” package (version 4.1.1).

During initial quality control (QC), Seurat objects were constructed for each dataset. Cells expressing fewer than 200 genes and genes detected in fewer than three cells were excluded from subsequent analyses. The remaining gene expression data were normalized, and 3,000 highly variable genes (HVGs) were identified per sample using the “vst” method. All genes were then scaled, followed by principal component analysis (PCA).

Unsupervised cell clustering was conducted with a resolution parameter set to 0.8, and dimensionality reduction was visualized using uniform manifold approximation and projection (UMAP) based on the top 30 principal components. Cell-type annotation was performed using the SingleR package (version 1.8.1) and further refined through manual curation based on canonical marker genes. To evaluate hub gene activity at the single-cell level, pathway activity scores were calculated for each cell using the “AUCell” package (version 1.16.0) (Aibar et al. 2017).

Statistical analysis

All statistical analyses were conducted using R software (version 4.1.2). Differences between groups were assessed using the Mann-Whitney-Wilcoxon test. Correlations between immune cell infiltration and hub gene expression were evaluated using Spearman’s correlation analysis. An adjusted p-value < 0.05 was considered statistically significant.

Results

Identification of common differentially expressed genes (DEGs) between COVID-19 and psoriasis

Differential gene expression analysis was conducted on COVID-19 and psoriasis datasets using the “DESeq2” package. Applying standard threshold criteria (|log₂ fold change (FC)| ≥ 1 and adjusted p-value < 0.05), a total of 277 DEGs were identified in the COVID-19 dataset, including 266 upregulated and 11 downregulated genes (Fig. 1A, B; Table S1). In the psoriasis dataset, 1,985 DEGs were detected, comprising 1,036 upregulated and 949 downregulated genes compared to healthy controls (Fig. 1C, D; Table S2). By intersecting the DEGs from both datasets using Venn diagram analysis, 73 common DEGs were identified (Fig. 1E). After removing genes with opposite expression trends, 66 common DEGs with consistent upregulation in both datasets (GSE163151 and GSE54456) were retained.

Fig. 1.

Fig. 1

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Identification of common differentially expressed genes (DEGs) between COVID-19 and psoriasis. A Volcano plot of DEGs in COVID-19 samples, with upregulated genes shown in orange and downregulated genes in medium purple. B Heatmap of DEG expression in COVID-19 nasopharyngeal swabs; high expression is indicated in ruby red and low expression in green. C Volcano plot of DEGs in psoriasis skin samples, with upregulated genes in orange and downregulated genes in medium purple. D Heatmap of DEG expression in psoriasis lesion samples, with high expression in ruby red and low expression in green. E A total of 73 common DEGs were identified by intersecting the DEGs from both COVID-19 and psoriasis datasets

Pathway enrichment analysis of common DEGs

To further investigate the biological function of the common DEGs, pathway enrichment analysis was performed. KEGG enrichment analysis revealed that the DEGs were primarily involved in pathways such as Influenza A, NOD-like receptor signaling, and Coronavirus disease-COVID-19 (Fig. 2A; Table S3). Gene Ontology (GO) enrichment analysis indicated that the common DEGs were mainly enriched in biological processes and components including defense response to other organisms, response to virus, inflammasome complex, and double-stranded RNA binding (Fig. 2B; Table S4).

Fig. 2.

Fig. 2

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Pathway enrichment analysis of common DEGs. A Chord diagram showing KEGG pathway enrichment of the common DEGs. B Bubble chart displaying the top 10 enriched Gene Ontology (GO) terms for the common DEGs

Protein-protein interaction (PPI) and hub genes analysis

To elucidate the potential interactions among proteins encoded by the common DEGs and to identify hub genes, we constructed a protein–protein interaction (PPI) network consisting of 37 nodes and 171 edges (Fig. 3A). The network was visualized using Cytoscape software, where genes with higher connectivity are represented in deeper red. A key gene module containing 31 shared DEGs was identified using the MCODE plug-in (Fig. 3B).

Fig. 3.

Fig. 3

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Protein-protein interaction (PPI) and hub genes analysis. A PPI network constructed from the common DEGs. B A significant gene module identified from the network. C Venn diagram showing eight overlapping hub genes identified by seven different algorithms. D Hub genes and their co-expressed genes visualized using GeneMANIA

To further screen hub genes, we applied seven algorithms provided by the cytoHubba plug-in to identify the top 15 hub genes. By intersecting the results using a Venn diagram, 8 common hub genes were ultimately identified: OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 (Fig. 3C).

Using the GeneMANIA database, we constructed a comprehensive gene interaction network to investigate the biological functions of these hub genes. The interaction types were predominantly co-expression (78.59%), followed by physical interactions (13.30%), predicted interactions (7.78%), shared protein domains (0.22%), and co-localization (0.12%) (Fig. 3D). Additionally, 20 genes associated with the 8 hub genes were identified. Functional enrichment revealed that these genes were mainly involved in responses to type I interferon, cellular responses to type I interferon, response to virus, negative regulation of viral processes, regulation of viral genome replication, viral genome replication, and regulation of the viral life cycle.

Validation of hub genes in external datasets

To validate the expression of the identified hub genes, we analyzed independent GEO datasets: GSE152075 and GSE156063 for COVID-19, and GSE121212 and GSE78097 for psoriasis. The results consistently showed that all eight hub genes (OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15) were significantly upregulated in both COVID-19 and psoriasis samples compared to healthy controls (Fig. 4).

Fig. 4.

Fig. 4

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Validation of hub genes in external datasets. A, B Boxplots showing the expression of hub genes between COVID-19 and healthy samples in GSE152075 and GSE156063 datasets, respectively. C, D Boxplots showing the expression of hub genes between psoriasis and healthy samples in GSE121212 and GSE78097 datasets, respectively. *p < 0.05, **p < 0.01, ***p < 0.001

ROC curves analysis of hub genes in COVID-19 and psoriasis

The diagnostic performance of the eight hub genes was evaluated using the “pROC” package. ROC curves were generated for four datasets based on the expression levels of the hub genes to assess their diagnostic accuracy (Fig. 5). In the COVID-19 datasets (GSE163151, GSE152075, and GSE156063), all eight genes exhibited area under the curve (AUC) values greater than 0.7, indicating good diagnostic capability in distinguishing COVID-19 patients from healthy controls (Fig. 5A–C). In the psoriasis datasets (GSE54456, GSE121212, and GSE78097), all eight hub genes showed AUC values exceeding 0.9, suggesting excellent diagnostic performance in distinguishing psoriasis patients from healthy individuals (Fig. 5D–F). These findings suggest that OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 exhibit consistent and robust discriminative potential in both COVID-19 and psoriasis. However, their clinical utility as diagnostic biomarkers requires further validation in independent cohorts and experimental studies.

Fig. 5.

Fig. 5

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ROC curves analysis of hub genes in COVID-19 and psoriasis. Receiver operating characteristic (ROC) curves were plotted to assess the diagnostic performance of hub genes for A–C COVID-19 (GSE163151, GSE152075 and GSE156063), and D–F psoriasis (GSE54456, GSE121212 and GSE78097)

Association between hub genes and immune infiltration

To explore the relationship between hub gene expression and immune cell infiltration, a combined score was calculated using ssGSEA based on the expression levels of the eight hub genes. In the GSE163151 dataset, infiltration levels of 21 out of 28 immune cell types were significantly elevated in COVID-19 samples compared to controls (Fig. 6A). Spearman correlation analysis revealed that the expression levels of the eight hub genes were positively correlated with multiple immune cell types, including regulatory T cells (Tregs), follicular helper T cells (Tfhs), activated dendritic cells (DCs), natural killer T cells (NKTs), activated B cells, effector memory CD8 + T cells, monocytes, neutrophils, mast cells, macrophages, myeloid-derived suppressor cells (MDSCs), type 1 helper T cells (Th1), immature B cells, central memory CD8 + T cells, and type 17 helper T cells (Th17) (Fig. 6B).

Fig. 6.

Fig. 6

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Association between hub genes and immune infiltration. A Differences in immune cell infiltration levels between COVID-19 and healthy controls. B Correlation between hub gene expression and immune infiltration in GSE163151 dataset. C Differences in immune cell infiltration levels between psoriasis patients and healthy controls. D Correlation between hub gene expression and immune infiltration in GSE54456 dataset. E Detailed correlation between neutrophil infiltration and combined score in COVID-19. F Detailed correlation between Th17 cell infiltration and combined score in COVID-19. G Detailed correlation between neutrophil infiltration and combined score in psoriasis. H Detailed correlation between Th17 cell infiltration and combined score in psoriasis

In the psoriasis dataset (GSE54456), infiltration levels of most immune cell types were significantly increased, except for mast cells and memory B cells (Fig. 6C). Notably, neutrophils and Th17 showed a strong positive correlation with both the expression levels of the hub genes and the combined score (Fig. 6D). Specifically, the combined score was significantly correlated with neutrophil infiltration (r = 0.34, p < 0.05; Fig. 6E) and type Th17 (r = 0.38, p < 0.05; Fig. 6F). Consistent correlation patterns were observed in the psoriasis dataset as well (Fig. 6G and H).

Analysis of the hub genes expression at single-cell level in COVID-19

To investigate the expression of hub genes at single-cell resolution in COVID-19, we collected nasopharyngeal (NP) samples from 37 COVID-19 patients and 15 healthy donors, followed by single-cell RNA sequencing (scRNA-seq). After standard data processing and quality control, transcriptomic profiles from 20,011 cells were obtained. Principal cell clusters were identified using an unsupervised graph-based clustering approach, and cell types were annotated with the “SingleR” package. The cellular composition is presented in Fig. 7A.

Fig. 7.

Fig. 7

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Analysis of the hub genes expression at single cell level in COVID-19. A UMAP showing the major cell types in COVID-19 (n = 37) and heathy controls (n = 15) at single-cell transcriptomes. B Density plot of AUC values. C Boxplot comparing AUC values between COVID-19 and healthy controls

The expression patterns of hub genes across different cell populations in COVID-19 and healthy NP swabs are shown in Figure S1. Single-cell rank-based gene set enrichment analysis revealed that the enrichment scores were particularly elevated in epithelial cells, especially ciliated cells and goblet cells (Fig. 7B). Furthermore, the hub genes generally showed higher expression in COVID-19 samples compared with healthy controls across several major cell types (Fig. 7C), suggesting a potential association with the immune response rather than implying causal effects.

Analysis of the hub genes expression at single-cell level in psoriasis

To explore the expression of hub genes in psoriatic lesions at single-cell resolution, we collected skin samples from three psoriasis patients and three healthy donors from comparable anatomical regions and performed scRNA-seq. Following standard data processing and quality control procedures, we obtained transcriptomic profiles from 16,837 cells. Principal cell clusters were identified using an unsupervised graph-based clustering approach, and cell types were annotated using the “SingleR” package. The cellular composition is shown in Fig. 8A. Single-cell rank-based gene set enrichment analysis revealed higher enrichment scores in keratinocytes, particularly differentiated keratinocytes and basal keratinocytes (Fig. 8B). The expression patterns of hub genes across different cell populations in psoriatic lesions and healthy skin are displayed in Figure S2. Notably, ISG15, MX1, IFI44L, and IFI27 showed elevated expression in keratinocytes from psoriatic lesions, suggesting their potential involvement in disease-associated cellular responses without implying direct causality.

Fig. 8.

Fig. 8

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Analysis of the hub genes expression at single cell level in psoriasis. A UMAP showing major cell types and clusters in psoriasis patients (n = 3) and healthy controls (n = 3). B Density plot of AUC values. C Boxplot comparing AUC values between psoriasis and healthy controls

Discussion

As of November 11, 2022, more than 639 million COVID-19 cases had been reported worldwide, representing approximately 8% of the global population (Worldometer 2022). Emerging evidence suggests that acute COVID-19 infection and its associated treatments can influence the course of various cutaneous conditions, including atopic dermatitis, psoriasis, vitiligo, and chronic urticaria (Criado et al. 2022). For example, one study reported that 405 out of 926 participants (43.7%) experienced moderate to severe psoriasis during the COVID-19 pandemic (Kuang et al. 2020). Furthermore, some individuals with psoriasis may be more susceptible to COVID-19 infection (Kutlu and Metin 2020).

Both psoriasis and COVID-19 are inflammatory conditions driven by complex interactions between innate and adaptive immune cells, which can affect the clinical course of psoriasis, including its initiation, relapse, and exacerbation phases. This intricate interplay also poses new challenges for the clinical management of COVID-19, particularly regarding the use of systemic immunosuppressive therapies. Therefore, exploring the shared mechanisms between these two diseases is essential for better understanding their interplay and informing clinical strategies.

Our analysis of hub gene expression at the single-cell level revealed that genes such as OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 exhibited higher expression in epithelial cells of the nasopharynx and in keratinocytes of psoriatic lesions, particularly in ciliated and goblet cells or differentiated and basal keratinocytes, respectively. Single-sample gene set enrichment and immune infiltration analyses further suggested positive correlations between hub gene expression and multiple immune cell populations, including neutrophils and Th17 cells, which are known to contribute to inflammatory responses in both conditions.

The type I interferon (IFN-I) pathway is a key component of innate immunity, rapidly activated upon viral infection and inducing hundreds of interferon-stimulated genes (ISGs) that exert antiviral effects across multiple stages of the viral life cycle, including entry, replication, assembly, and release (Chen et al. 2017). Numerous studies have demonstrated that IFN-I responses are essential for defense against respiratory syncytial virus (Antunes et al. 2019), influenza A virus (Hao et al. 2020), and systemic control of reovirus dissemination (Phillips et al. 2021). Dysregulation of the IFN-I pathway and ISGs have been implicated in autoimmune and inflammatory diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjogren’s syndrome, and systemic sclerosis (Nehar-Belaid et al. 2020; Hall and Rosen 2010; Kalliolias and Ivashkiv 2010). Key components of this IFN/ISG system include interferon regulatory factors (IRF1, IRF3, IRF7), MX proteins (MX1, MX2), the IFIT family (IFIT1, IFIT2, IFIT3, IFIT5), and 2’−5’-oligoadenylate synthetases (OAS1, OAS2, OAS3, OASL) (Schoggins 2019). Upregulation of hallmark type I IFN/ISGs is commonly observed in COVID-19 patients (Zhou et al. 2020; Martin-Sancho et al. 2021), with expression levels of genes such as RSAD2, IFIT1, MX1, OAS1, OAS2, IFI27, IFI35, and IFI6 varying across molecular subtypes (Hu et al. 2021) and correlating with disease severity (Zhang et al. 2021; Dong et al. 2022).

ISGs possess an adjunctive antiviral effect by amplifying inflammatory signals and are therefore referred to as “pro-inflammatory ISGs”. However, when the type I IFN/ISG-mediated protective immune response becomes dysregulated, it can lead to autoimmune tissue damage (Hall and Rosen 2010). This has led to the concept of “type I interferonopathies”, where excessive activation of antiviral sensors triggers pathological responses. For instance, overexpression of IFNs and ISGs has been linked to the pathogenesis and severity of autoimmune diseases such as systemic lupus erythematosus (SLE) (Wahadat et al. 2018), and systemic sclerosis (Skaug and Assassi 2020). Several studies have also reported significant upregulation of ISGs (HERC6, ISG15, MX1, RSAD2, OAS2, OASL, and OAS3) in psoriatic lesions (Zhang et al. 2019; Raposo et al. 2015; Melero et al. 2018; Wolk et al. 2013). Nevertheless, the precise mechanisms by which type I IFN/ISGs drive psoriasis remain incompletely understood.

Activation of the type I IFN signaling pathway sustains a persistent inflammatory response to viral infection. It can also induce the expression of chemokines, activate B and T cells, and promote autoantibody production, leading to loss of self-tolerance and contributing to the development or exacerbation of autoimmune diseases such as SLE (Ardoin and Pisetsky 2008). Moreover, aberrant recognition of host DNA and RNA can trigger antiviral sensors, resulting in upregulation of type I interferon signaling (Crow and Stetson 2022). Consequently, it is speculated that autoimmune diseases and COVID-19 infection may mutually exacerbate each other (Liu et al. 2021; Galeotti and Bayry 2020). In our study, we identified ISG genes that are highly expressed in both psoriasis and COVID-19 patients, and we further validated these gene sets using single-cell datasets, confirming their elevated expression in both innate and adaptive immune cells. This finding may partly explain the molecular mechanisms through which COVID-19 triggers and/or worsens psoriasis. Targeting the IFN signaling pathway and the induced ISG expression may represent a promising therapeutic strategy for patients with concurrent COVID-19 and psoriasis.

Additionally, uncontrolled activation of Th17 cells and the IL-17-related pathways have been observed in severe COVID-19 patients and is also central to psoriasis pathogenesis (Wu and Yang 2020). In our study, immune infiltration and single-cell transcriptome analyses revealed that these ISG genes were predominantly highly expressed in neutrophils, activated dendritic cells, and Th17 cells, suggesting that these immune populations may mediate overlapping inflammatory responses in both diseases. While concerns have been raised about immunosuppressive therapies (such as anti-TNF-α or anti-IL-17 drugs) increasing COVID-19 risk in psoriasis patients (Stallmach et al. 2020; Zumla et al. 2020), evidence indicates that biologic treatments may alleviate hyperinflammation in some case (Amerio et al. 2020; Ebrahimi et al. 2022), supporting the notion of shared underlying pathophysiology.

Single-cell analyses showed that hub genes were predominantly expressed in epithelial cells in COVID-19 patients (including ciliated and goblet cells), with ISG15, OAS2, and MX1 upregulated in SARS-CoV-2–infected human corneal epithelial cells (Singh et al. 2022). In psoriasis, hub genes were mainly expressed in keratinocytes, which can secrete antiviral proteins such as MX1, ISG15, and OAS2 upon IFN stimulation (Wolk et al. 2013).

Overall, this study is the first to integrate bioinformatics, enrichment analysis, and immune infiltration profiling to describe the potential shared pathogenesis between COVID-19 and psoriasis. Both diseases exhibit upregulation of ISGs in Th17 cells and neutrophils, which may underlie the onset, relapse, or exacerbation of psoriasis following COVID-19 infection or vaccination. These findings provide a theoretical framework for developing therapeutic strategies targeting the comorbidity of these diseases. However, this study has several limitations. It relies on publicly available transcriptomic and single-cell datasets, which may introduce sampling and technical biases. The analyses are largely associative, lacking experimental validation to establish causality, and the cross-sectional design prevents assessment of longitudinal changes. Future studies with prospective cohorts and functional experiments are warranted to validate and extend these findings.

Conclusion

In summary, our findings highlight a potential shared pathogenesis between COVID-19 and psoriasis, providing insights into the underlying mechanisms by which COVID-19 influences immune-inflammatory skin disorders. Moreover, the IFN/ISGs system may represent a promising therapeutic target for treating psoriasis, COVID-19, or their coexistence.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (858.4KB, docx)
Supplementary Material 2 (15.8KB, xlsx)
Supplementary Material 3 (118.1KB, xlsx)
Supplementary Material 4 (1.7MB, xlsx)
Supplementary Material 5 (3.1MB, xlsx)

Author contributions

Conceptualization, Data curation, YWL; Formal analysis, YWL; Funding acquisition, YYL, RJD, and YWL; Investigation, Methodology, LHY; Supervision, YYL; Visualization, Writing – original draft, RJD; Writing – review & editing, all authors.

Funding

This work was partially supported by the National Natural Science Foundation of China (grant numbers 82203934, 82272356, 82260624), the Talent Introduction Project of Hubei Polytechnic University (grant numbers 21xjz33R, 21xjz34R), and the Key research and development program of Yunnan Province (202403AC100011).

Data availability

Data in supplementary information files.

Declarations

Conflict of interest

The authors declare no competing interests.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rong-Jing Dong and Lu-Hui Yang contributed equally to this work.

Contributor Information

Yu-Ye Li, Email: yyeli2000@126.com.

You-Wang Lu, Email: youwanglu@163.com.

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Associated Data

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Supplementary Materials

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Supplementary Material 2 (15.8KB, xlsx)
Supplementary Material 3 (118.1KB, xlsx)
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Supplementary Material 5 (3.1MB, xlsx)

Data Availability Statement

Data in supplementary information files.

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