Mendelian randomization reveals DNA methylation–related pyroptosis genes associated with psoriasis risk

Abstract

Background

Research has indicated a connection between pyroptosis and psoriasis, yet the specific genes involved remain largely unidentified. This study employed Mendelian randomization (MR) to evaluate the potential causal impact of both pyroptosis-related genes and DNA methylation signatures on psoriasis risk.

Methods

Pyroptosis-related genes were sourced from the GeneCards database. We integrated quantitative trait locus (QTL) data, including expression (eQTLs), DNA methylation (mQTLs), and protein expression (pQTLs). The GCST90014456 database provided genome-wide association study (GWAS) data for psoriasis, using the FinnGen and UKB cohorts for validation. Summary data-based Mendelian randomization (SMR) analysis assessed interactions between these genes and psoriasis, while colocalization analysis identified shared causal genetic variants.

Results

SMR analysis identified 82 methylation sites, 18 gene, and 2 protein were associated with psoriasis risk. Multi-omics integration highlighted ADAR, which increased methylation at cg27530370 was associated with decreased ADAR expression (OR = 0.505, 95% CI [0.421–0.607]). Additionally, DNMT3B, PROM2, and KIF11 were the intersection genes of mQTL and eQTL analysis, and were validated by the UKB cohort in eQTL analysis, and NFKB1 was validated by the UKB cohort in pQTL analysis.

Conclusion

This multi-omics analysis reveals that pyroptosis-related genes, particularly ADAR, DNMT3B, PROM2, KIF11, and NFKB1, may be involved in psoriasis development, providing important insights into its molecular mechanisms and potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-026-02063-7.

Background

Psoriasis is a chronic, relapsing skin disease with a global prevalence of approximately 2%, affecting individuals of all ages but more commonly seen in adults [1]. The hallmark of psoriasis is sustained inflammation leading to uncontrolled keratinocyte proliferation and dysfunctional differentiation [2]. In addition, psoriasis is a multifactorial disease caused by the interplay between multiple inherited alleles and environmental risk factors, and involving a variety of processes such as inflammation, antigen presentation, cell signaling, and transcriptional regulation [2, 3]. Indeed, it has a particularly strong genetic component among complex diseases, with heritability estimated to exceed 60% [4]. Large-scale genome-wide association studies (GWAS) have been pivotal in unraveling this genetic architecture, identifying over 80 susceptibility loci to date. These loci implicate genes in several core pathways: HLA-C within the major histocompatibility complex; IL23R and IL12B in IL-23 signaling; TNIP1 and TNFAIP3 in NF-κB signaling; and LCE3B/LCE3C in skin barrier integrity [5, 6]. These discoveries have collectively underscored the critical roles of innate and adaptive immune dysregulation and epidermal dysfunction in disease pathogenesis. Therefore, genetic research may have delivered critical insights into the biology of psoriasis and will be crucial for developing more effective and personalized therapies for patients suffering from psoriasis.

Pyroptosis is a form of programmed cell death characterized by cellular swelling, pore formation in the plasma membrane, and the release of pro-inflammatory contents [7]. The process is primarily mediated by the activation of gasdermin proteins, particularly Gasdermin D (GSDMD) [8], following the cleavage by inflammatory caspases, such as Caspase-1 (CASP1) [9]. Recent studies suggested that pyroptosis is deeply involved in the pathogenesis of psoriasis [10]. For instance, elevated levels of N-terminal GSDMD (N-GSDMD), a product of GSDMD cleavage, have been detected in the epidermis of psoriasis patients and in mouse models of psoriasis-like dermatitis [11]. Additionally, the inhibition of the NLRP3/CASP1/GSDMD pathway has been shown to attenuate keratinocyte proliferation, inflammatory responses, and pyroptosis [9]. Clinical studies have demonstrated that patients with psoriasis exhibit elevated levels of GSDMD and CASP1 in affected skin tissues, suggesting a critical role for these proteins in the disease [11, 12]. Animal models of psoriasis have further supported these findings, showing that the inhibition of GSDMD-mediated pyroptosis can alleviate psoriasis-like skin inflammation [13]. Genetic interventions, such as the use of siRNAs to downregulate GSDMD expression, have been shown to reduce keratinocyte proliferation and promote cell apoptosis in vitro, providing insights into potential therapeutic targets [14]. Collectively, these studies underscore the importance of pyroptosis genes such as GSDMD and CASP1 in the pathophysiology of psoriasis and highlight the potential of targeting these genes for the development of novel treatments. Despite these findings, the exact mechanisms linking pyroptosis to psoriasis remain incompletely understood, and the clinical implications of targeting pyroptosis-related genes in psoriasis management are yet to be fully explored.

To further investigate the potential interplay between cell pyroptosis-related genes and psoriasis, Mendelian Randomization (MR) has emerged as a robust epidemiological method that leverages genetic variation to infer causal links between risk factors and disease outcomes [15]. While traditional MR analyses primarily rely on single omics datasets, such as gene expression data, these approaches often fall short in fully elucidating the multifaceted etiology of complex diseases. To address this limitation, multi-omics MR methodologies have been developed, which integrate genomic, transcriptomic, epigenomic, and proteomic data to enhance the precision and reliability of causal inferences [16, 17]. In this study, we employ Summary-data-based Mendelian Randomization (SMR) analysis to combine multi-omics data, including DNA methylation, gene expression, and protein expression, to identify and validate genetic variants associated with psoriasis. This integrated approach not only increases the statistical power of causal inference but also offers a more comprehensive view by examining the associations between cell pyroptosis-related genes and psoriasis across various biological levels.

This study aims to investigate the role of cell pyroptosis-related pathogenic genes in the development of psoriasis using multi-omics MR methods. By integrating data at the levels of gene expression, DNA methylation and proteomics, we aim to identify key genes involved in cell pyroptosis that contribute to the pathogenesis of psoriasis. This integrated approach will help construct a multi-layered molecular landscape that captures the changes and regulatory mechanisms during the progression of psoriasis.

Methods

Study design

In this study, genes related to pyroptosis were extracted as instrumental variables at three biological levels: methylation, gene expression, and protein abundance. Separate Mendelian Randomization (MR) analyses were conducted for each biological level to investigate their association with psoriasis. The GCST90014456 dataset was used as the primary discovery dataset, while the FinnGen_L12_PSORIASIS cohort and UKB-b-10,537 cohort were employed for validation. There was no overlap between the exposure and outcome groups. Rather than meta-analyzing heterogeneous datasets, we adopted a discovery-replication framework (GCST90014456 as discovery; FinnGen and UKB as validation). This design enhances robustness by confirming consistent effect directions across independent cohorts. To enhance causal inference, colocalization analysis was also performed. By integrating the results from these three distinct MR analyses, we identified causal candidate genes and further validated them in tissue-specific contexts. The research design and the workflow for analysis methods were summarizes in Fig. 1.

Fig. 1.

The research workflow and analysis methods. Pyroptosis-related protein-coding genes were sourced from GeneCards. Their cis-acting genetic instruments for DNA methylation (mQTL), gene expression (eQTL), and protein abundance (pQTL) were obtained from the respective European-ancestry consortia. These instruments were then used in SMR analyses against psoriasis GWAS data from a discovery cohort (GCST90014456) and two independent replication cohorts (FinnGen and UK Biobank). Significant findings were further subjected to colocalization analysis and integrated across omics layers

Data source

Pyroptosis-related protein-coding genes were systematically retrieved from the GeneCards database (https://www.genecards.org/). The search was performed using the keyword “Pyroptosis”, and the results were filtered by setting the “Category” to “Protein Coding” to ensure a focus on genes with protein-coding potential. This initial screening yielded a list of 593 unique genes for subsequent analysis (Table S1). The blood mQTL summary data were sourced from a meta-analysis of two European cohorts [18]: the Brisbane Systems Genetics Study (n = 614) and the Lothian Birth Cohorts (n = 1366); blood eQTL data were obtained from the eQTLGen Consortium [19], which includes gene expression data from 31,684 individuals; blood pQTL summary data were provided by Ferkingstad et al. [20], covering 35,559 individuals from Iceland. To assess the tissue specificity of the genetic regulatory effects, cis-eQTL data for skin (Skin_Not_Sun_Exposed_Suprapubic and Skin_Sun_Exposed_Lower_leg) were obtained from the GTEx portal (v8). These data encompassed 838 donors and 17,382 samples across 52 tissues and two cell lines.The GWAS statistics for psoriasis came from the GCST90014456 database, including 5459 cases and 324,074 controls. For validation, we used the FinnGen_L12_PSORIASIS dataset, comprising 10,312 cases and 397,564 controls, as well as the UKB-b-10,537 dataset, which includes 5341 cases and 457,619 controls. All summary statistics used for MR analysis were derived from previously published studies (Table 1), all of which received ethical approval.

Table 1.

The GWAS queue information

Trait	GWAS ID	Population(case/control)	SNPs
Psoriasis	GCST90014456	5459/324,074	8,989,407
Psoriasis	FinnGen_R10_L12_PSORIASIS	10,312/397,564	20,193,186
Psoriasis	ukb-b-10,537	534/457,619	9,851,867

The scale and overlap of genetic variants across the datasets used in this study are detailed as follows. The blood mQTL, eQTL, and pQTL summary datasets contained 1,017,836,986; 127,341,798; and 25,133,500 single nucleotide polymorphisms (SNPs), respectively. The number of SNPs in the psoriasis GWAS datasets is provided in Table 1. For the SMR analysis, which requires the harmonization of effect alleles across the QTL and GWAS summary statistics, the number of shared SNPs available for analysis varied by gene probe and outcome cohort. A summary of the overlapping SNPs for the key analyses is provided in Table S2. SMR Analysis (Summary-data-based MR Analysis).

SMR analysis were used to estimate the relationships between pyroptosis-related genes methylation, expression, and protein abundance related to psoriasis. SMR provides greater statistical power compared to traditional MR analyses based on the most relevant cis-QTLs, particularly when both exposure and outcome data originate from two large independent cohorts. In this study, the window centered around the corresponding gene (± 1000 kb) and the P-value threshold of 5.0 × 10⁻⁸ were used to select the most significant cis-QTL. SNPs (including LD reference samples, QTL summary data, and outcome summary data) with allele frequency differences greater than a specified threshold (set at 0.2 in this study) were excluded. The default threshold for allowed allele frequency differences for eQTLs, mQTLs, and pQTLs was set at 0.05. The same SMR and HEIDI outlier test criteria (p_SMR_multi <  0.05, p_SMR <  0.05, and P-HEIDI > 0.05) applied to the blood QTL analyses were used to interrogate the skin eQTL data for causal associations with psoriasis.

Beyond exploring causal associations between QTLs (mQTLs, eQTLs, and pQTLs) and psoriasis, SMR was utilized to integrate mQTL, eQTL, and pQTL data to investigate causal relationships between methylation and gene expression, as well as between gene expression and protein abundance. The analysis examined mQTLs as exposures and eQTLs as outcomes, or eQTLs as exposures and pQTLs as outcomes. This approach aimed to determine whether the expression of target genes is influenced by specific CpG site methylation within their functional regions or if the expression of target genes affects the abundance of their encoded proteins. The focus of this study is on the results derived from these analytical methods.

On the basis of SMR analysis, a multi-SNP SMR analysis method was developed. This method considers all SNPs within the QTL probe window region (default 500 kb) with p-values below the default threshold of 5 × 10^− 8 and LD r2 values below the default threshold of 0.9 with the top associated SNP in the SMR analysis. In this study, we will comprehensively evaluate the significance of results obtained using this method. Subsequently, results without pleiotropy will be screened using the HEIDI test with P > 0.05. Thus, under the condition of P-SMR < 0.05, results that satisfy both P-SMR-multi < 0.05 and P-HEIDI > 0.05 will be used for subsequent eQTL, mQTL, and pQTL colocalization and integrative analyses. SMR and HEIDI tests were implemented using the SMR software tool (SMR v1.3.1). To control for multiple testing, all SMR p-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR) method. Results were considered significant at FDR < 0.05.

Colocalization analysis

R package coloc was used for co-localization analysis to identify shared causal variants between pyroptosis-related cis-QTLs (including eQTLs, mQTLs, and pQTLs) and psoriasis. Specifically, when GWAS signals and QTLs were detected to colocalize, we inferred that loci on the GWAS signal might influence the phenotype by altering gene-related biological processes. In the colocalization analysis, five different posterior probabilities were reported, corresponding to five exclusive hypotheses: (1) H0: No features in the region are genetically associated with the SNP; (2) H1: Only feature 1 is genetically associated with the SNP; (3) H2: Only feature 2 is genetically associated with the SNP; (4) H3: Both features are associated with the SNP but use different causal variables; (5) H4: Both features are associated with the SNP and share a common causal variable.According to published literature, for colocalization analyses involving mQTL-GWAS, eQTL-GWAS, and pQTL-GWAS, the colocalization region window was set to ± 1000 kb around the probe location. To allow for weaker QTL signals to colocalize with the main signal, we considered the colocalization successful if the following conditions were met: [21] (1) For p12 = 5 × 10^{− 5}, PP.H4 > 0.5; (2) For p12 = 1 × 10⁻⁵, PP.H3 < 0.5.

Other statistical analysis details

All statistical analyses were performed using R (v4.3.0), with the “ggplot2” and “ggrepel” packages for Manhattan plots, “forestplot” for forest plots. Code for SMR Locus Plot and SMR Effect Plot adapted from Zhu et al. [22].

Results

Methylation of pyroptosis-related genes in psoriasis

Based on the criteria of p_SMR_multi <  0.05, p_SMR < 0.05, and P-HEIDI >  0.05, we identified 82 methylation sites (corresponding to 48 genes) related to pyroptosis (all SMR analysis results are shown in Table S3). Among these, 4 sites (corresponding to 3 genes) were validated in the FinnGen_L12_PSORIASIS cohort (Refer to Table S4 for all SMR analysis results), while 31 sites (corresponding to 24 genes) were validated in the UKB-b-10,537 cohort (Refer to Table S5 for all SMR analysis results), providing strong evidence for the stability and consistency of these sites across different populations.

In the discovery cohort, 6 sites (corresponding to 6 genes) were also identified with strong colocalization evidence (PPH3 < 0.5 & PPH4 > 0.5) (Fig. 2), with methylation site cg04903600 of GPX4 serving as an example of colocalization results (Fig. S1A). Among these sites, DNMT3B (cg26553763) was validated in the UKB cohort (OR = 1.003, 95% CI [1.001–1.005]) and was positively associated with psoriasis risk; DNMT3B (cg09149842) (OR  =  0.998, 95% CI [0.997–0.999]) and ADAR (cg27530370) (OR  = 0.998, 95% CI [0.996–1]) were negatively associated with psoriasis risk. These validation results further support the role of these genes in psoriasis through the pyroptosis pathway.

Fig. 2.

Forest plot of the SMR analysis results for mQTLs in the GCST90014456 cohort. This figure shows the causal estimates of selected pyroptosis-related DNA methylation sites on psoriasis risk. Each horizontal line represents the OR and 95% confidence interval for a methylation site. The displayed sites met the significance criteria of p_SMR_multi < 0.05, p_SMR < 0.05, P-HEIDI > 0.05, and showed strong colocalization evidence (PPH3 < 0.5 & PPH4 > 0.5)

Expression of pyroptosis-related genes in psoriasis

18 pyroptosis-related genes were identified whose expression was significantly associated with psoriasis (p_SMR_multi < 0.05, p_SMR  <  0.05, and P-HEIDI >  0.05) (Fig. 3), with all SMR analysis results detailed in Table S6. Among these, high expression of 9 genes (ADAR, ATG7, CITED2, DHX8, HMGB1, HSP90B1, IL18R1, KIF11, TFAM) was positively correlated with psoriasis risk, while high expression of the remaining genes was negatively correlated with psoriasis risk. In the SMR results above, 11 genes (ATG5, ATG7, CITED2, DHX8, DNMT3B, H3-3B, HSP90B1, KIF11, PROM2, SMAD2, TPM3) were validated in the UKB cohort (Refer to Table S7 for all SMR analysis results), whereas no genes were validated in the FinnGen cohort (Refer to Table S8 for all SMR analysis results). Additionally, in the discovery cohort, we found that the expression of KIF11 gene showed strong colocalization evidence (PPH3 <  0.5 & PPH4  >  0.5) (Fig. S1B).

Fig. 3.

Forest plot of the SMR analysis results for eQTLs in the GCST90014456 cohort. This figure shows the causal estimates of selected pyroptosis-related gene expression levels on psoriasis risk. Each horizontal line represents the OR and 95% confidence interval for a gene. The displayed genes met the significance criteria of p_SMR_multi <  0.05, p_SMR <  0.05, and P-HEIDI >  0.05. Values of PPH3  < 0.5 & PPH4 > 0.5 indicate evidence of colocalization between the eQTL and psoriasis GWAS signals

Protein abundance of pyroptosis-related genes in psoriasis

The abundance of two proteins (DAF and NF_kappa_B_p105) was found to be associated with the risk of psoriasis (P_SMR-multi <  0.05, P_SMR < 0.05, P-HEIDI > 0.05) (Refer to Table S9 for all SMR analysis results). This suggests that these proteins may play a key role in the pathological process of psoriasis, particularly in mechanisms related to pyroptosis. In the SMR analysis results, the NF_kappa_B_p105 protein (corresponding to the NFKB1 gene) was validated in both the FinnGen and UKB cohorts (Refer to Tables S10 and S11 for related SMR analysis results).

Integrate evidence from multiple omics levels

Then integrating key results from the SMR analysis to further examine the expression of psoriasis-related pyroptosis genes regulated by DNA methylation in the blood. Drawing on significant findings from psoriasis and psoriasis -related mQTL and eQTL (Table S12), through SMR analysis and screening (P_SMR-Multi <  0.05, P_SMR < 0.05, P-HEIDI > 0.05), 6 intersection genes (IL18R1, DNMT3B, MFHAS1, ADAR, PROM2, HMGB1) were identified as being genetically associated with psoriasis. However, we further combined the SMR analyses of blood mQTL-eQTL and found that ADAR (cg27530370) appeared in all of the above results and the result was significant (Table 2). However, the combined eQTL and pQTL analysis did not yield positive results for this portion. Manhattan plots of SMR analysis results could be found in Fig. 4. We also illustrated the distribution of significant signals using locus zoom plots and demonstrated their impact on psoriasis risk with SMR effect plots (Fig. 5). For ADAR, we assessed risk associations and regulatory directions through OR values. The methylation level of the CpG site cg27530370 in ADAR was negatively associated with psoriasis risk (OR = 0.982, 95% CI [0.967–0.996]); while the expression level of the ADAR gene was positively associated with psoriasis risk (OR = 1.027, 95% CI [1.007–1.048]). The methylation level of cg27530370 was negatively regulated by ADAR gene expression (OR  =  0.505, 95% CI [0.421–0.607]). This suggests a potential mechanism where higher methylation levels of cg27530370 may suppress ADAR gene expression, thereby reducing the risk of psoriasis.

Table 2.

Regulatory relationship between mQTL and eQTL

Expo_probe	Outco_Gene	p_SMR	multi_pvalue_SMR	FDR	p_HEIDI	OR (95% CI)
cg27530370	ADAR	2.2e−13	2.2e−13	5.449E−12	0.115	0.505(0.421–0.607)

Fig. 4.

Results of the Manhattan plot analysis for the GCST90014456 cohort. A Results of the Manhattan plot analysis for mQTLs. B Results of the Manhattan plot analysis for eQTLs. C Results of the Manhattan plot analysis for pQTLs. The horizontal line indicates the significance threshold (p-SMR-multi < 0.05). The mQTL and eQTL analyses identified multiple significant signals, with the ADAR gene appearing as a prominent cross-omics hit. The pQTL analysis revealed two proteins, NFKB1 and CD55, significantly associated with psoriasis risk

Fig. 5.

Analysis of the key gene ADAR and its methylation site cg27530370 A Locus plot of ADAR (ENSG00000160710) showing the genetic consistency between the eQTL and psoriasis GWAS signals in this genomic region B SMR effect plot of ADAR illustrating the association between its genetically predicted expression and psoriasis risk C Locus plot of the methylation site cg27530370 showing the genetic consistency between the mQTL and psoriasis GWAS signals D SMR effect plot of cg27530370 illustrating the association between this methylation site and psoriasis risk

Additionally, our study integrated all analysis data and found that the DNMT3B, PROM2, and KIF11 genes and their methylation sites were validated in the UKB cohort. The NFKB1 was validated in both the UKB and FinnGen cohorts, indicating a likely close genetic relationship with psoriasis. Future research should focus on these genes to explore the specific molecular mechanisms and underlying associations with psoriasis.

Tissue specificity of the identified QTL signals

To evaluate whether the blood-derived QTL signals reflect regulatory mechanisms relevant to the target tissue of psoriasis, we further examined cis-eQTL effects in GTEx (v8) skin—both sun-exposed and non-sun-exposed. Applying the same SMR significance and pleiotropy thresholds (p_SMR_multi <  0.05, p_SMR < 0.05, P-HEIDI >  0.05), none of the prioritized pyroptosis-related genes exhibited significant associations with psoriasis in either skin tissue (Tables S13, S14). These negative findings suggest that the genetically regulated expression captured by current skin cis-eQTL datasets does not support tissue-level replication of the blood-based signals. Collectively, this highlights the tissue-specific nature of regulatory architecture and underscores the need for caution when interpreting cross-tissue MR/SMR inferences.

Discussion

In summary, our study’s results indicate a significant association between pyroptosis-related genes and psoriasis pathogenesis. Through rigorous analysis of multi-omics data, including eQTLs, mQTLs, and pQTLs, combined with GWAS datasets and SMR analysis, we identified several key genetic markers. Notably, 82 methylation sites, 18 genes, and 2 proteins were initially found to be putative causal factors for psoriasis. Subsequent colocalization analysis further supported the involvement of KIF11 and NFKB1. Replication in independent cohorts confirmed these associations, highlighting ADAR, DNMT3B, PROM2, KIF11, and NFKB1 as pivotal in the pyroptosis-psoriasis connection. These findings underscore the complex genetic landscape influencing psoriasis and highlight candidate genes warranting further investigation as potential targets for therapeutic intervention.

While our study specifically investigated the role of pyroptosis-related genes, it is important to consider the potential pleiotropic effects of the prioritized genes. Mendelian randomization relies on the assumption that genetic instruments influence the outcome primarily through the exposure of interest. However, several of our key genes are involved in broad biological processes beyond pyroptosis. For instance, NFKB1 encodes a subunit of the NF-κB transcription factor, a master regulator of inflammation, cell survival, and proliferation with well-documented roles in psoriasis pathogenesis through various mechanisms [23]. Similarly, ADAR is central to RNA editing processes [24], DNMT3B functions in epigenetic silencing [25], and KIF11 is essential for cell division [26]. Although the SMR and HEIDI tests were employed to mitigate bias from linkage disequilibrium, these statistical methods cannot entirely rule out the possibility that the identified genetic associations are mediated by alternative biological pathways. Therefore, our findings should be interpreted as highlighting robust genetic associations, with the pyroptosis-related hypothesis being a primary, but not exclusive, explanatory framework.

ADAR was particularly prominent in our study, appearing repeatedly across multiple levels of analysis, which showed consistent associations with psoriasis risk at mQTL, eQTL and mQTL-eQTL levels. ADAR1 (a member of the ADAR family) is a key player in adenosine-to-inosine (A-to-I) RNA editing, which is crucial for normal brain development and function [27]. ADAR1, particularly its P150 isoform, competes with Z-DNA/RNA binding protein 1 (ZBP1) for binding to Z-RNA, thereby suppressing ZBP1-dependent PANoptosis, a combined form of pyroptosis, apoptosis, and necroptosis, in neurons exposed to sevoflurane [28]. This protective effect is further supported by studies showing that ADAR1 attenuates pyroptosis and lung injury in sepsis through modulation of the miR-21/A20/NLRP3 signaling pathway [29]. Collectively, these findings underscore the critical role of ADAR1 in mitigating pyroptosis in various pathological contexts. Although direct evidence linking ADAR to pyroptosis function and psoriasis is currently lacking, its role in these basic processes suggests it may influence pyroptosis function and RNA editing, thereby impacting psoriasis. Novel ADAR1 mutations were identified in three cases of co-occurring psoriasis and dyschromatosis symmetrica hereditaria, implicating altered RNA editing processes in the pathogenesis of these skin disorders [30]. These findings suggest that ADAR1 may contribute to the development of psoriasis through disruption of RNA editing, highlighting its potential role in the disease mechanism, which is corresponding to the analysis results of our study that the expression of ADAR may increase the risk of psoriasis. While there is currently no direct evidence linking ADAR1 to pyroptosis in psoriasis, its role in RNA editing implies a possible indirect influence on psoriasis development, further supported by the co-occurrence of ADAR1 mutations with psoriasis. Changes in ADAR1 expression could serve as an early diagnostic marker and aid in monitoring disease progression.

In addition, DNMT3B, PROM2, and KIF11 genes showed consistent associations with psoriasis risk at mQTL and eQTL levels, and these associations were validated in the UK Biobank cohort, making them important potential candidate genes. The DNMT3B gene encodes a DNA methyltransferase that plays a critical role in de novo methylation, a process essential for influencing various biological processes including embryonic development and the onset of diseases [31]. Pyroptosis occurs in rat mesangial cells (RMCs) when stimulated with OX7 antibodies and normal rat serum (NRS). This process involves the positive expression of gasdermin E (GSDME), a key mediator of pyroptosis. However, DNMT3B reduces GSDME expression, indicating its role in modulating pyroptosis [32]. Additionally, DNMT3B plays a role in the epigenetic regulation of gene expression and has been implicated in the pathogenesis and treatment response of psoriasis [33]. Polymorphisms in DNMT3B, such as rs2424913, are associated with inadequate treatment response to methotrexate in patients with moderate to severe plaque psoriasis, conferring a 4-fold increased risk of treatment failure compared to those with the wild-type genotype [34]. This suggests that DNMT3B variants may influence the efficacy of methotrexate through altered methylation patterns affecting drug metabolism or target gene expression [33]. Therefore, DNMT3B could serve as a diagnostic biomarker to identify patients who may not respond well to methotrexate, allowing for personalized treatment planning; a therapeutic target to enhance the effectiveness of existing treatments by modulating its methylation activity; a source of mechanistic insight leading to the development of novel therapeutic strategies aimed at correcting aberrant methylation patterns associated with psoriasis; and a predictive tool for genetic testing to guide therapy selection and reduce the risk of ineffective treatments and adverse side effects. These potential clinical roles highlight the importance of further research into the specific mechanisms by which DNMT3B influences psoriasis and its treatment, paving the way for more effective personalized medicine approaches.

PROM2 (Prominin 2) is a transmembrane protein involved in membrane dynamics, including vesicle formation and maintenance of cellular polarity [35]. In the context of pyroptosis, PROM2 might influence membrane integrity and inflammatory responses. Although direct links between PROM2 and pyroptosis are still under investigation, its role in membrane dynamics suggests it could modulate this process [36]. PROM2, through its potential regulation of membrane vesicles and inflammation, might indirectly influence psoriasis pathogenesis by affecting pyroptotic pathways and immune responses. However, more research is needed to clarify these connections and determine PROM2’s precise role in pyroptosis and psoriasis. Although direct links between PROM2 and pyroptosis remain under investigation, its impact on membrane integrity and inflammatory responses suggests an indirect role in psoriasis pathology, indicating the need for further exploration. Early diagnosis through PROM2 detection, the development of new therapies targeting PROM2, and improved disease management by monitoring PROM2 expression levels all represent potential clinical values that require validation through further research, particularly clinical trials, to establish the safety and efficacy of PROM2-targeted treatments.

KIF11 is core pyroptosis-related genes influencing the therapeutic effect of dehydroabietic acid in liver cancer. Bioinformatics analyses identified KIF11 as key targets in liver cancer treatment with dehydroabietic acid. The study showed that the mRNA and protein levels of KIF11 decreased significantly following treatment with dehydroabietic acid, suggesting that KIF11 plays a crucial role in the mechanism of action of pyroptosis [37]. Currently, there is no direct evidence or publication details provided to summarize the relationship between KIF11 and psoriasis or its mechanism of action in psoriasis. However, a key characteristic of psoriasis is the abnormal rapid proliferation of epidermal keratinocytes, in which KIF11, a crucial motor protein in cell mitosis, may play a significant role [38]. Therefore, we speculate that the overexpression or dysfunction of KIF11 could contribute to the excessive proliferation of keratinocytes in psoriasis. Previous studies and our findings both emphasize the importance of KIF11 in cell division, although direct evidence linking KIF11 to psoriasis is yet to be established. Its role in cell division suggests a potential involvement in psoriasis pathogenesis. While KIF11 has been identified as a key target in hepatocellular carcinoma treatment, its relevance in psoriasis requires further investigation. Nonetheless, the success of targeting KIF11 in other contexts provides a rationale for exploring its therapeutic potential in psoriasis. Utilizing KIF11 as a biomarker for early diagnosis, developing new therapeutic strategies targeting cell proliferation and inflammation, and managing psoriasis through monitoring KIF11 expression levels all represent potential clinical values that warrant further validation, particularly through clinical trials, to determine the safety and efficacy of KIF11-targeted therapies.Additionally, we observed that NFKB1 exhibits a strong colocalization effect, which was validated in both the UKB and FinnGen cohorts, suggesting a close causal relationship with psoriasis. NFKB1 is a key transcription factor that regulates the expression of genes involved in inflammation and immune responses, which has been shown to modulate pyroptosis. Specifically, NFKB1 plays a critical role in inhibiting NLRP3 inflammasome-mediated pyroptosis, as demonstrated in a study where MG53 protected against Coxsackievirus B3-induced acute viral myocarditis in mice through the NF-κB signaling pathway [39]. Furthermore, the involvement of NFKB1 in the regulation of pyroptosis-related genes has also been implicated in diabetic keratopathy, where a hub gene signature associated with pyroptosis was identified and validated [40]. Although direct evidence linking NFKB1 to pyroptosis function and psoriasis is currently lacking, its role in NF-κB pathway suggests it may influence pyroptosis function, thereby impacting psoriasis. Genetic variants in NFKB1 have been associated with clinical features of psoriasis vulgaris in a Han Chinese population [41], and polymorphisms in the NF-κB pathway, including NFKB1, distinguish patients with mild from severe psoriasis [42]. Additionally, a variant in the NF-κB pathway inhibitor NFKBIA distinguishes patients with psoriatic arthritis within the spectrum of psoriatic disease [43]. Integrated analysis of gene expression profiles has identified transcription factors, including those potentially involved in the NF-κB pathway, as key players in psoriasis pathogenesis. Early diagnosis and disease monitoring through NFKB1 detection, development of new therapies targeting inflammatory and cell death mechanisms, and personalized medical approaches based on NFKB1 variant detection all represent potential clinical values that require validation through further research, particularly clinical trials, to establish the safety and efficacy of NFKB1-targeted treatments.

In this study, SMR analysis was utilized to explore the causal relationship between pyroptosis-related genes and psoriasis, employing a multi-omics approach that encompassed comprehensive analysis of methylation, gene expression, and protein abundance. Through the integration of multi-omics level results, this study aims to construct a comprehensive regulatory network and explore the synergistic roles of these genes in the pathologic progression of psoriasis and pyroptosis. This multidimensional approach enhanced the reliability and comprehensiveness of our findings. Despite the integrative multi-omics design, several limitations of this study warrant consideration. First, the MR framework, while powerful for inferring causality, remains an observational approach and cannot definitively establish causal relationships without further experimental validation. Second, our analysis was constrained by its reliance on publicly available summary-level data. This precluded the control for specific clinical covariates such as BMI and comorbidities beyond the standard adjustments in the original studies, and residual confounding cannot be entirely excluded. Third, our selection of pyroptosis-related genes was based on the GeneCards database, which, while comprehensive, may include genes with diverse or indirect roles. Furthermore, the genetic instruments were primarily derived from European-ancestry cohorts. Although this minimizes population stratification, it limits the generalizability of our findings to other ethnicities. The validation of 11 genes solely in the UK Biobank but not in the FinnGen cohort further highlights the potential for population-specific genetic effects even within European subpopulations. Fourth, a key limitation is the predominant use of blood-derived QTL data to investigate a skin disease. It is recognized that a proportion of genetic regulatory effects are tissue-specific; for instance, evidence suggests that approximately 5–10% of mQTLs exhibit tissue- or cell type-specificity [44]. Our unsuccessful attempt to replicate the primary blood QTL signals in skin tissue resources further underscores this tissue-specific nature of genetic regulation. Therefore, our findings should be interpreted with caution, as they may reflect systemic biological processes rather than disease-specific mechanisms operating within the skin. Finally, the evidence presented here is solely computational. Direct experimental or clinical validation using psoriatic skin tissues or relevant cellular models is crucial to confirm the expression, regulation, and functional roles of the prioritized genes including ADAR, DNMT3B, KIF11 in the local pathophysiology of psoriasis. Future functional studies are essential to translate these statistical associations into a concrete biological and clinical understanding.

Conclusions

In summary, this study provides genetic evidence consistent with a putative causal role for cell pyroptosis-related genes in psoriasis through SMR analysis and multi-omics methods, while discussing the possible mechanisms of ADAR, DNMT3B, PROM2, KIF11, and NFKB1 in cell pyroptosis and psoriasis based on existing literature. These findings not only highlight the complex role of epigenetic regulation in disease progression but also suggest that future research should focus on elucidating the intricate interactions among these genes with the aim of comprehensively understanding the pathophysiological mechanisms intertwining cell pyroptosis and psoriasis. This could ultimately provide more precise targets for disease prevention and treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

QTL

Quantitative trait locus

SMR

Summary data-based Mendelian randomization

Author contributions

Conception and design: Wenwu Dong. Administrative support: Cuiping Shi. Collection and assembly of data: Wenwu Dong. Data analysis and interpretation: Wenwu Dong, Cuiping Shi. Manuscript writing: Wenwu Dong, Cuiping Shi. Final approval of manuscript: Cuiping Shi.

Funding

None.

Data availability

All data generated or analysed during this study are included in this published article.

Code availability

The analysis code for this study has been deposited in the GitHub repository at: https://github.com/dwwsmile/-Psoriasis-Pyroptosis-SMR-.

Declarations

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable