Genetic analysis of potential biomarkers and therapeutic targets in hypoxia and pyroptosis from psoriasis

Jing Cui; Nianyi Zhang; Liuyi Yang; Xiaoping Shen; Ming Ni

doi:10.1016/j.bbrep.2025.102314

. 2025 Nov 5;44:102314. doi: 10.1016/j.bbrep.2025.102314

Genetic analysis of potential biomarkers and therapeutic targets in hypoxia and pyroptosis from psoriasis

Jing Cui ^a, Nianyi Zhang ^a, Liuyi Yang ^a, Xiaoping Shen ^a, Ming Ni ^b,^⁎

PMCID: PMC12636341 PMID: 41282541

Abstract

Background

Pyroptosis and hypoxia play pivotal roles in the onset and progression of psoriasis, though their interactions remain poorly understood. This study aims to clarify the involvement of pyroptosis-related genes (PRGs) and hypoxia-related genes (HRGs) in psoriasis pathogenesis.

Methods

Psoriasis-related datasets were analyzed alongside PRGs and HRGs. Differentially expressed genes (DEGs) between psoriasis and control samples were first identified. Expression levels of PRGs and HRGs were used to compute respective scores, which facilitated the identification of key module genes. Candidate genes were then derived by intersecting DEGs with key module genes. Biomarkers were selected using machine learning algorithms, gene expression analysis, and receiver operating characteristic (ROC) curves. A nomogram was constructed and subsequently validated. Additional analyses were conducted to investigate the underlying mechanisms. Finally, biomarker expression was assessed via real-time reverse transcriptase-polymerase chain reaction (RT-qPCR).

Results

PI3 and LCE3D, exhibiting significantly elevated expression and an area under the curve (AUC) greater than 0.9, were identified as biomarkers. The nomogram constructed with these biomarkers accurately predicted the risk of psoriasis. Enrichment analyses revealed that the cytosolic DNA-sensing pathway, focal adhesion, and oxidative phosphorylation were significantly associated with these biomarkers. Immune infiltration analysis highlighted 20 distinct cell types with significant expression differences between psoriasis and control samples. Furthermore, 18 potential therapeutic drugs were predicted based on the biomarkers. RT-qPCR validation confirmed elevated biomarker expression in psoriasis.

Conclusion

This study identified two biomarkers, PI3 and LCE3D, linked to pyroptosis and hypoxia in psoriasis. These findings provide valuable insights that could guide future therapeutic strategies for psoriasis.

Keywords: Psoriasis, Pyroptosis, Hypoxia, Biomarkers, Immune infiltration analysis

Highlights

•
Dual Roles of Pyroptosis and Hypoxia: Pyroptosis and hypoxia are pivotal in psoriasis pathogenesis, aggravating the disease through distinct mechanisms like modulating inflammation and cellular metabolism.
•
Limitations of Existing Research: Prior studies have mostly examined pyroptosis and hypoxia in psoriasis in isolation, with their combined effects underexplored. This study bridges this gap by integrating these two mechanisms.
•
Comprehensive Analytical Approach: The study systematically analyzes pyroptosis - and hypoxia - related genes using transcriptomic data and bioinformatics to identify potential psoriasis biomarkers.
•
Clinical Application Prospects: The study aims to offer new insights for psoriasis diagnosis and treatment, advancing precision medicine and improving patients' quality of life.

1. Introduction

Psoriasis, a chronic inflammatory skin disorder with a multifactorial etiology that includes both genetic and environmental factors, is characterized by inflammation, excessive epidermal proliferation, disrupted epidermal maturation, and alterations in vascular structure [1,2]. The disease's prevalence shows significant geographical variability, ranging from 0.51 % to 11.43 % in adults and 0 %–1.37 % in children [3]. The World Health Organization (WHO) classifies psoriasis as a chronic, incurable, non-infectious, painful, disabling, and disfiguring skin condition [4]. Clinically, psoriasis manifests in various phenotypes, most commonly as chronic plaque psoriasis, which presents as well-demarcated erythematous plaques with silvery scales, typically affecting the scalp, elbows, lumbosacral region, and knees. Other subtypes include guttate, pustular, and erythrodermic psoriasis, all of which involve skin inflammation and immune responses. T-cell-mediated immune dysfunction primarily drives the pathogenesis of psoriasis. The disease has a complex etiology involving genetic predisposition and environmental triggers such as infections, medications, psychological stress, and climatic conditions. Psoriasis is often associated with comorbidities, including obesity, type 2 diabetes, hypertension, cardiovascular disease, and mental health disorders such as depression and suicidal ideation [2,5]. Treatment strategies include topical therapies (corticosteroids, vitamin D3 analogs), oral systemic agents (methotrexate, cyclosporine), and biologic treatments (anti-TNFα, IL-12/23 inhibitors, and IL-17 inhibitors) [2]. Although psoriasis remains incurable, treatment focuses on symptom management, disease progression control, and improving quality of life [2,6]. Despite advancements, challenges remain, such as the need for personalized treatment approaches due to diverse disease phenotypes and immunogenetic mechanisms, as well as concerns over treatment failure and resistance, particularly with biologic therapies [2,6]. Continued research into the pathophysiological mechanisms is crucial for developing novel therapeutic strategies and providing more tailored treatment options for patients.

Pyroptosis and hypoxia are critical biological and pathological concepts that play pivotal roles in various diseases, including psoriasis [[7], [8], [9], [10]]. Pyroptosis is a form of programmed cell death marked by rapid rupture of the plasma membrane and the release of pro-inflammatory intracellular mediators. This process is driven by inflammasome-activated inflammatory caspases, such as caspase-1, which cleave Gasdermin proteins to form membrane pores, leading to cell death. Pyroptosis is involved in host defense, inflammatory responses, and tumorigenesis, and it contributes to the regulation of both innate and adaptive immunity by eliminating infected cells and promoting the release of inflammatory cytokines. Its signaling pathways include inflammasome activation and interactions with immune pathways such as Toll-like receptors and interferons [7,8].

Hypoxia refers to a deficiency in oxygen supply to cells or tissues, which triggers the activation of Hypoxia-inducible factors (HIFs). These factors are essential for regulating cell metabolism, angiogenesis, energy balance, and cell survival [9,10]. Under hypoxic conditions, HIFs drive the expression of target genes, such as VEGF, to enhance oxygen delivery and facilitate adaptation to low-oxygen environments [9,10]. Pyroptosis and hypoxia may play significant roles in the pathogenesis of psoriasis. For instance, HIF activation in hypoxic conditions promotes keratinocyte proliferation and inflammatory cytokine expression, while pyroptosis may exacerbate skin inflammation. Single-cell transcriptomic analyses have revealed substantial alterations in the expression patterns of pyroptosis-related genes (PRGs) and hypoxia-related genes (HRGs) in psoriasis and other autoimmune diseases, highlighting their potential contribution to disease progression [[7], [8], [9], [10]].

Previous studies have predominantly examined the individual roles of pyroptosis or hypoxia in psoriasis, with few studies exploring their synergistic effects. Recognizing the importance of integrating these mechanisms to understand the pathophysiology of psoriasis and identify new therapeutic targets, this study utilized transcriptomic data and bioinformatics to identify biomarkers associated with both pyroptosis and hypoxia in psoriasis. The study further investigated the diagnostic value and molecular regulatory mechanisms of these biomarkers, aiming to provide new insights for clinical diagnosis and treatment of psoriasis.

2. Materials and methods

2.1. Data collection

Psoriasis-related datasets (GSE13355 and GSE14905) (GPL750) were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The GSE13355 dataset contained 58 psoriasis and 64 control skin tissue samples, serving as the training set, while GSE14905 comprised 33 psoriasis and 21 control skin tissue samples, used as the validation set. PRGs (169 genes) [11] and HRGs (200 genes) [12] were obtained from the literature.

2.2. Differential expression analysis

In the training set (GSE13355), differentially expressed genes (DEGs) between psoriasis and control groups were identified using the limma package (v 3.54.1) [13], with thresholds set at |log₂ Fold Change (FC)| > 1 and adjusted p-value <0.05. For more stringent identification, the filtering criteria were adjusted to |log₂FC| > 2 and adjusted p-value <0.01. Volcano and heat maps of DEGs were generated using the ggplot2 (v 3.3.6) [14] and pheatmap (v 1.0.12) [15] packages, respectively.

Using PRGs and HRGs as background gene sets, PRG and HRG scores for psoriasis and control samples were calculated via the GSVA package (v 1.42.0) [16]. The differences in PRG and HRG scores between the two groups were assessed using the Wilcoxon test (p < 0.05).

2.3. Weighted gene co-expression network analysis (WGCNA)

To explore genes associated with PRG and HRG scores, WGCNA was performed using the WGCNA package (v 1.71) [17]. WGCNA is a computational method that identifies associations between gene sets and biological traits by constructing gene co-expression networks. To ensure data reliability, hierarchical clustering analysis was first conducted on all samples in training set, and the ward. D2 clustering method was used to calculate clustering distances based on the gene expression matrix, which enabled the identification and subsequent removal of outliers present among samples. Subsequently, as the soft threshold determines the weight of gene expression correlation in the network, the optimal soft threshold (β) was selected to make the network approximately satisfy the scale-free topology characteristic (with a scale-free R² value near 0.9 and a mean connectivity value near 0), thereby constructing the gene co-expression network. Finally, PRGs score and HRGs score as trait, correlation analysis was used to explore relation of module eigengene (ME) score of above obtained modules and trait. Genes were further screened from these key modules, and ultimately, genes that simultaneously satisfied the conditions of high gene significance (GS, |GS| > 0.2; a metric for evaluating the correlation between a gene and the target trait) and high module membership (MM, |MM| > 0.8; a metric for evaluating the similarity between a gene and the eigengene of its corresponding module) were designated as key module genes.

2.4. Identification and analysis of candidate genes

Candidate genes were identified by intersecting DEGs and key module genes using the ComplexUpset package (v 1.3.3) [18]. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed to investigate the biological roles and signaling pathways related to these candidate genes, utilizing the clusterProfiler package (v 4.6.2) [19] (adj.p < 0.05). Protein-level correlations for candidate genes were analyzed through the STRING database (https://string-db.org), and a protein-protein interaction (PPI) network was constructed with a confidence score threshold of ≥0.4. The results were visualized using Cytoscape software (v 3.9.0) [20].

2.5. Identification of biomarkers

To identify candidate hub genes, two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were employed. LASSO, an embedded feature selection method, achieved automatic variable screening by introducing an L1 regularization penalty term into the loss function of the regression model. Its analysis was implemented using the glmnet package (v 4.1–4) [21]. The parameter family = binomial was set to implement Lasso logistic regression. Five-fold cross-validation was employed, and the model was constructed based on the criterion of minimum mean squared error (lambda.min). Genes with regression coefficients not penalized to 0 were screened out and designated as potential feature genes 1. while SVM-RFE, a wrapper-based feature selection method, was implemented using the caret package (v 6.0–93) [22]. By means of the RFE method, gene importance and rankings were iteratively calculated during the process of gradually reducing all input gene combinations to a single gene combination; in each iteration, unimportant genes were removed, and the importance of the remaining genes was re-evaluated. Ten-fold cross-validation was used to calculate the error rates under different features, and the gene combination with the lowest error rate was selected as potential feature genes 2. Candidate hub genes were identified by intersecting the signature genes obtained from LASSO and SVM-RFE. The expression levels of these candidate hub genes were compared between psoriasis and control groups using the Wilcoxon test in both the training and validation datasets (p < 0.05). Genes exhibiting significant differences and consistent trends between the psoriasis and control groups across both datasets were selected as candidate biomarkers. The diagnostic performance of these biomarkers was assessed by constructing receiver operating characteristic (ROC) curves using the pROC package (v 1.18.0) [23]. Genes with an area under the curve (AUC) greater than 0.7 were considered valid biomarkers, indicating their potential as diagnostic tools.

2.6. Construction of nomogram

Based on the identified biomarkers, a nomogram was developed to predict the risk of psoriasis using the rms package (v 6.5–0) [24]. The predictive accuracy of this nomogram was thoroughly evaluated using calibration curves, clinical impact curves (CIC), decision curve analysis (DCA), and ROC curves to validate its predictive performance. These analyses were carried out using the regplot (v 1.1) [25], rmda (v 1.6) [26], and pROC packages, respectively.

2.7. Gene set enrichment analysis (GSEA) and GeneMANIA analysis

To explore the biological functions associated with the progression of psoriasis in relation to the biomarkers, GSEA was performed using the clusterProfiler package. Spearman correlation analysis was performed to compute the correlation coefficients between biomarkers and all genes using the psych package (v 2.2.9) [27]. Genes were ranked based on the correlation coefficient, and GSEA was then carried out using the background gene set c2.cp.kegg.v7.4.symbols.gmt from the GSEA website (https://www.gsea-msigdb.org/gsea/index.jsp) (adj.p < 0.05, |NES| > 1), which includes curated gene sets from the KEGG pathway database. The top five enriched pathways, ranked by p-value from lowest to highest, were visualized using the enrichplot package (v 1.18.0) [28]. Additional genes associated with the functions of the biomarkers were identified using the GeneMANIA database (https://genemania.org/).

2.8. Immune microenvironment analysis

Immune infiltration of 28 immune cell types was calculated using the ssGSEA algorithm for both psoriasis and control groups in the training dataset. The differences in immune cell composition between the two groups were assessed using the Wilcoxon test (p < 0.05). The results were visualized in boxplots created with the ggplot2 package. Additionally, the correlation between differentially expressed immune cells and biomarkers was analyzed using the psych package (|cor| > 0.3, p < 0.05). To further explore the correlation between biomarkers and pathways involved in inflammation, immunity, and development, enrichment scores for 50 representative pathways were calculated, including those related to inflammation and immunity, in psoriasis samples from the training dataset using the ssGSEA algorithm from the GSVA package. Spearman correlation analysis was then performed to calculate the correlation between biomarkers and inflammation-related pathways (|cor| > 0.3, p < 0.05), using the h.all.v2023.2.Hs.symbols.gmt gene set as a reference.

2.9. Regulatory network analysis and drug prediction

The NetworkAnalyst website (https://www.networkanalyst.ca/NetworkAnalyst/) was used to predict transcription factors (TFs) targeting biomarkers, exploring the molecular regulatory mechanisms of biomarkers in psoriasis. The miRNAs targeting these biomarkers were predicted using the miRWalk database (http://mirwalk.uni-hd.de/). Following this, long noncoding RNAs (lncRNAs) targeting the identified miRNAs were predicted using the StarBase database (http://starbase.sysu.edu.cn/) (clipExpNum >20). After identifying miRNAs and lncRNAs, a lncRNA-miRNA-mRNA regulatory network was constructed using the ggalluvial package (v 0.12.5) [29]. Additionally, potential drugs for psoriasis were predicted using the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/), and a drug-biomarker network was visualized using Cytoscape.

2.10. Expression analysis of prognosis genes

Five pairs of samples (5 psoriasis and 5 control samples) were collected from participants at the Affiliated Hospital of Guizhou Medical University. All participants provided informed consent, and the study was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University (approval number: 2021549).

Total RNA from the 10 collected samples (50 mg) was extracted using 1 mL of TRIzol reagent (Ambion, USA) according to the manufacturer’s protocol. The RNA concentration was measured using the NanoPhotometer N50. cDNA was synthesized by reverse transcription using the SureScript-First-strand-cDNA-synthesis-kit, and the reverse transcription process was performed with the S1000™ Thermal Cycler (Bio-Rad, USA). The quantitative polymerase chain reaction (qPCR) assay was carried out using the CFX Connect Real-time Quantitative Fluorescence PCR Instrument (Bio-Rad, USA), with an initial denaturation step at 95 °C for 1 min, followed by 40 cycles of denaturation at 95 °C for 20 s, annealing at 55 °C for 20 s, and extension at 72 °C for 30 s. The relative quantification of mRNAs was calculated using the 2^–ΔΔCT method. The sequences of all primers can be found in Supplementary Table 1.

2.11. Statistical analysis

R software (v 4.2.2) was used for data processing and analysis. The statistical significance between two groups was evaluated using the Wilcoxon rank-sum test (p < 0.05).

3. Results

3.1. DEGs and key module genes associated with psoriasis were selected

Differential expression analysis identified 92 DEGs between psoriasis and control groups, with 72 genes upregulated and 20 downregulated, highlighting their relevance to psoriasis and highlighting their potential for further investigation (|log₂FC| > 2, adj.p < 0.01) (Fig. 1A and B). PRGs scores were higher in psoriasis, whereas HRGs scores were lower (Fig. 1C). No outlier samples were detected in the training dataset, allowing for the inclusion of all samples in subsequent analyses (Supplementary Fig. 1). A soft threshold of 9, selected with R² = 0.9, ensured mean connectivity close to 0 (Fig. 1D). Consequently, nine co-expression modules were identified through similarity analysis (Fig. 1E). The MEbrown module showed the strongest positive correlation (cor = 0.71, p = 1e−19) with PRGs scores, while the MEturquoise module exhibited the strongest positive correlation (cor = 0.5, p = 6e−09) with HRGs scores (Fig. 1F). A total of 1710 key module genes were identified using criteria of |GS| > 0.2 and |MM| > 0.8 from these two modules (Supplementary Fig. 2).

Fig. 1 — DEGs and key module genes between psoriasis and control groups. (A) DEGs are depicted in a volcano plot, with up-regulated genes represented by red dots, downregulated genes represented by green dots, and non-significant genes represented by black dots. (B) Heatmap of the top 20 genes with the most prominent differential expression between psoriasis and control groups. (C) The violin plots illustrate the distribution and density of GSVA scores for each gene set across the control and psoriasis groups. Both of the GSVA scores of HRGs genes (left) and PRGs genes (right) in control (blue) and psoriasis (red) groups showed a significant difference, with a Wilcoxon p-value of 1.5e-05 and less than 2.2e-16, respectively. (D) The selection of soft threshold. The left panel showed the scale-free fit index (y-axis) under different soft thresholds (x-axis), where the red line indicated the selected value of the scale-free fit index. The right panel presented the network connectivity under different soft thresholds. (E) Cluster dendrogram produced by average linkage hierarchical clustering of genes based on topological overlap matrix (TOM). Each branch in the dendrogram is a line that represents a single gene. Each color indicates a single module that contained closely conserved genes. (F) Heatmap of WGCNA module-trait relationship results. Each color module and factor of interest contain a correlation coefficient and p-value (in parentheses). Red represents positive correlations and blue represents negative correlations between each color module and factor of interest.

3.2. Candidate genes and corresponding pathways

A set of 73 candidate genes was derived by intersecting the 92 DEGs with the 1710 key module genes (Fig. 2A). Enrichment analysis revealed 159 GO terms and 16 KEGG pathways associated with these 73 genes. Notable GO terms included keratinization, cornified envelope formation, and serine-type endopeptidase activity (Fig. 2B). KEGG pathways enriched included IL-17 signaling, viral protein interaction with cytokines and cytokine receptors, and cytokine-cytokine receptor interactions (Fig. 2C). A PPI network was constructed with 55 nodes and 209 edges, where S100A7, SPRR2B, PI3, and CXCL1 showed the strongest correlations with other genes (Fig. 2D).

3.3. PI3 and LCE3D were selected as biomarkers in psoriasis

Through LASSO regression analysis with the binomial family parameter, five signature genes (LCE3D, KRT16, S100A9, PI3, and DEFB4A) were identified (lambda._min = −7.49317114392034) (Fig. 3A). Subsequently, SVM-RFE selected PI3, LTF, and LCE3D as signature genes (Fig. 3B). By intersecting the five signature genes from LASSO and the three signature genes from SVM-RFE, two candidate hub genes (LCE3D and PI3) were identified (Fig. 3C).

Fig. 3 — The biomarkers in psoriasis were identified. (A) The left panel showed a LASSO logistic coefficient penalty plot, where the abscissa was log(Lambda) and the ordinate represented the cross-validation error. The right panel presented the coefficient distribution in the model, with each curve in the figure representing the change trajectory of each gene's coefficient, the ordinate being the coefficient value and the abscissa being log(Lambda).(B) The variable selection process using SVM-RFE. (C) The Venn diagram showed the overlap of feature genes identified by LASSO and SVM-RFE. (D) Expression analysis of candidate hub genes. The figure shows the expression levels of LCE3D and P13 in psoriasis and control groups. Based on this analysis in GSE13355 and GSE14905 datasets, it is demonstrated that LCE3D and P13 have significantly higher expression in psoriasis groups compared to the control groups. (E) ROC Curves for Candidate Biomarkers in GSE13355 and GSE14905 Datasets. Abbreviations: LASSO, least absolute shrinkage and selection operator. Support Vector Machine with Recursive Feature Elimination (SVM-RFE). ROC, the receiver operating characteristic. AUC, The area under the curve.

Expression analysis of the candidate hub genes revealed that LCE3D and PI3 exhibited significantly higher expression in the psoriasis groups from both the GSE13355 and GSE14905 datasets. Consequently, LCE3D and PI3 were selected as candidate biomarkers for further investigation (Fig. 3D). The diagnostic value of these biomarkers was assessed through ROC curve analysis, which indicated that the AUC values for both biomarkers exceeded 0.9 in both datasets. Thus, LCE3D and PI3 were identified as biomarkers linked to hypoxia and pyroptosis in psoriasis (Fig. 3E).

3.4. Nomogram well predicted the risk of psoriasis

A nomogram based on the selected biomarkers was developed to predict the risk of psoriasis, aiming to reduce redundancy in diagnostic and prognostic evaluations (Fig. 4A). The calibration curve for the nomogram closely matched the ideal curve (Fig. 4B), indicating excellent agreement between predicted and observed outcomes. DCA demonstrated that the nomogram provided a higher clinical net benefit (Fig. 4C). Furthermore, the CIC visually confirmed that the nomogram consistently offered a superior overall net benefit across a wide range of threshold probabilities, significantly influencing patient outcomes and highlighting its strong predictive value (Fig. 4D). Finally, an AUC value of 1 for the ROC curve confirmed the nomogram’s stable predictive performance (Fig. 4E). These results underscore the nomogram’s robust ability to predict psoriasis risk.

Fig. 4 — Predicting Psoriasis Risk Based on Biomarkers. (A) Nomogram based on 2 independent risk biomarkers, LCE3D and PI3, to evaluate the risk of psoriasis. (B) Calibration Curve for the Nomogram in Predicting Psoriasis Risk. (C) The DCA for the Nomogram. (D) The ROC curve for evaluating the performance of a risk prediction nomogram. The AUC is 1.000, indicating perfect discrimination ability of the nomogram in distinguishing between different outcomes. (E) The clinical impact curve of the nomogram model. In the figure, the red line represented the number of people with outcome events predicted by the model, and the blue line represented the actual number of people with outcome events.

3.5. Common pathways enriched by biomarkers

To explore the biological functions of the biomarkers in psoriasis, GSEA was performed. The top five enriched pathways for both LCE3D and PI3 included the cytosolic DNA-sensing pathway, focal adhesion, oxidative phosphorylation, and proteasome pathways (Fig. 5A and B). Additionally, the top 20 function-related genes included CELA1, CSTA, and others, with functions related to defense response to bacteria, antimicrobial humoral response, and more (Fig. 5C). These findings highlight the potential roles of LCE3D and PI3 in immune response regulation and the pathogenesis of psoriasis.

Fig. 5 — Pathway and network analysis of biomarkers. (A, B)GSEA of 5 Biomarkers in Psoriasis. (C) Functional Network Analysis of Top 20 Biomarker Genes. Abbreviations: GSEA, Gene Set Enrichment Analysis.

3.6. Biomarkers closely correlated with immune cells

Immune infiltration analysis revealed significant differences in the infiltration of 28 immune cell types between psoriasis and control groups (Fig. 6A). Of these, 20 immune cell types (including activated B cells, macrophages, mast cells, etc.) were notably different between the two groups, with most differential immune cells being more highly expressed in the psoriasis group (Fig. 6B). Activated CD8 T cells showed a significant positive correlation with activated CD4 T cells (p < 0.05) (Fig. 6C). Further correlation analysis demonstrated that the biomarkers were positively correlated with neutrophils, activated CD4 cells, activated CD8 cells, and others (cor >0.5, p < 0.05) (Fig. 6D). Additionally, analysis of biomarkers and representative pathways identified the top three pathways associated with biomarkers: UV response DN, coagulation, and epithelial-mesenchymal transition (Fig. 6E). These findings emphasize the complex immune landscape in psoriasis and suggest potential biomarkers and pathways that may contribute to the disease's pathogenesis and immune response.

Fig. 6 — The correlation between the biomarkers and immune cells. (A) The results of immune infiltration analysis. This heatmap illustrates differential infiltration levels of 28 distinct immune cell types between psoriasis patients and controls. (B) Comparison of Immune Cell Scores between Psoriasis and Control Groups. The boxplot visually demonstrates that the majority of the differentially expressed immune cells were higher in the psoriasis group compared to the control group. (C) Correlation between various immune cell types. In this heatmap,the x - axis and y - axis both list different immune cell types, allowing for a comprehensive comparison of correlations between pairs of cell types. (D) The correlation analysis between various immune cell types and specific biomarkers in two distinct datasets, LCE3D and Pl3. In this scatter plot, the x-axis represents the correlation coefficient, while the y-axis lists different immune cell types. Each point on the plot corresponds to a specific cell type, with the size of the point indicating the strength of the correlation. The analysis reveals a positive correlation between the biomarkers and neutrophils, activated CD4 cells, and activated CD8 cells, among others. (E) Correlation Analysis of Biomarkers with Representative Pathways. In this figure, the color gradient from light pink to dark blue represents the strength and direction of the correlation between the biomarkers and the pathways, the size of the dots corresponds to the statistical significance of the correlation.

3.7. Regulatory mechanism was explored by biomarkers

A total of 11 TFs predicted by LCE3D and 7 TFs predicted by PI3 were used to construct a TF-mRNA regulatory network. In this network, FOXC1 and FOXL1 were identified as key regulators of PI3 and LCE3D (Fig. 7A). Further network construction revealed that TUG1, MIR17HG, and MIR497HG regulate LCE3D via hsa-miR-140-3p, while MALAT1 and AC007952.4 regulate PI3 via hsa-miR-4306 (Fig. 7B). Following this, based on the biomarkers, a drug-mRNA network analysis was performed via the CTD, a drug-mRNA network was also constructed, consisting of 55 nodes and 70 edges. Among these, 18 compounds had potential associations with both biomarkers, including 2-methyl-4-isothiazolin-3-one, cadmium chloride, sodium dodecyl sulfate, among others (Fig. 7C). These findings provide valuable insights into the regulatory mechanisms and potential therapeutic targets for psoriasis, highlighting key biomarkers, miRNAs, and drugs that could influence treatment and management strategies.

Fig. 7 — Regulatory mechanism was explored by biomarkers. (A) Transcription Factor-mRNA Regulatory Network. The red diamonds represent the key genes LCE3D and PI3, while the green ellipses represent the transcription factors that interact with these genes. (B) miRNA-mediated Regulatory Network of mRNA and lncRNA. This Sankey diagram illustrates the regulatory network involving mRNAs (LCE3D and PI3), miRNAs, and lncRNAs. (C) Drug-mRNA Network. The network visualizes the connections between 18 drugs and the two key biomarkers, LCE3D and PI3. The nodes represent the drugs and the mRNAs, while the edges indicate the interactions between them. The network highlights potential therapeutic targets and drug candidates that could modulate the expression of LCE3D and PI3. Abbreviations: TF, transcription factor.

3.8. Biomarkers had higher expression in psoriasis

Finally, RT-qPCR analysis of the biomarkers confirmed their higher expression in the psoriasis group (Fig. 8A and B). This elevated expression suggests that these biomarkers may play a significant role in the pathogenesis or progression of psoriasis, potentially serving as useful indicators for disease diagnosis or monitoring.

4. Discussion

Pyroptosis and hypoxia play pivotal roles in the pathogenesis of psoriasis. This chronic inflammatory skin disease is characterized by abnormal keratinocyte proliferation and inflammatory cell infiltration. Hypoxia-induced activation of HIF promotes keratinocyte proliferation and inflammation, while pyroptosis exacerbates skin inflammation. Research has shown significant alterations in the expression of PRGs and HRGs in psoriasis, highlighting their key roles in disease progression [[7], [8], [9], [10]]. This study integrated bioinformatics and experimental validation to identify two biomarkers associated with pyroptosis and hypoxia (PI3 and LCE3D), offering valuable insights for the treatment of psoriasis.

DEGs and key module genes linked to psoriasis were selected, and subsequent enrichment analysis revealed 159 GO terms and 16 KEGG pathways, including keratinization [30,31], the IL-17 signaling pathway [[32], [33], [34]], and cytokine-cytokine receptor interactions [35,36]. Psoriasis is recognized as a T cell-mediated disease, where cytokines from Th1 and Th17 subsets, such as IFNγ, IL-17A, and IL-22, contribute to keratinocyte hyperproliferation, abnormal differentiation, and autoimmune amplification, leading to the formation of psoriatic plaques [37]. Excessive keratinocyte proliferation is a hallmark of psoriasis [37]. Alterations in the innate immune response of keratinocytes can trigger unregulated inflammatory reactions, driving the pathological cascade that characterizes the disease [30,31]. The IL-17 signaling pathway is a key immune cascade in psoriasis pathogenesis. IL-17A, a major cytokine produced by Th17 cells, binds to receptors on keratinocytes, triggering inflammation and hyperproliferation of keratinocytes. This cascade results in the recruitment of immune cells, including neutrophils, to the skin, contributing to psoriasis lesions. Inhibition of IL-17A or its receptor through targeted therapies has been shown to alleviate psoriasis symptoms, emphasizing the pathway's critical role in disease development [[32], [33], [34]]. Cytokine-cytokine receptor interactions also play a pivotal role in psoriasis by mediating the binding of cytokines like IL-17A, IL-22, IL-23, and TNF-α to their receptors on cells, triggering inflammation and abnormal skin cell growth. Targeting these cytokines with biologic drugs, such as anti-IL-17A or anti-IL-23 antibodies, has proven effective in treating psoriasis, reinforcing their central role in the disease's pathogenesis [35,36].

Biomarker screening successfully identified PI3 and LCE3D as significantly associated with psoriasis. GSEA further revealed that these biomarkers are enriched in multiple biological pathways, including cytosolic DNA sensing, focal adhesion, and oxidative phosphorylation, indicating their potential roles in the pathophysiology of psoriasis. The cytosolic DNA-sensing pathway contributes to disease progression by triggering inflammation and facilitating immune evasion [38].PI3, as a member of the serine protease inhibitor family, can inhibit neutrophil elastase. The PI3K/AKT signaling pathway is abnormally activated in psoriasis and participates in regulating the activation, proliferation, and migration of immune cells such as T cells and neutrophils [39]. This study further confirmed that PI3 is highly correlated with neutrophils, and its high expression state is consistent with the previous finding that the PI3K signaling pathway is abnormally activated in psoriasis and promotes the excessive proliferation of keratinocytes [40,41]. Additionally, this study confirmed its diagnostic value through ROC analysis (AUC >0.9), which further supports its reliability as a potential biomarker for psoriasis and lays a theoretical foundation for subsequent functional verification and clinical application. On the other hand, LCE3D, as a member of the LCE gene family, has genetic variation (e,g, the deletion of LCE3B/LCE3C) that have been confirmed to be associated with psoriasis susceptibility [42]. Abnormal expression of this gene family may lead to defects in the keratin envelope structure, increase the skin's sensitivity to external stimuli, and thereby promote disease occurrence [43]. This study found that LCE3D was significantly upregulated in psoriasis, which not only indicates its involvement in the pathogenesis of psoriasis at the genetic level (consistent with prior findings on LCE3BL/LCE3C deletion) but also suggests its important role at the transcriptional level. Sun et al.'s study showed that the gene polymorphism of LCE3D was significantly associated with psoriasis in the Mongolian population, further supporting the results of this study [44]. In summary, PI3 and LCE3D play important roles in the immune inflammation and skin barrier disruption of psoriasis. They may affect the progression of psoriasis by regulating immune cell infiltration and skin barrier function, providing new biomarkers and potential intervention targets for the early diagnosis and targeted treatment of the disease, which has important clinical significance.

PI3 may regulate focal adhesion by modulating ECM remodeling and intracellular signaling pathways [41,[45], [46], [47]]. Additionally, LCE3D dysfunction disrupts the cornified envelope, altering the mechanical properties of the skin and directly impacting focal adhesion [[44], [45], [46],48]. These findings highlight the complex interplay between inflammation, tissue remodeling, and barrier function, which collectively drive disease progression. In relation to oxidative phosphorylation, IL-17A enhances oxidative phosphorylation in keratinocytes, triggering oxidative stress and inflammatory responses. Psoriatic keratinocytes exhibit a metabolic shift from oxidative phosphorylation to glycolysis, a process regulated by CD147, which plays a significant role in excessive keratinocyte proliferation [49,50].

Immune infiltration analysis revealed significant discrepancies in the infiltration levels of specific immune cells, such as neutrophils [51,52], macrophages [53,54], and mast cells [55,56], between psoriasis and control groups. Neutrophils are central to psoriasis pathogenesis, contributing to inflammation, tissue damage, and lesion formation. Their interactions with other immune cells and molecular pathways highlight their importance, and targeting neutrophil function and NET formation could offer new therapeutic strategies for improving psoriasis outcomes [51,52]. Macrophages promote inflammation through several mechanisms: they serve as a major source of proinflammatory cytokines like TNF-α and IL-1β, which drive the inflammatory cascade and recruit immune cells; generate reactive oxygen species that induce oxidative stress, a key factor in psoriasis pathogenesis; and interact with T cells, dendritic cells, and keratinocytes. For instance, IL-23 activates Th17 cells, and TNF-α stimulates keratinocytes, amplifying the inflammatory response. Mast cells, critical in psoriasis pathogenesis, interact with a range of cell types, including T cells, Tregs, keratinocytes, and sensory neurons. Understanding the pathogenic mechanisms of these interactions could provide novel therapeutic targets for psoriasis treatment [[55], [56], [57]].

The construction of the regulatory network identified several key regulators, including FOXC1 [58,59] and MALAT1 [60,61]. Current research suggests that FOXC1 likely plays a protective role in psoriasis through multiple mechanisms: 1) Suppressing the expression of pro-inflammatory genes: FOXC1 may act as a transcriptional repressor, inhibiting the expression of genes that encode chemokines, cytokines, antimicrobial peptides (AMPs), and keratins, which contribute to the inflammatory and hyperproliferative processes in psoriasis. 2) Counteracting the effects of pro-inflammatory TFs: FOXC1 may antagonize the activity of pro-inflammatory TFs such as STAT1, STAT3, and NF-kB1, thus modulating the inflammatory response. 3) Maintaining skin homeostasis: By regulating genes involved in keratinocyte differentiation and proliferation, FOXC1 may help sustain normal skin architecture and function [58,59]. On the other hand, MALAT1 plays a multifaceted role in psoriasis pathogenesis. It regulates immune cell differentiation, interacts with miRNAs, and modulates inflammatory pathways, making it a significant contributor to the disease. The upregulation of MALAT1 in psoriatic lesions and its dysregulated interaction with miRNA-9 suggest its potential as both a biomarker and a therapeutic target. Future research should focus on elucidating the exact mechanisms through which MALAT1 influences psoriasis development and exploring the potential of targeting MALAT1 for therapeutic interventions [60,61].

Among the 18 potential compounds predicted based on biomarkers, several have been explored and their mechanisms of action are closely related to the pathogenesis of psoriasis. Sodium dodecyl sulfate (SDS), as a psoriasis model stimulant, can simulate key features of the disease and reveal the complex relationship between environmental stimuli and skin immune responses by acting on keratinocytes [62,63], providing a reference for understanding the pathogenesis of psoriasis and developing treatment strategies. Although cadmium chloride does not directly cause psoriasis-related gene mutations, it may be involved in the disease process through indirect mechanisms such as regulating the immune system and disrupting the skin barrier [64], and cadmium exposure may exacerbate psoriasis through immune system disorders [65]. All-trans retinoic acid (ATRA), a commonly used clinical drug for psoriasis, improves disease symptoms by regulating the proliferation and differentiation of keratinocytes [66,67]. 2-Methyl-4-isothiazolin-3-one (an isothiazolinone preservative), which can react with biomolecules to cause contact dermatitis, may aggravate skin inflammation in psoriasis patients [68]. Bisphenol A, as an endocrine disruptor, can interfere with estrogen, androgen, and thyroid pathways [69]. And since the estrogen signaling pathway is related to the pathological mechanism of psoriasis [70], it may affect disease development by disrupting this pathway. In addition, environmental pollutants such as particulate matter and polycyclic aromatic hydrocarbons (PAHs) in tobacco smoke can promote the occurrence and development of psoriasis by inducing oxidative stress and systemic inflammation [71,72]; especially, childhood tobacco exposure is significantly associated with the risk of psoriasis [73].

In this study, bioinformatics methods were employed to identify PI3 and LCE3D as candidate biomarkers associated with cellular pyroptosis and hypoxia in psoriasis. A nomogram model with good predictive performance was further constructed based on biomarkers. Clinicians can output the probability of disease risk based on the expression levels of PI3 and LCE3D in patients, which is helpful for risk stratification and the formulation of more targeted treatment strategies. Additionally, the underlying mechanisms and related signaling pathways involving immune cells were explored, providing new insights for psoriasis treatment. However, this study has some limitations. Firstly, this study is mainly based on public datasets. Bias in datasets and the quality bias of GEO data may affect data consistency and thereby interfere with the stability of the results. Secondly, although we conducted a preliminary validation via RT-qPCR, the sample size was small and the population coverage was limite, which restricted the universality and robustness of the conclusion. At the same time, the lack of functional experimental verification makes it difficult to deeply reveal the specific mechanisms of PI3 and LCE3D in psoriasis. Moreover, the clinical phenotypes and genetic backgrounds of psoriasis are complex and diverse, and the current research results are difficult to be comprehensively applied to all patient groups. Therefore, in the future, we plan to expand the sample size, cooperate with more medical institutions, and include more representative and diverse psoriasis and control samples for validation, so as to accurately assess the potential of PI3 and LCE3D as biomarkers. We will also conduct studies on different psoriasis subtypes and populations to evaluate the universality and stability of the results. On the other hand, we will combine immunohistochemistry, Western blot and other techniques to further verify their expression patterns at the protein level, enhancing the reliability of the results. And through gene knockout, overexpression and other means, we will explore the regulatory effects of the two genes on inflammatory responses and keratinocyte functions in psoriasis-related cell models, as well as the specific pathways, thereby clarifying their molecular mechanisms and clinical transformation potential.

CRediT authorship contribution statement

Jing Cui: Data curation, Conceptualization, Formal analysis, Methodology, Validation, Writing – original draft, Visualization. Nianyi Zhang: Data curation, Investigation, Funding acquisition, Software. Liuyi Yang: Data curation, Investigation, Resources. Xiaoping Shen: Data curation, Investigation, Resources. Ming Ni: Conceptualization, Writing – review & editing, Project administration, Validation, Supervision

Funding

This work was supported by the Natural Science Foundation of Guizhou Province (Qian Ke He Foundation- ZK[2022] No.416), Medical Research Uni on Fund for High-quality health development of Guizhou Province (2024GZYXKYJJXM0091), National Natural Science Foundation of China (Nos. 82160046).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Not applicable.

Footnotes

^{Appendix A}

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbrep.2025.102314.

Abbreviations

PRGs, pyroptosis-related genes; HRGs, hypoxia-related genes; DEGs, differentially expressed genes; ROC, receiver operating characteristic; RT-qPCR, real-time reverse transcriptase-polymerase chain reaction; AUC, area under the curve; WHO, World Health Organization; HIFs, Hypoxia-inducible factors; FC, FoldChange; WGCNA, Weighted gene co-expression network analysis; ME, module eigengene; GS, gene significance; MM, module membership; PPI, protein-protein interaction; GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; LASSO, least absolute shrinkage and selection operator; SVM - RFE, support vector machine-recursive feature elimination; CTD, comparative toxicogenomics database; CIC, clinical impact curve; DCA, decision curve analysis; GSEA, Gene set enrichment analysis; TFs, transcription factors; lncRNAs, long noncoding RNAs; SDS, Sodium dodecyl sulfate; GEO, Gene Expression Omnibus.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1

mmc1.docx^{(13.3KB, docx)}

Supplementary Figure 1Sample Clustering and Trait Heatmap. The dendrogram plotted by hierarchical clustering of samples based on their gene expression profiles. The heatmap presented below the dendrogram indicates the expression levels of two gene groups: PRGs and HRGs. The color intensity reflects the expression levels, with darker shades indicating higher expression.

Supplementary Figure 2Hub genes are determined by module membership (MM) and gene significance (GS). GS represents the correlation between a gene and a trait. MM represents the correlation between an individual gene and the module eigengene. GS is plotted on the y-axis, MM is plotted on the x-axis, and each point represents an individual gene within each module. The red lines represented the thresholds of MM > 0.8 and GS > 0.2 set for hub genes and separated an area in the upper right corner.

mmc2.pdf^{(399.7KB, pdf)}

Data availability

The datasets analyzed in this study are available in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), including GSE13355 and GSE14905 dataset.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1

mmc1.docx^{(13.3KB, docx)}

mmc2.pdf^{(399.7KB, pdf)}

Data Availability Statement

The datasets analyzed in this study are available in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), including GSE13355 and GSE14905 dataset.

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PERMALINK

Genetic analysis of potential biomarkers and therapeutic targets in hypoxia and pyroptosis from psoriasis

Jing Cui

Nianyi Zhang

Liuyi Yang

Xiaoping Shen

Ming Ni

Abstract

Background

Methods

Results

Conclusion

Highlights

1. Introduction

2. Materials and methods

2.1. Data collection

2.2. Differential expression analysis

2.3. Weighted gene co-expression network analysis (WGCNA)

2.4. Identification and analysis of candidate genes

2.5. Identification of biomarkers

2.6. Construction of nomogram

2.7. Gene set enrichment analysis (GSEA) and GeneMANIA analysis

2.8. Immune microenvironment analysis

2.9. Regulatory network analysis and drug prediction

2.10. Expression analysis of prognosis genes

2.11. Statistical analysis

3. Results

3.1. DEGs and key module genes associated with psoriasis were selected

Fig. 1.

3.2. Candidate genes and corresponding pathways

Fig. 2.

3.3. PI3 and LCE3D were selected as biomarkers in psoriasis

Fig. 3.

3.4. Nomogram well predicted the risk of psoriasis

Fig. 4.

3.5. Common pathways enriched by biomarkers

Fig. 5.

3.6. Biomarkers closely correlated with immune cells

Fig. 6.

3.7. Regulatory mechanism was explored by biomarkers

Fig. 7.

3.8. Biomarkers had higher expression in psoriasis

Fig. 8.

4. Discussion

CRediT authorship contribution statement

Funding

Declaration of competing interest

Acknowledgements

Footnotes

Abbreviations

Appendix A. Supplementary data

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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