ABSTRACT Psoriasis is a prevalent inflammatory skin disorder with immune-related mechanisms that remain incompletely understood. To elucidate the immune landscape of psoriasis, we analyzed

expression profiles to identify 115 psoriasis susceptibility genes (PSGs) and subsequently pinpointing eight immune-related hub genes (IRHGs). A predictive model incorporating these IRHGs

demonstrated promising prognostic potential for psoriasis. Additionally, extensive intercellular communication was observed among keratinocytes, dendritic cells, monocytes, and T cells. The

cellular differentiation trajectory revealed a complex interplay among various cell types and states, highlighting genes such as _CXCL8_, _CCL2_, _STAT3_, and _STAT1_ emerging as closely

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Open access 07 October 2023 IDENTIFICATION OF NOVEL IL17-RELATED GENES AS PROGNOSTIC AND THERAPEUTIC BIOMARKERS OF PSORIASIS USING COMPREHENSIVE BIOINFORMATICS ANALYSIS AND MACHINE LEARNING

Article Open access 02 April 2025 INTRODUCTION Psoriasis, a chronic autoimmune disorder, predominantly targets the skin1, stemming primarily from immune system dysregulation that disrupts

some biological equilibria2. In the context of psoriasis, immune cells, particularly T cells, along with other pivotal inflammatory players such as macrophages and dendritic cells, undergo

heightened activation. This abnormal activation triggers a cascade of inflammatory reactions that contribute significantly to the development and perpetuation of the disease3,4,5. The skin

of psoriasis patients is characterized by hyperproliferation of keratinocytes. The life cycle of these cells is shortened, resulting in the accumulation of scaly, immature cells on the

skin’s surface6,7. Additionally, the disease pathology is marked by an amplified inflammatory response and a pervasive immune imbalance8. Various factors interact to exacerbate the skin

symptoms and the inflammatory progression of psoriasis, highlighting the need for specific immune-related biomarkers that can improve our understanding of the disease and aid in its

management. Therefore, identifying these biomarkers is crucial for advancing both our knowledge and the clinical management of psoriasis. Our aim is to elucidate the immune landscape of

psoriasis by analyzing transcriptomic data from psoriatic samples (Fig. 1). We identified 115 psoriasis susceptibility genes (PSGs) and further condensed these to eight immune-related hub

genes (IRHGs) to construct a prognostic model for psoriasis. By investigating the roles of these IRHGs and their interactions with various immune cells, we seek to provide new insights into

potential biomarkers for psoriasis. MATERIALS AND METHODS DATA SOURCES Psoriatic bulk transcriptomic sequencing datasets were downloaded from the GEO database

(https://www.ncbi.nlm.nih.gov/geo/), encompassing GSE30999 and GSE201827. The datasets include 217 psoriatic samples and 102 normal samples. We employed the combat package in R to remove

batch effects from all expression profiles. Psoriatic trait information from GSE309999 and GSE20182710 were utilized to correlate gene expression with traits. In addition, psoriasis

single-cell transcriptomic sequencing dataset GSE15117711, were downloaded from the GEO database, comprising 13 psoriatic samples and 5 normal samples. Furthermore, the atopic dermatitis

(AD) dataset GSE174582, including 30 disease samples and 4 controls, was also obtained. Detailed information regarding all gene expression profiles is delineated in Supplementary Table 1.

IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN PSORIASIS The “limma” package in R was utilized to identify differentially expressed genes. The screening for differentially expressed

genes was conducted using thresholds of |log2 fold change (FC)| > 1 and _p_ < 0.05, which were then visualized using volcano plots. WGCNA IDENTIFIED KEY MODULES ASSOCIATED WITH

PSORIASIS The gene networks were constructed using the WGCNA package in R. To ascertain the optimal soft threshold for constructing the adjacency matrix, the scale-free topology criterion

was employed, and the “pickSoftThreshold” function was used to select the soft threshold. Through this process, it was determined that the optimal soft threshold β is 14. Next, closely

interconnected genes were clustered, module detection was performed using an unsupervised clustering method, and the correlation between modules and genes was calculated. For further

validation, module genes with the strongest correlations with psoriasis clinical traits (baseline psoriatic skin lesions) were selected for subsequent analysis. FUNCTIONAL ENRICHMENT

ANALYSIS To explore the biological functional categories and potential mechanisms of psoriasis-causing genes, Gene Ontology (GO)12 and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses

were conducted13,14,15. IDENTIFICATION AND MODULAR ANALYSIS OF IRHGS The R package 'venn' was used to perform a cross-analysis of the psoriasis-related genes identified by the two

methods mentioned above, and the set of psoriasis-associated genes was retrieved from GeneCards (https://www.genecards.org/) to identify the common psoriasis-causing genes. PPI networks

from STRING database (https://cn.string-db.org/)16 and Cytoscape software (version 3.8.1)17 were used to explore the physical interactions between proteins but also their functional

associations. Five distinct topological analysis methods, namely Degree, EPC, MNC, Radiality, and Closeness, were employed to filter out pivotal genes of importance, with the top 10 genes

with the highest scores selected from each method. The intersection of the candidate genes obtained from these five algorithms was then taken. Lastly, these candidate genes were analyzed in

comparison with known immune genes, leading to the identification of IRHGs. CONSTRUCTION AND EVALUATION OF A NOMOGRAM FOR PREDICTIVE MODELING OF DIAGNOSTIC MARKERS FOR IRHGS Receiver

operating characteristic curve (ROC) analysis was conducted to evaluate the predictive efficacy of each of the IRHGs genes, and area under the curve (AUC) values were calculated using the

“pROC” package. IRHGs with an AUC > 0.8 were considered to possess high potential for psoriasis diagnosis. Utilizing these eight core genes, the “rms” package in R software was employed

to construct a nomogram for evaluating each pivotal gene and their performance in psoriasis diagnosis. Calibration curve, Decision Curve Analysis (DCA), and clinical impact curve analyses

were performed to assess the efficacy of the nomogram in psoriasis prediction. EVALUATION OF IMMUNE CELL INFILTRATION AND ITS CORRELATION WITH IRHGS The relationship between immune

infiltrating cells and key biomarkers was investigated based on Spearman correlation using the CIBERSORT tool. The R package “corrplot” was used to visualize the expression matrix of

Spearman correlation coefficients among 22 immune cells in the psoriasis dataset. In addition, the correlation between the expression of IRHGs and immune cell infiltration was calculated

using the R packages “reshape2”, “ggpubr”, “ggExtra”, and “ggstatsplot”, and visualized with the “ggplot2” package. DRUG SENSITIVITY ANALYSIS OF CANDIDATE MARKERS IRHGs were searched against

the Connectivity Map database (https://clue.io) to identify potential small molecule drugs for the treatment of psoriasis. Ultimately, the top 10 compounds with the highest enrichment

scores were identified. SINGLE-CELL ANALYSIS We used the “Seurat” package to process single-cell RNA sequencing data. First, cells were filtered with a threshold of min.cells = 3 and

min.features = 250, requiring the number of expressed genes detected per cell be between > 500 and < 6000. At the same time, we excluded the top 3% of cells with the highest nCount_RNA

expression and the top 2% of cells with the highest mitochondrial gene content. For quality control, we also removed the smallest 1% and the largest 1% of cells based on the proportion of

rRNA expression to the total genes in each cell. Upon completing data preprocessing, the data were normalized using the LogNormalize method. Subsequently, unsupervised clustering was

performed, encompassing principal component analysis (PCA) and UMAP analysis18, enabling the visualization of different cell populations’ distribution on a 2D map. Next, cellular annotation

was carried out using the “SingleR” package19, with reference data sourced from the Human Primary Cell Atlas. For the identification of marker genes for each cell population, the

“FindAllMarkers” function was utilized with a fold change (FC) ≥ 1 set as the criterion. This strategy was also employed to screen for differentially expressed genes across different cell

populations. Subsequently, the identified marker genes were visualized using the VlnPlot and FeaturePlot functions. Finally, the Monocle software package was utilized to perform machine

learning based on the expression patterns of key genes to simulate dynamic changes during temporal development. The results of cell trajectories should be based on the distribution of

trajectories by cell type as well as changes in the expression of characterized genes to confirm the start and end points of differentiation. Upon identifying the cell types in psoriasis,

CellChat (version 1.1.3) was employed to analyze intercellular communication. RNA EXTRACTION AND QRT-PCR Total RNA was extracted from the blood samples of one healthy individual and one

psoriasis patient using the EasyPure Blood RNA Kit (TransGen Biotech). The extracted RNA was then reverse transcribed into cDNA using the RNA Reverse Transcription Kit (PCR Kit AMV ver.

3.0). Real-time quantitative PCR (RT-qPCR) analysis was performed on the synthesized cDNA using the Roche fluorescence quantitative kit (FastStart Universal SYBR Green Master). Each sample

was subjected to three biological replicates. The 2−ΔΔCT method was used to calculate and analyze the mRNA expression levels of C-X-C Motif Chemokine Ligand 1 (_CXCL1_), C-X-C Motif

Chemokine Ligand 8 (_CXCL8_), C-X-C Motif Chemokine Ligand 10 (_CXCL10_), C-C Motif Chemokine Ligand 2 (_CCL2_), Toll Like Receptor 2 (_TLR2_), Signal Transducer And Activator Of

Transcription 1 (_STAT1_), Signal Transducer And Activator Of Transcription 3 (_STAT3_), and Interleukin 1 Beta (_IL1β_), using _GAPDH_ as the internal control gene. The sequences of the

relevant primers can be found in Supplementary Table 2. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee from The First Affiliated

Hospital of Shihezi University, China (Approval Number: KJ2024-293-02). Informed consent was obtained from all subjects involved in the study. RESULT IDENTIFICATION OF GENES ASSOCIATED WITH

PSORIASIS PROGRESSION By comparing transcriptomic data from psoriasis samples with control samples, a total of 1,673 differentially expressed genes were identified. Of these, 680 genes

exhibited up-regulation in expression, while 993 genes were down-regulated (Fig. 2a). The down-regulated genes, such as _S100A12_, _TMPRSS11D_ and _KYNU_, are mainly involved in

cytokine-cytokine receptor interactions, NOD-like receptor signaling, and IL-17 signaling pathways (Supplementary Fig. 1a). Conversely, up-regulated genes, such as _BCAR3_, _JADE1_, and

_KRT77_, are mainly involved in the AMPK, Wnt, and PPAR signaling pathways (Supplementary Fig. 1b). In the gene network of psoriasis samples, we identified 18 expression modules with module

sizes ranging from 118 to 2,659 genes. For description and analysis, these modules were labeled with different colors (Fig. 2b, c). Some network modules may play a key role in the biological

function of psoriasis. For example, gene expression in the blue module was highly correlated with the baseline of patient skin lesions (Spearman _r_ = 0.73). Genes in this module are

predominantly associated with biological pathways involved in immune regulation and inflammatory response (Supplementary Fig. 1c). The set of differentially expressed between the psoriasis

group and the control group overlapped with the set of network module genes. For example, there is a set of genes related to immune regulation in the blue network module, and some genes

related to immune response in the differential gene set. By combining the genes known to be associated with psoriasis in GeneCards, the differentially expressed genes, and the modular genes,

we discerned 115 psoriasis susceptibility genes (PSGs) (Fig. 2d). FUNCTIONAL ENRICHMENT AND MODULAR ANALYSIS OF PSGS PSGs are primarily involved in a variety of inflammatory responses and

immune pathways, encompassing various biological processes such as responses to lipopolysaccharides, cytokine-mediated signaling, granulocyte chemotaxis, and keratinocyte envelope formation.

Additionally, these genes play roles in molecular functions related to cytokine activity, receptor-ligand interactions, and signaling receptor activation. Furthermore, they were associated

with multiple KEGG pathways, including cytokine-cytokine receptor interactions, the IL-17 signaling pathway, and the NOD-like receptor signaling pathway (Fig. 3a, b). These findings imply an

important role of immune activation and inflammatory pathways in psoriasis progression. In addition, we calculated the Betweenness centrality index in the gene network of the psoriasis

samples and found higher centrality of _IL1β_ in the PSGs (Supplementary Fig. 2). _IL1β_, a known cytokine, induces Th17 cell production and promotes keratinocyte chemokine secretion20. Its

expression is regulated by the nucleotide-binding oligomerization structural domain-like receptor 3 (NLRP3) inflammasome21, suggesting that _IL1β_ may play a key role in immunomodulation in

psoriasis. IDENTIFICATION OF IRHGS Some PSGs in the gene network may play an important role in the development of psoriasis. To explore the role of PSGs in the network, we assessed the

network significance of PSGs using five hub metrics (Degree, EPC, MNC, Radiality and Closeness). Among PSGs, eight genes (_CCL2_,_ CXCL1_,_ CXCL10_,_ CXCL8_,_ IL1β_,_ STAT1_,_ STAT3_, and

_TLR2_) showed high node centrality across these metrics (Fig. 3c), implying that these PSGs occupy a critical position in the network and may be core targets in psoriasis. Upon searching

the Immunology Database, the above eight PSGs were also identified as immune genes. These hub PSGs are thus designated as immune-related hub genes (IRHGs) associated with psoriasis (Fig. 

3d). EVALUATION OF THE DIAGNOSTIC EFFECTIVENESS OF IRHGS We constructed a predictive model for psoriasis diagnosis using IRHGs as biomarkers and evaluated the efficacy of IRHGs in psoriasis

diagnosis using ROC curves. In the psoriasis dataset (GSE30999), the AUCs of the eight IRHGs (_CCL2_,_ CXCL1_,_ CXCL10_,_ CXCL8_,_ IL1β_,_ STAT1_,_ STAT3_, and _TLR2_) all surpassed 0.8

(Fig. 4a). Moreover, in another independent dataset (GSE201827), the AUC values for all eight IRHGs were all greater than 0.8 (Fig. 4b), indicating that these IRHGs can effectively

distinguish psoriatic lesional tissue from healthy skin tissue. To further assess their specificity, we analyzed these IRHGs in psoriasis and atopic dermatitis samples (GSE174582). Six genes

(_CCL2_,_ CXCL1_,_ CXCL10_,_ CXCL8_,_ STAT1_, and _STAT3_) showed AUC values greater than 0.8, while _TLR2_ and _IL1β_ demonstrated lower discriminative power (AUC = 0.615 and 0.658,

respectively; Supplementary Fig. 3). These results suggest that the six IRHGs (_CCL2_,_ CXCL1_,_ CXCL10_,_ CXCL8_,_ STAT1_, and _STAT3_) may serve as psoriasis-specific biomarkers. In

addition, IRHGs exhibited varying levels of expression across different samples of psoriasis. Specifically, IRHGs were significantly up-regulated in skin lesion samples and down-regulation

after 12 and 52 weeks of treatment with Secukinumab, suggesting that Secukinumab exerted an inhibitory effect on the expression of IRHGs during the treatment of psoriasis (Fig. 4c). We

utilized a logistic regression model encompassing IRHGs, assigning each gene a score (Points in Fig. 5a) based on its contribution to the outcome variable. Subsequently, the scores of the

genes were aggregated to yield the total score (Total Points), from which the risk probability of disease (Risk of Disease) were derived. The outcomes are depicted in the form of a nomogram

(Fig. 5a), and a calibration curve was used to assess the predictive efficacy of the model. The calibration curve displays an absolute error of 0.023 between actual and predicted risk,

indicating that the logistic regression model is both stable and accurate (Fig. 5b). The predictive model, using IRHGs as predictive markers, is rational for disease occurrence probabilities

between 0 and 0.6. It offers the highest net benefit compared to a strategy that assumes all patients are either positive or negative, with net benefit reflecting the balance between true

positives and false positives. However, for disease probabilities greater than 0.6, the net benefit rapidly decreases, approaching the net benefit of a strategy that treats all patients as

negative (‘None’) (Fig. 5c). In addition, as the high-risk threshold increased, the number of false positives predicted by the model progressively decreased. Both the number of individuals

categorized as high risk (represented by the red solid line in Fig. 5d) and the number of high-risk individuals who experienced true events (represented by the blue dashed line in Fig. 5d)

gradually declined. However, the rates of these reductions converged over time. This convergence suggests that the model’s interpretation positivity rate closely aligns with the true

positivity rate at higher risk thresholds. IDENTIFICATION OF IMMUNE CELL INFILTRATION IN PSORIASIS AND ITS CORRELATION WITH IRHGS To explore the immune landscape of psoriasis, we assessed

the proportions of 22 immune cell types in both control and psoriasis groups. We found significantly higher proportions of CD4 memory T cells, helper T cells, activated dendritic cells, and

M1-type macrophages in psoriasis samples compared to controls (Fig. 6a). We further evaluated the relationship between IRHGs and immune cells. In the psoriasis dataset, IRHGs were strongly

correlated with helper T cells, activated dendritic cells, activated CD4 memory T cells, M1 macrophages, neutrophils, γδ T cells, activated mast cells, and eosinophils. In psoriasis samples,

the expression levels of IRHGs were positively correlated with the infiltration of helper T cells, activated dendritic cells, activated CD4 memory T cells, and M1 macrophages; and

negatively correlated with the infiltration of regulatory T cells and quiescent mast cells (Fig. 6b). IDENTIFICATION OF POTENTIAL SMALL MOLECULE COMPOUNDS FOR PSORIASIS TREATMENT To explore

small molecule drugs with potential therapeutic effects for psoriasis, we searched compounds in the Connectivity Map (CMap) database. Some compounds were found to induce the expression

pattern of IRHGs in the disease state. By potentially altering the expression patterns of target genes, these compounds may hold therapeutic potential or contribute to the amelioration of

psoriasis. The top 10 compounds identified for their pronounced interference effects include triamcinolone, GDC-0941, selumetinib, MDM2-inhibitor, avrainvillamide-analog-5, tipifarnib,

FG-7142, VEGF-receptor-2-kinase-inhibitor-IV, TPCA-1, and benzanthron (Supplementary Fig. 4a, c). Among them, VEGF-receptor-2-kinase-inhibitor-IV is a VEGFR inhibitor (Supplementary Fig. 

4b). Levels of VEGF and its receptors are known to correlate with disease severity in the plasma and skin of patients with psoriasis. Furthermore, topical or systemic treatment in psoriasis

patients leads to a reduction VEGF levels and vascular proliferation22,23. Thus, VEGF-receptor-2-kinase-inhibitor-IV may be a potential small molecule compound for psoriasis treatment.

IDENTIFICATION OF PSORIASIS CELL SUBTYPES We observed variations in the expression levels of IRHGs across different psoriatic cell subpopulations, potentially linking to their underlying

pathological mechanisms. Firstly, we filtered 4,789 single-cell transcriptome data from two sets of psoriasis samples and control samples, considering a range of criteria including the count

of genes, the number of UMIs in each cell, the percentage of mitochondrial content, and the proportion of rRNA expression to the total gene count. After elimination of low-quality and dead

cells, we obtained transcriptome data for 3,644 cells (Supplementary Fig. 5a). Subsequently, we conducted a variance analysis on the gene abundance in these transcriptome data, identifying

the top 2,000 genes with the most significant fluctuations (Supplementary Fig. 5b). We discovered that the top 20 principal components could distinguish these single-cell samples

(Supplementary Fig. 5c). Following this, we performed a cluster analysis of the core cells, categorizing them into six distinct cell clusters and identifying the top five important marker

genes for each cell type (Supplementary Fig. 6). We then annotated these cell clusters, identifying six cell groups including keratinocytes, T cells, dendritic cells, and monocytes. Notably,

T cells, dendritic cells, monocytes, and IL-22-expressing keratinocytes were present in high numbers in the psoriasis samples (Fig. 7a, b), implying a significant role of these cell

populations in the pathogenesis of psoriasis. In addition, we explored the relationship between IRHGs and the six cell populations described above. The results showed that _CXCL8_ was highly

expressed across all six cell subpopulations (Fig. 7c), suggesting that _CXCL8_ may play a role in psoriasis development within these cellular frameworks. On the other hand, _CCL2_,

identified as a marker gene, exhibited high expression exclusively in monocytes. We also observed that _STAT1_ was highly expressed in monocytes, while _STAT3_ was predominantly expressed in

IL1β-expressing keratinocytes24. Both genes also had relatively high expression levels in dendritic cells. Therefore, we propose that _STAT3_ and _STAT1_ may play roles in mediating the

inflammatory response, potentially through modulating chemokine expression that facilitates dendritic cell recruitment25,26,27. However, the expression levels of _CXCL1_,_ CXCL10_,_ IL1β_

and _TLR2_ are relatively low in these six cell subpopulations. These findings elucidate the expression patterns of IRHGs across different cell subpopulations, suggesting their potential

association with psoriasis. RECONSTRUCTION OF DIFFERENTIATION TRAJECTORIES FOR PSORIASIS-ASSOCIATED CELL SUBPOPULATIONS AND ANALYSIS OF CELLULAR COMMUNICATION Utilizing psoriasis-associated

keratinocytes and T cells, we constructed differentiation trajectories to analyze immune cell heterogeneity among patients (Fig. 8). In this trajectory, keratinocytes, and T cells occupy

different branches, each resenting different states of differentiation. Further analysis revealed that effector memory CD4 + T cells progressed toward an IL-22-expressing keratinocyte state

before differentiation (bifurcation) occurred. IL-22, produced by CD4 + T cells, is an important downstream cytokine of IL-23. Its receptor IL-22R can be expressed in non-hematopoietic cells

like keratinocytes and epithelial cells28. Shortly after forming the IL-22-expressing keratinocyte state, the cells bifurcated into two distinct branches, representing the two main cell

lineages in the later stages of reprogramming (Fig. 8). Furthermore, based on the distribution of various cell types under different states, we found that the cell types in State 1 primarily

included IL-22-expressing keratinocytes, T cells, and a small proportion of monocytes. In State 2, the predominant cell types were IL1β-expressing keratinocytes, T cells, and a few

IFN-γ-expressing keratinocytes. State 3 mainly comprised T cells, IFN- γ-expressing Keratinocytes, and a small number of monocytes. In addition, we observed that _CCL2_,_ CXCL8_,_ STAT3_,

and _STAT1_ are differentially expressed from State 1 to State 3, suggesting that these genes may serve as markers during the differentiation trajectories of these cells (Supplementary Fig. 

7). We conducted intercellular communication network analysis to predict interactions among six cell subpopulations based on specific ligand receptors. For example, by comparing paired

receptors between different cells, we identified interactions between monocytes and keratinocytes expressing IL-22 and IFN-γ (Fig. 9b). In addition, based on differences in the strength of

ligand receptor action, we observed a more prominent interaction between dendritic cells and IL-22-expressing keratinocytes. However, there is less interaction between IL1β-expressing

keratinocytes and other cells (Fig. 9a). These results are consistent with previous studies and underscore the importance of interactions between keratinocytes and other immune cells in the

development of psoriatic lesions26. Additionally, we observed several signaling pathways between these six cell subpopulations, including MIF, CD99, CLEC and CCL signaling pathways. In the

MIF signaling pathway, we identified two ligand-receptor pairs with significant contributions: MIF-(CD74 + CD44) and MIF-(CD74 + CXCR4) (Fig. 9c). Monocytes secrete MIF, which binds to its

receptor CD74 on macrophages, leading to the recruitment of inflammatory factors29,30,31. This interaction results in substantial expression of inflammatory factors such as TNF-γ and IL-22

by keratinocytes in patients with psoriasis, contributing to the dysregulation of the immune homeostasis and the inflammatory response (Fig. 9d, e). QRT-PCR VERIFICATION OF IRHG EXPRESSION

To preliminarily validate the expression levels of IRHGs, we performed qRT-PCR analysis on the control and psoriasis groups (Fig. 10). The results indicated that the expression levels of

_CXCL1_,_ CXCL8_,_ CXCL10_,_ IL1β_,_ STAT1_, and _STAT3_ were higher in the psoriasis group compared to the control group, which is consistent with the results from the previous analysis

(Fig. 4c). DISCUSSION IRHGS INTERACT WITH IMMUNE CELLS TO PROMOTE PSORIASIS DEVELOPMENT Psoriasis is a chronic autoimmune skin disease characterized by immune system dysregulation leading to

abnormal biological responses1,2. During this process, both T cells and inflammatory cells exhibit enhanced activity, releasing inflammatory mediators such as tumor necrosis factor-α and

interferon-γ. These mediators, in turn, stimulate abnormal skin cell growth and inflammatory responses32. These interactions drive the progression of the skin symptoms and inflammation in

psoriasis. Despite the key role of the immune system in the disease, research on immune-related biomarkers in psoriasis remains limited. In-depth studies of these markers and their

relationship with immune cells are crucial for diagnosis, treatment, disease assessment, individualized therapy, and clinical trial design. Such research will offer valuable insight into

psoriasis research and treatment. In the present study, we employed a fusion of differential expression analysis and WGCNA to identify psoriasis immune-related biomarkers. This approach

identified 115 psoriasis susceptibility genes (PSGs) and subsequently aggregated them into eight immune-related hub genes (IRHGs) within the network. Functional analysis revealed that PSGs

are enriched in cytokine-cytokine receptor interactions and the IL-17 signaling pathway, both of which may significantly correlate with the development of psoriasis. Past research has

demonstrated that IL-17 plays a key role in psoriasis by triggering the secretion of three different classes of cytokines, each contributing to various stages of the disease33. Initially,

IL-17 C and IL-36, as class I cytokines, enhance tissue inflammation, thereby advancing the pathological process of psoriasis34. Subsequently, induced jointly by TNF-α and IL-17, has the

potential to directly cause structural and functional changes in the psoriatic epidermis, thereby intensifying disease severity35. Finally, IL-19, a member of the third class of cytokines,

amplifies the effects of IL-17 through positive feedback regulation, further facilitating the progression of psoriasis36. After identifying a set of PSGs, we aggregated eight IRHGs within

network: _CCL2_, _CXCL1_, _CXCL10_, _CXCL8_, _IL1β_, _STAT1_, _STAT3_, and _TLR2_. Using these IRHGs, we constructed a predictive model which exhibited strong performance in ROC curve

analysis. The verification through ROC analysis and the expression evaluations confirmed the high prognostic reliability of these IRHGs. Further examinations, using a column-line graphs,

calibration curves, and clinical impact curves, highlighted the prognostic significance of these IRHGs for psoriasis patients. Previously studies have reported that three of the identified

immune-related hub genes IRHGs—_CXCL1_, _CXCL8_, and _CXCL10_—are upregulated in psoriatic skin lesions37,38, with a noted positive correlation between their expression and neutrophil

infiltration. This highlights the significance of these genes in the immunopathological progression of psoriasis. Specifically, _CXCL1_ and _CXCL8_ are chemokines whose expression in

keratinocytes is induced by IL-36G, promoting neutrophil migration and contributing to the inflammation characteristic of psoriasis39. This aligns with our study outcomes, highlighting the

crucial role which these genes play in the psoriatic immune response. Moreover, _CXCL10_ functions as a chemokine that attracts immune cells like T cells, monocytes, and NK cells to sites of

inflammation. Elevated _CXCL10_ expression in psoriasis is associated with immune response and inflammation, potentially leading to an accumulation of immune cells in psoriatic skin and

thereby intensifying the inflammatory reaction40. _CCL2_ may play an important role in the pathological development of psoriasis. Our study found elevated expression levels of _CCL2_ in

psoriasis samples, which were positively correlated with CD4 + T cell infiltration. This finding aligns with other studies that have observed increased _CCL2_ expression in the epidermal

tissues of psoriasis patients41. Such evidence underscores the importance of _CCL2_ in the immunopathological dynamics of psoriasis, particularly its association with CD4 + T cell

infiltration. The interplay between _STAT3_ and _STAT1_ may be crucial in the pathogenesis and progression of psoriasis. In this study, we observed significantly elevated expression levels

of both _STAT3_ and _STAT1_ in psoriasis samples, consistent with previous research showing increased activation of transcription factor _STAT3_ in psoriatic skin lesions42. Interestingly,

when _STAT1_ expression was subdued, it disrupted the balance between _STAT1_ and _STAT2_ activation, shifting the activation state of IL-27 from inhibitory to one that promotes IL-17 A

expression43. This suggests that heightened _STAT3_ activation may enhance pro-inflammatory effects of IL-27 within specific psoriatic immune microenvironments, particularly when the

_STAT3_/_STAT1_ activation ratio is elevated. Toll-like receptor 2 (_TLR2_) may play an important role in the innate immune response. In our investigation, we observed a positive correlation

between _TLR2_ expression and dendritic cell infiltration through immune-infiltration analysis. The _TLR2-_mediated signaling pathway promotes IL-10 production, which is induced by

dendritic cells and regulatory T cells44. Furthermore, _TLR2_ supports the proliferation of regulatory T cells while reducing the release of inflammatory cytokines, thereby significantly

alleviating imiquimod-induced skin inflammation in psoriasis45. _IL-1β_ plays multiple roles in the context of psoriasis. In our investigation, we observed an elevated expression of _IL-1β_

in psoriasis samples, which positively correlated with the infiltration of various T-cell types. _IL-1β_ is a potent inflammatory mediator that initiates and amplifies inflammatory

responses. Particularly in psoriasis patients, _IL-1β_ levels increase, especially within lesion areas20,46. _IL-1β_ activates inflammatory cells and triggers the release of inflammatory

mediators through engagement with its receptor, inducing a localized inflammatory cascade46. Additionally, _IL-1β_ orchestrates the activity of immune cells, including T cells and

inflammatory cells, and plays a crucial role in the migration and aggregation—key aspects in the development of psoriatic skin lesions47. _IL-1β_ often collaborates with other inflammatory

agents, such as tumor necrosis factor-α (TNF-α), to further drive inflammatory reactions and lesion formation, potentially exacerbating the inflammatory condition in psoriatic skin. COMBINED

ANALYSIS OF IRHGS AND PSORIASIS-ASSOCIATED CELL SUBSETS Through psoriatic single-cell RNA sequencing (scRNA-seq) profiling, we identified six cell types including keratinocytes, dendritic

cells, monocytes, and T cells, which frequently engage in mutual communication. We discovered variations in the expression levels of IRHGs across cell subpopulations, potentially linked to

their underlying pathological mechanisms. _CXCL8_ exhibited expression across all six cell types, albeit to varying degrees, with a notable expression in keratinocytes. Previous research

indicated that _CXCL8_, predominantly produced by keratinocytes, shows heightened expression in psoriatic lesion skin40. It induces skin inflammation through interaction with the CXCR1

receptor, guiding neutrophil infiltration into the epidermis. Additionally, while granulocytes and T lymphocytes are dominant players in psoriasis progression, monocytes also hold a crucial

role in skin inflammation. _CCL2_ is prominently expressed in these cells, serving as a marker gene for monocytes. Research has demonstrated that this molecule coordinates the recruitment of

monocytes to inflammation sites via the CCL2/CCR2 signaling pathway, initiating a cascade of inflammatory responses48,49. This cascade contributes to the exacerbation of psoriasis, aligning

with the observations in our study. Macrophage migration inhibitory factor (MIF) drives the pathological progression of psoriasis through cross-cell subgroup signaling networks, with

mechanisms involving immune regulation, epidermal homeostasis imbalance, and other factors. Studies have found that the abnormal activation of the MIF signaling pathway in monocytes,

dendritic cells, and keratinocytes is closely associated with immune dysregulation in psoriasis. Specifically, MIF secreted by T lymphocytes and monocytes targets CD74 receptors and

chemokine receptors CXCR2/CXCR450, promoting the migration and infiltration of neutrophils and monocytes to the epidermal-dermal junction, thereby participating in the remodeling of the

inflammatory microenvironment. Additionally, the binding of MIF to membrane receptor complexes (such as CXCR4/CD74 and CD74/CD44) activates the nuclear factor-kappa B (NF-κB) signaling

pathway30,51, which induces the excessive secretion of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), further promoting the differentiation

of T-helper 17 (Th17) cells and the production of interleukin-17 A (IL-17 A), thereby exacerbating the abnormal activation of the IL-23/Th17 immune axis. In keratinocytes31,52, MIF activates

extracellular signal-regulated kinase (ERK) and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathways53, stimulating abnormal cell proliferation and inhibiting apoptosis, leading

to histopathological changes such as acanthosis and hyperkeratosis. Our findings are consistent with these earlier studies, further validating the important role of MIF in regulating immune

and inflammatory responses across different cell subgroups. This study integrated single-cell transcriptomic data with pseudotime analysis to elucidate the dynamic interaction mechanisms

between immune cells and keratinocytes during the pathogenesis of psoriasis. Specifically, CD4 + effector memory T cells follow a clear differentiation trajectory along pseudotime: from a

resting state (State1), through an intermediate state (State2), to a terminal activation state (State3). This differentiation process is accompanied by characteristic gene expression

changes: early (State1→2), _STAT1_ is significantly upregulated, driving Th1-type immune responses; while in the late stage (State3), _STAT3_ maintains high expression, promoting the

activation of the Th17 pathway54. Keratinocytes show dynamic responses to changes in the immune microenvironment, particularly under the stimulation of IFNγ and _IL-1β_, where they actively

participate in immune cell recruitment by secreting chemokines such as _CCL2_ and _CXCL8_55. Notably, _CXCL8_ exhibits explosive expression in State2, coinciding with the peak of T cell

activation; whereas the activation of _TLR2_ in State3 suggests that microbial signals may play a role in maintaining the chronic phase of psoriasis56. These findings establish a

pathological feedback loop framework of “chemokine recruitment–immune cell activation–epidermal proliferation,” providing a single-cell level theoretical basis for the development of

stage-specific targeted therapeutic strategies. This study has certain limitations. First, although preliminary testing of PBMCs showed that the expression trends of the eight IRHGs were

consistent with bioinformatics predictions, these results require validation in larger skin tissue samples to establish their reliability as psoriasis biomarkers. Second, while the current

sample size (_n_ = 1/group) does not meet statistical power requirements, it provides directional insights that can guide future research. CONCLUSION The presented study unveils eight IRHGs

and explores their prognostic relevance in psoriasis through comprehensive analysis. Employing scRNA-seq profiling, we deciphered a variety of cell subpopulations and their interactions

within the psoriatic immune microenvironment. We found that the expression profiles of these IRHGs varied across different cell subpopulations, highlighting potential ties to the underlying

pathological mechanisms of psoriasis. Extensive intercellular communication was observed among keratinocytes, dendritic cells, monocytes, and T cells. The cellular differentiation trajectory

in psoriasis revealed a complex interplay among various cell types and states. Moreover, certain genes like _CXCL8_, _CCL2_, _STAT3_, and _STAT1_ are closely intertwined with the cellular

composition and functional status within the psoriasis immune microenvironment. Through this work, we have garnered a deeper understanding of the immunopathological mechanisms of psoriasis

potentially providing valuable insight for the development of future therapeutic strategies and biomarkers for this complex skin disorder. DATA AVAILABILITY The datasets supporting the

conclusions of this articleare available in the Gene Expression Omnibus repository(https://www.ncbi.nlm.nih.gov/geo/). GSE30999:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30999

GSE201827:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201827 GSE151177:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151177

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Download references ACKNOWLEDGEMENTS We extend our gratitude to all the contributors of data utilized in this study. Our appreciation also extends to the editors and anonymous reviewers

whose insightful suggestions greatly enriched the manuscript. We would like to thank all the donors who contributed samples. FUNDING The research was supported by the National Natural

Science Foundation of China (32060145, 32060300 and 31860308). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the

manuscript. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * College of Life Science, Shihezi University, Shihezi City, Xinjiang, China Tingting Yin, Tingting Zhang & Lei Ma Authors *

Tingting Yin View author publications You can also search for this author inPubMed Google Scholar * Tingting Zhang View author publications You can also search for this author inPubMed 

Google Scholar * Lei Ma View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS TY and LM designed the study. TY led the data collection, analysis

interpretation, and manuscript writing. LM and TZ revised the manuscript and provided administrative support. All authors made significant contributions to the article and approved the

version submitted for publication. CORRESPONDING AUTHORS Correspondence to Tingting Zhang or Lei Ma. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

CONSENT FOR PUBLICATION Not applicable. INSTITUTIONAL REVIEW BOARD STATEMENT The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee from

The First Affiliated Hospital of Shihezi University, China (Approval Number: KJ2024-293-02). INFORMED CONSENT Informed consent was obtained from all subjects involved in the study.

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psoriasis pathogenesis. _Sci Rep_ 15, 17765 (2025). https://doi.org/10.1038/s41598-025-02822-1 Download citation * Received: 12 November 2024 * Accepted: 16 May 2025 * Published: 22 May 2025

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is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * Psoriasis * Bulk RNA-Seq * scRNA-Seq *

Prognostic model * Immune microenvironment