Bioinformatics Analysis Identifies Potential PANoptosis Key Gene in Psoriasis with Single-cell validation and screening of related natural drug

doi:10.21203/rs.3.rs-2837234/v1

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Bioinformatics Analysis Identifies Potential PANoptosis Key Gene in Psoriasis with Single-cell validation and screening of related natural drug

https://doi.org/10.21203/rs.3.rs-2837234/v1

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PANoptosis is a pivotal process in the pathway of cell death, which affects various cell types, including keratinocytes, and is linked to several autoimmune disorders. While apoptosis, necroptosis, and pyroptosis have been investigated in psoriasis, the precise involvement of PANoptosis in this condition remains largely unexplored. We gathered psoriasis-related data and PANoptosis-related genetic information from authoritative sources such as the GeneCards and Gene Expression Omnibus (GEO). In this particular study, we employed the reliable technique of robust rank aggregation to detect any notable alterations in gene expression (PEGs) between individuals with psoriasis and control subjects. Our approach involved the integration of six distinct gene expression datasets of PANoptosis. TCN1, S100A12, PRKCQ, and ABCC1 in four PRGs were subsequently identified as marker genes with tolerable diagnostic ability by LASSO and SVM-RFE. Following the analysis, it was revealed that the identified marker genes may potentially contribute to the cause of psoriasis by facilitating the regulation of various pathways, such as cell cycle, immune response, and several other pathways associated with this condition. In addition, the differentiated expressions of the marker gene in psoriasis and normal samples were confirmed by the validation set. And the enrichment of marker genes in keratin-forming cells was verified by single-cell validation. Ultimately, the validated genes were employed to prognosticate the efficacious pharmaceutical treatments for psoriasis by utilizing the DGIdb/CMap database. Herb database were used to find relevant natural agents. We have conceived a model that exhibits significant diagnostic efficacy and has yielded valuable insights for exploring the underlying mechanisms of psoriasis. However, additional research is necessary to verify its diagnostic potential for psoriasis before its implementation in clinical settings.

psoriasis

PANoptosis

single-cell

RAA，natural drug

Psoriasis, a chronic inflammatory disease, manifests clinically as hypertrophic erythematous scales [1]. The global prevalence of psoriasis is within the range of 0.91% to 8.5%, and it is one of the most common diseases faced by dermatologists [2]. The skin symptoms induced by psoriasis can add to the patient's heart burden, thus causing negative emotions such as depression and anxiety, and elevating the likelihood of developing cardiovascular disease, metabolic syndrome, cancer, and various other medical conditions. [3]. Psoriasis is prone to recurrence and is also known as undead cancer, which has posed a major challenge to public health [4]. Therefore, the study of potential therapeutic targets for psoriasis remains an important direction for clinical research.

The skin functions as an environmental barrier mainly via the outermost layer of the epidermis [5]. Keratin-forming cells are located in the most outer layer of the epidermis and lay the basis for the epidermis to prevent water loss, avoid allergen and toxin invasion, and control the skin microbiota, among other functions [6]. The skin barrier is dependent on the continuous renewal and death execution of keratinocytes [5], [7], [8]. Cell death is linked to the activation of multiple "danger signals" that may contribute to the development of psoriasis. Therefore, exploring cell death-related genes in psoriasis will help us to identify mechanisms that may result in the identification of new therapeutic targets.

PANoptosis, first proposed in 2019, is now thought to be a type of cell death caused primarily by infection or after injury, which can be promoted by ZBP1 and also inhibited by TAK1 [9], [10]. The PANoptosome contains molecules essential for necroptosis, apoptosis, and pyroptosis and can activate the above three cell death procedures to perform pro-inflammatory cell death [11]. Due to the significant role played by PANoptosis in a range of inflammatory, infectious, and neoplastic conditions, the constituents of the PANoptosome hold promise as viable targets for therapeutic intervention for numerous pathologies, and exploring potential biomarkers and therapeutic targets for psoriasis patients will help develop new therapeutic strategies [12]–[14].

Apoptosis is a highly modulated form of cell death that is influenced by crucial proteins like the tumor necrosis factor (TNF) receptor superfamily, the cysteine family, and the BCL-2 protein family [15]. Research has shown that the expression of anti-apoptotic proteins like BCL- xL and BCL-2 is up-regulated in psoriatic skin lesions, indicating that psoriasis development may be influenced by epidermal cell apoptosis[16], [17]. Additionally, the necroptotic processes involve the death of structural domain receptors, including TNFR and Fas, as well as the downstream Toll-like receptor (TLR)-4 or TLR3. [18]. As demonstrated in mouse experiments, TNFR1 is dependent on the ubiquitination levels of RIPK1 to induce NF-κB activation, and mice lacking key components of this pathway typically exhibit a psoriatic skin phenotype [19]. Necrotroph of keratin-forming cells is an important trigger for inflammation in psoriasis [20]. Nonetheless, more research is required to verify the precise mechanisms of keratin-forming cell necroptosis in the pathogenesis of psoriasis. In addition, it is essential to further investigate the mechanisms of necroptosis-related gene expression in human psoriatic lesions. Pyroptosis is currently considered as a cell death type mediated by cystatin proteases 3, 7, 8 and 9 and other influencing proteins. Additionally, PAMPs and damage-associated molecular patterns (DAMPs) play essential roles in the activation of inflammatory vesicles that lead to the development of chronic inflammatory and autoimmune diseases [21]. Psoriatic skin-derived basal cells express high-levels of pyroptosis-related genes like CASP1, CAPS8, GSDMA, GSDMB and GSDMD, so pyroptosis is involved in the psoriatic process[22].

It is currently understood that psoriasis is caused by abnormalities in the acquired immune system (T-cells) and the innate immune system in resident epidermal cells. Hyperproliferation and abnormal differentiation of keratin-forming cells are the main cellular events in the development of psoriasis [23]. The formation of the induced psoriasis model does not depend exclusively on the aforementioned cell death modalities, and the loss of only one component necessary for the cell death pathway does not exempt cells from the induced cell model [24]. Moreover, there is widespread crosstalk between the molecular components of apoptosis, necroptosis ,and pyrotosis [25].

The notion that skin inflammation may originate from exogenous disturbances in keratinization or related regulatory mechanisms has been posited[5]. Further study of the relationship between keratinization, cell charring, and PANoptosis will help to determine the initial stage of inflammatory skin disease and provide new targets for disease prevention and treatment. Psoriasis is a disease regulated by multiple genes. Nonetheless, current research on the expression of PANoptosis-related genes (PRGs) is usually limited to one or two PRGs. In this regard, comprehensive analysis of PRG characteristics is significant to clarify the underlying mechanism and predict the role of psoriasis in immunotherapy.

Microarray Datasets of psoriasis

Psoriasis gene expression datasets were collected based on the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov)[26]. Thereafter, microarray were thoroughly searched using the MeSH Keywords "psoriasis", "Expression profiling by array", "Homo sapiens" and "attribute name tissue". In order to eliminate potential individual deviations among samples, standardization via quantile normalization was initially performed on all of the microarray data sets.

Before selecting baseline phase samples, The gene expression matrix as well as the associated annotation files for each array dataset were acquired from the GEO database. Afterwards, microarray probes were accurately aligned with gene symbols using the relevant annotation files, and the average value was employed, in which numerous probes were assigned to a single symbol.

PANoptosis gene list

The gene lists of pyroptosis, apoptosis, and necroptosis were combined to generate the PANoptosis gene list (PRGs, n=5111), and the overlapping genes were then eliminated. Typically, all the gene lists were loaded from the gene card (https://www.genecards.org/) with score <1.

RRA and screening of PANoptosis-genes in psoriasis

Robust regression analysis (RRA) technique, an efficient tool to integrate numerous array results, was used to discover robust DEGs to reduce discrepancies and integrate the results from different microarray experiments [27], [28]. Prior to the RRA procedure, gene lists for each dataset that were ordered from the highest to the lowest were obtained based on the expression fold change between cases and controls. Subsequently, the acquisition of gene expression data in each dataset was based on the intersection of PANoptosis genes with the gene list under consideration. All of the ranked gene lists from datasets were combined with the "Robust Rank Aggregation" in the R program. The adjusted P-value of the RRA tool indicated the likelihood that each gene ranked highly in the final gene list. Significant genes were selected upon the thresholds of P-value < 0.05 and a fold change > 0.5.

Functional Enrichment Analysis

The R package "clusterProfiler" was employed to conduct Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. To explore potential roles, relevant genes were uploaded for RRA analysis upon the thresholds of P-value of less than 0.05 and false discovery rate (FDR) less than 0.05.

The optimal diagnostic gene biomarkers for psoriasis

To reduce the dimension of the data, we implemented the least absolute shrinkage and selection operator (LASSO) algorithm [29], [30]. Specifically, the LASSO method was utilized to detect psoriasis-related gene biomarkers, while PRGs in psoriasis versus healthy samples were maintained to select characteristics. Then, the mean misjudgment rates were compared using 10-fold cross-validations when constructing the support vector machine-recursive feature elimination (SVM-RFE) model utilizes the SVM package[31]. Moreover, the best gene biomarkers for psoriasis were determined to be the overlapped biomarkers produced by both systems. Later, The diagnostic efficacy of the optimal gene biomarker was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC).

Single-gene gene set analysis for enrichment

The GSVA (V.1.38.0) package of the R program was utilized in this work, and the background gene set for gene set variation analysis (GSVA) of many marker genes was the KEGG pathway set. Under the significance levels of |t| > 2 and P < 0.05, The utilization of the limma program was implemented for the evaluation of the diverse GSVA scores linked with marker genes, across the high-expression and low-expression groups. In the low-risk group, activation of the pathway occurred in the absence of t > 0, whereas in the high-expression group, activation of the pathway occurred when t > 0 was present.

Differentially expressed genes (DEGs) in the verification set

AUC values, sensitivity, specificity, and accuracy were calculated as part of the validation process by plotting the ROC curves. The ROC curves were adopted for assessing whether the logistic regression models constructed in this study had a certain diagnostic value.

Validation in the single-cell dataset

R software Seutat (V.4.3.0) package was utilized for validation in the single cell dataset. Moreover, single-cell RNA sequencing analysis and single-cell data quality control were carried out separately for each sample prior to data integration [32]–[34]. Genes with differential expression between each cluster and the rest of the cells were identified by graph-based clustering and principal component analysis (PCA). In a cluster, the average gene expression of psoriasis cells was measured compared with control cells. Next, similar immune cell subsets in adjacent clusters were merged to evaluate the differences between psoriasis and control cells in each type of skin immune cell cluster. Further, to compare immune cell clusters, the number of target-gene-expressing cells and the proportion of target-gene-expressing cells in each cluster were measured. The SingleR software was employed to compare cells in various clusters against an annotated reference dataset and completed the cluster annotation [35].

Ethical Declaration

Every piece of information used in this analysis came from open databases. There was no human or animal research included in this study.

Information of the Included Microarrays

GSE161683, GSE181318, GSE162998, GSE166388, GSE117468, and GSE136757 were included in this study based on the previously specified inclusion criteria. There were 185 lesional skin (LP) and 177 non-lesional skin (NP) samples in these six datasets. Table 1 displays the specific details of these datasets.

Table 1 information about the datasets used in our study
tissues	GSEID	analysis type	platform	Database
9 NP and 9 LP	GSE161683	expression profiling by array	GPL6244	GEO
3 NP and 3 LP	GSE181318	expression profiling by array	GPL22120	GEO
3 NP and 11 LP	GSE162998	expression profiling by array	GPL8432	GEO
4 NP and 4 LP	GSE166388	expression profiling by array	GPL570	GEO
128 NP and 128 LP	GSE117468	expression profiling by array	GPL570	GEO
30 NP and 30 LP	GSE136757	expression profiling by array	GPL570	GEO
84 NP and 83 LP	GSE117239	expression profiling by array	GPL570	GEO
4 NP and 4 LP	GSE50790	expression profiling by array	GPL570	GEO
6 NP and 17 LP	GSE151177	high throughput sequencing	GPL18573	GEO

Identification of PRGs in psoriasis

The standardized results are displayed in Supplemental Fig. 1 for each dataset, with all samples achieving acceptable homogeneity. Figure 1 depicts volcano plots generated from the six microarrays. Each gene in each dataset was thought to be randomly sorted by the RRA approach. There were altogether 52 PRGs found from 984 DEGs in the psoriasis versus healthy samples across the entire dataset, with 38 showing up-regulation and 14 showing down-regulation (Supplementary Table 1). In Fig. 2A, a heatmap is presented showcasing the top 10 genes that were both up- and down-regulated. The analysis revealed that out of the significant genes that displayed aberrant expression in psoriasis, 9 of them were up-regulated; these include LCN2 (P = 1.22E-05), TCN1 (P = 1.67E-04), TMBIM6 (P = 2.19E-04), AKR1B10 (P = 5.18-04), PI3 (8.72E-04), TLR3 (P = 8.72E-04), MMP1 (P = 8.72E-04), S100A12 (P = 1.6E-04), and PRKCQ (P = 2.1E-04). Furthermore, one gene, BCL6 (P = 7.55E-04), was found to be down-regulated. The gene associations are exhibited in Fig. 2B.

Functional enrichment analysis of PRGs from the GSE117239 dataset

The biological processes and pathways linked to PRGs were identified using GO functional annotation and Reactome pathway analysis. As a result, PRGs were significantly enriched in the following GO-molecular function (MF) terms, including “RAGE receptor binding”, “signaling receptor activator activity”, “chemokine activity”, “Toll − like receptor binding”, “cytokine activity”, “protease binding”, “long − chain fatty acid binding”, “growth factor activity”, “chemokine receptor binding” and “receptor ligand activity”. Regarding cellular component (CC) terms, PRGs were markedly associated with “secretory granule lumen”, “secretory granule lumen”, “clathrin − coated endocytic vesicle membrane”, “clathrin − coated vesicle membrane”, “clathrin − coated endocytic vesicle”, “endocytic vesicle membrane”, “specific granule lumen”, “coated vesicle membrane”, “specific granule lumen” and “vesicle lumen”. Also, the annotation of GO-biological process (BP) terms indicated the close relation of PRGs with “regulation of DNA − binding transcription factor activity”, “positive regulation of inflammatory response”, “positive regulation of DNA − binding transcription factor activity”, “defense response to bacterium”, “positive regulation of defense response”, “regulation of inflammatory response”, “chronic inflammatory response”, “positive regulation of NF − kappaB transcription factor activity”, “neutrophil chemotaxis” and “positive regulation of response to external stimulus” (Fig. 2C). Based on Reactome pathway analysis, IL − 17 signaling pathway, Lipid and atherosclerosis, Hippo signaling pathway, Cytokine − cytokine receptor interaction, Cholesterol metabolism, Pyrimidine metabolism, Adipocytokine signaling pathway, Viral protein interaction with cytokine and cytokine receptor, PPAR signaling pathway, and Wnt signaling pathway were significantly enriched (Fig. 2D). The above-mentioned results suggested that PRGs might significantly affect psoriasis pathogenesis by controlling autophagy, cytokines, kinases, and immune cells.

4 PRGs served as the diagnostic genes for psoriasis

Furthermore, DEGs obtained by LASSO and SVM-RFE were screened, and this study concentrated on the prediction of whether PRGs could be applied in disease diagnosis. To this end, the GSE117239 dataset was used to analyze two distinct machine learning techniques, LASSO and SVM-RFE, so as to select PRGs that significantly identified patients with psoriasis from healthy individuals. By using the LASSO logistic regression that selected six psoriasis-related characteristics, the penalty parameter was adjusted (Figs. 3A, B). Also, the SVM-RFE algorithm was applied to filter PRGs, and six best characteristic gene combinations were obtained (minimal RMSE = 0.00606, maximal accuracy = 0.994) (Figs. 3C,D). Subsequently, in further investigations, four marker genes (TCN1, S100A12, PRKCQ, and ABCC1) were obtained by intersecting the marker genes obtained from the two aforementioned algorithms (Fig. 3G). The DEGs obtained from RRA analysis were modeled logistically, the results distinguished well between NP and LP samples (Fig. 3E,F), and the four marker bases were filtered by machine learning for better differentiation (Fig. 3H, I).

Marker genes exhibited close relations to several pathways connected to psoriasis

The single-gene GSEA-KEGG pathway analysis was carried out to more thoroughly investigate the potential functions of marker genes in differentiating psoriasis from healthy samples. These four marker gene-associated pathways are shown in Fig. 4. After a thorough analysis of marker genes, it was discovered that they were significantly linked to the cytokine-cytokine receptor interaction, the Nod-like receptor pathway, the JAK-STAT pathway, the Toll-like receptor pathway, as well as several disease pathways (graft versus host disease and type I diabetes mellitus). Typically, the "cytokine-cytokine receptor interaction" was a common pathway among all marker genes.

In addition, GO analysis was performed on the four PRGs, as a result, all these four genes were associated with “cytokine activity", with S100A12, PRKCQ, and ABCC1 being associated with “intermediate filament”, “keratin filament”, and “response to cytokine response” separately, as shown in Fig. 4.

To find potential medications targeting marker genes, DGIdb database was used for analysis, and the two-parameter interaction relation was left at default (Supplementary Table 2). A total of 73 medicines that targeted marker genes were investigated, including one for TCN1, five for S100A12, seventeen for PRKCQ, and fifty for ABCC1. In our subject paper, we conducted a search in the cMap database to identify potential natural active ingredients for treating psoriasis using PANoptosis. The cMap database assigns a score to measure the correlation between small molecules and genes. A score closer to 1 indicates a positive correlation between genes and drugs, while a score closer to -1 represents a negative correlation. The study's findings indicate that BRD-K34812979, WYE-125132, and BRD-K27925875 could potentially serve as small molecule components for treating psoriasis. Additionally, Supplementary Table 3 highlights the top 10 small molecule components with lower scores that could treat psoriasis by inhibiting PANoptosis. Meanwhile, we utilized HERB (http://herb.ac.cn/) to investigate the relationship between diagnostic gene biomarkers and traditional Chinese medicine. Visualization images were generated using cytoscape 3.9.0. Our findings revealed that PRKCQ targets 11 natural medicines, as depicted in Fig. 5A. Additionally, S100A12 was found to be associated with 35 natural medicines, as shown in Fig. 5B. Finally, ABCC1 was found to have a connection with 25 natural drugs, as illustrated in Fig. 5C.

Finally, using the GSE50790 dataset, the expression levels of marker genes were verified. Our findings revealed that the expression profiles of TCN1, S100A12, PRKCQ, and ABCC1 were identical to those in the GSE117239 dataset. Moreover, it is noteworthy that the levels of TCN1, S100A12, ABCC1, and PRKCQ expression were found to be considerably elevated in patients with psoriasis, as compared to those in the healthy control group, based on statistically significant data (Fig. 6A,B,C,D).

In total, thirteen psoriasis lesional skin and five healthy volunteer skin samples were collected, and there were 14,640 cells passing the quality control (Fig. 7A), including 11,462 from psoriasis samples, while the remaining from healthy samples with the following cell types (Fig. 7B). Single-cell samples can be divided into 5 major categories, DC cells, keratinocytes, monocyte,T cells, and tissue stem cells(Fig. 7C). Afterward, cells were divided into 15 clusters (Fig. 8). Of them, Chondrocytes, DC, Endothelial cells, Epithelial cells, Fibroblasts, Keratinocytes, Macrophage, Monocyte, MSC, Neurons, NK cell, ProB cell CD34+, ProMyelocyte, T cells, Tissue stem cells were among the 14 major cell types found in psoriasis (Fig. 8B). Thereafter, four marker genes, namely, TCN1, S100A12, PRKCQ, and ABCC1, were examined. The levels of these genes were noted in the associated cell types (Figs. 8B, C). Results for healthy tissues and psoriasis tissues are displayed in Figs. 8D, F. Marker genes were predominantly distributed in keratinocytes within the psoriasis samples. To be specific, TCN1, S100A12, and ABCC1 were mostly distributed in Keratinocytes, while PRKCQ was predominantly expressed in NK cells.

Skin cells, immune cells, and a variety of biological signaling molecules interact pathologically as a part of psoriasis [36]. We can not merely attribute necroptosis, apoptosis, and pyroptosis as the causes of the biological consequences observed in the pathogenesis of psoriasis. PANoptosis is a unique innate immune inflammatory programmed cell death (PCD) pathway regulated by the PANoptosomes[37]. It incorporates elements from other PCD pathways to be unique [25]. Researchers have increasingly concentrated on the role of PANoptosis in cancers like gastric cancer and melanoma of the skin[38]. To our knowledge, the current work is the first to describe the molecular and immunological aspects linked to PANoptosis in psoriasis.

According to our results, TCN1, S100A12, PRKCQ, and ABCC1 were identified as the putative PRGs in psoriasis, and it was confirmed that these genes were primarily found in keratinocytes and NK cells. TCN1 is a gene responsible for encoding proteins, which is linked to two conditions namely Transcobalamin I Deficiency and Vitamin B12 Deficiency [39]. This gene is also associated with tumor metastasis and proliferation, and sustains the fundamental processes of cell metabolism and proliferation [40], [41]. In this study, the psoriasis-related DEG TCN1 was identified, which was confirmed by single cell analysis to be mostly found in keratinocytes. The increased expression of TCN1 causes folate deficiency and leads to intracellular accumulation of homocysteine (HCY). The observation of a considerable rise in serum HCY levels in psoriasis patients lends credence to this theory [42], [43]. Moreover, HCY may promote immune inflammatory processes in the pathogenesis of psoriasis by activating Th1 and Th17 cells and neutrophils [44]. Additionally, folate may prevent the death of Treg cells via BCL-2, and Treg cells contain large levels of folate receptors [5]. BCL-2 is an important molecule in PANoptosis. We speculate that the PRG TCN1 is involved in the immune inflammatory process in the pathogenesis of psoriasis by regulating the expression of folate to affect the PANoptosis process in keratinocytes. Also, the results of clinical trial research confirm the efficacy and acceptability of calcium folinate for the treatment of psoriasis[45]–[47]. This opens the door for further research by indicating that folate-related receptors may be the molecular targets for psoriasis.

ABCC1 is a protein coding gene, which can boost the ATP-dependent transport of methotrexate to affect cell activity and control drug sensitivity through apoptosis [48]–[50]. Our functional analysis suggested the involvement of secretory granular lumen in the process of PANoptosis in psoriasis. ABCC1 may alter the death of other immune cells or the immunosuppressive effects of T cells by transporting specific cytokines or medications via the ATP transporter-related proteins, leading to drug resistance and diminished pharmacological effects. Research on ABCC1 can help us to explore the mechanism of drug resistance in psoriasis.

S100A12, a calcium-, zinc-, and copper-binding protein, is essential for controlling inflammatory processes and the immune system [51]. S100A12 acts as an alarmin or a DAMP in PANoptosis. As revealed by our GO analysis, S100A12 bound to its RAGE receptor, whose pro-inflammatory activity includes cell recruitment, cytokine and chemokine production. Moreover, S100A12 has also been demonstrated to possess this cellular function, supporting our findings. And it is expressed in both lesional and non-lesional keratin-forming cells as well as dermal cells. S100A12 is expressed differently in inflammatory dendritic cells (DCs) compared with local DCs [52]. Through increasing the expression of vascular leukocyte adhesion factor, S100A12 and the TNF-related apoptosis-inducing ligand (TRAIL) may worsen inflammation [52], [53].

Protein kinase C (PKC), also known as PRKCQ, is a family of enzymes that only metabolizes serine and threonine [54]. Through the phosphorylation of Bcl-2 and its crucial function in Th2 cell growth during immunological and inflammatory responses, T cell receptor (TCR) is triggered to protect T cells from apoptosis caused by Bcl-2 [55], [56]. In this study, Toll − like receptor binding was one part of the GO-molecular function, which is closely related to the recognition and binding of TLR to DAMPs. Therefore, the process of PRKCQ in psoriatic PANoptosis may be related to TLR, and the further investigation is needed to determine the exact mechanism.

Also, GSEA-KEGG analysis was performed on the obtained marker genes. Even when many different main pathways of PCD were combined, one individual type of cell death was unable to account for all of them, probably due to the functional redundancy between them. TNF-α is a multifunctional cytokine that mediates inflammation, immunological response, and apoptosis, which is also a key player in the pathophysiology of psoriasis [57]. Type 1 interferon (TNF) is the product of the Toll-like receptor pathway. It is shown that TNF-α and IFN-γ induced the production of PANoptosis. The pathway may be involved in PANoptosis in the pathogenesis of Psoriasis. By the blockage of each potential node, the precise mechanism of action has to be further researched.

In conclusion, TCN1, S100A12, PRKCQ, and ABCC1 are detected to be the marker genes for PANoptosis in psoriasis, and our discussion focuses on the contributions of individual components of PANoptosomes in the advancement and maturation of psoriasis. The future studies will concentrate on the role of the above genes in the pathogenesis of psoriasis. Through the inhibition of each possible node, it may be possible to effectively inhibit keratin-forming cell death, thus suggesting new options for psoriasis treatment.

Ethics approval and consent to participate

All appearing datasets are from the GEO database. The Beijing Chinese Medicine Hospital Research Ethics Committee has confirmed that no ethical approval is required.

Consent for publication( Not Applicable)

Availability of data and materials

Our study comprised of the datasets GSE161683, GSE181318, GSE162998, GSE166388, GSE117468, GSE136757, GSE117239, GSE50790, and GSE151177, all of which were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).

Competing interests

.The authors have no competing interests to declare that are relevant to the content of this article.

Funding

This study received financial backing from the National Natural Science Foundation of China, which was assigned grant number 82274525.

Authors' contributions

Yue-Min Zou performed the data analysis; Dong-mei Zhou, Ya-nan Jiang and Man-Ning Wu performed the formal analysis; Yue-Min Zou performed the validation; Yue-Min Zou and Dong-mei Zhou wrote the manuscript.

Acknowledgement

The completion of this manuscript would not have been possible without the noteworthy contributions of Dr. Dong-Mei Zhou, my esteemed supervisor, who provided invaluable reference materials consistently. Additionally, I extend my sincere gratitude to my friends, Dr. Man-Ning Wu and Ya-Nan Jiang, for their insightful guidance and enthusiastic encouragement throughout the process of shaping this manuscript, which merits my deepest appreciation.

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No competing interests reported.

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Bioinformatics Analysis Identifies Potential PANoptosis Key Gene in Psoriasis with Single-cell validation and screening of related natural drug

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