Neuroimmune-related differentially expressed genes in psoriasis: A bioinformatics analysis reveals potential biomarkers for diagnosis and treatment

Neuroimmune-related differentially expressed genes in psoriasis: A bioinformatics analysis reveals potential biomarkers for diagnosis and treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neuroimmune-related differentially expressed genes in psoriasis: A bioinformatics analysis reveals potential biomarkers for diagnosis and treatment Bi Qin, Lu Peng, Yuhua Huang, Fangfang Jia, Li Luo, Dandan Tong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8700962/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Psoriasis is a chronic inflammatory skin disease in which neuroimmune interactions are increasingly recognized but remain insufficiently defined. Objective To elucidate neuroimmune mechanisms implicated in psoriasis pathogenesis and identify candidate biomarkers and therapeutic targets. Methods Publicly available psoriasis transcriptomic datasets from the Gene Expression Omnibus (GEO) were analyzed. Neuroimmune-related differentially expressed genes (NRDEGs) were screened, followed by functional enrichment analyses. Supervised diagnostic models (logistic regression, support vector machine, least absolute shrinkage and selection operator [LASSO], and random forest) were constructed. Immune cell infiltration was estimated and correlated with NRDEG expression. Protein–protein interaction networks were built, and key genes were queried against drug/active-ingredient resources. Results Sixty-eight NRDEGs were identified in psoriatic tissue. These genes were significantly enriched in immune and inflammatory pathways, including NF-κB and IL-17 signaling. Diagnostic models based on NRDEGs achieved high predictive performance across algorithms. Five key genes—ENO2, SELL, GREM2, IL1B, and LCN2—were differentially expressed between patients and controls and showed strong associations with inferred immune cell infiltration in lesions. Network analyses prioritized hub genes and mapped them to potential drug active ingredients, suggesting avenues for pharmacologic modulation of neuroimmune pathways. Conclusions Integrated bioinformatics highlights a prominent neuroimmune signature in psoriasis, nominating NRDEGs—particularly ENO2, SELL, GREM2, IL1B, and LCN2—as candidate biomarkers for diagnosis and potential therapeutic intervention. Further experimental and clinical studies are warranted to define causal roles, clarify dynamics during disease progression, and evaluate responses to targeted therapies. Psoriasis neuroimmune machine learning diagnostic model network pharmacology decision-curve analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Psoriasis is a persistent, relapsing, inflammatory, and systemic disorder, clinically manifested by the presence of scaly erythematous plaques or patches which can appear on any part of the body surface and may evolve into distinct subtypes, including psoriatic arthritis or erythrodermic psoriasis, with potential consequences of disability and life-threatening complications [ 1 ]. Approximately 125 million people globally are estimated to have psoriasis, leading to a significant deterioration in their quality of life [ 2 ]. Increasing evidence suggests that psoriasis, besides impacting the skin and joints, is also associated with neuroimmune psychiatric comorbidities, such as depression, anxiety, and psychosis. Psoriasis is widely recognized as an autoimmune disease that involves T-cell mediation, where the significant dysregulation of the IL-17/IL-23 pathway is crucial in the development of the disease, as evidenced by the effectiveness of targeted therapies aimed at T cells [ 3 – 5 ]. However, the occurrence of adverse drug reactions, diminishing long-term efficacy, and recurrence of skin lesions underscore the necessity for novel diagnostic and therapeutic approaches in psoriasis management. The current therapeutic landscape for psoriasis is dominated by biological agents and small molecule targeted drugs, including anti-TNF-α inhibitors, IL-17 blockers, IL-23 antagonists, PDE4 inhibitors, and Tyk2 inhibitors. These treatment modalities have exhibited remarkable efficacy in managing the disease. However, their use is not without adverse consequences; a portion of patients experience severe infections, injection site reactions, and an elevated risk of malignancies. Moreover, a subset of individuals displays primary or secondary non-response to these drugs or develops antidrug antibodies, necessitating a change in therapeutic approach. Additionally, the exorbitant cost of these therapies presents a significant barrier for many patients, impeding widespread access. Hence, there exists an imperative requirement to develop therapeutic options that are safer, more efficacious, and economically viable for psoriasis. Current research endeavors ought to prioritize tackling these challenges and offering a wider array of treatment alternatives specifically designed to cater to the diverse requirements of patients suffering from this chronic inflammatory skin condition. Recent research has underscored the crucial importance of neuroimmune interactions in the pathophysiology of diverse diseases. Research has demonstrated that directional interactions occur between cutaneous nerve endings and immune cells. Specifically, neurons modulate immune responses via mediators like neuropeptides, and immune cells sensitize neurons [ 6 ]. Neuroimmune crosstalk is essential for regulating inflammation, facilitating tissue repair, and defending against pathogens [ 7 ]. Notably, the resolution of psoriatic lesions following nerve damage reflects the significance of neuroimmune interactions in the diseases resolution [ 8 ]. Psoriasis models have demonstrated that preemptive disruption of skin nerve innervation can mitigate lesion induction, highlighting the potential of neuroimmune research in developing new treatments [ 9 ]. Although we have gained these insights, notable lacunae persist in our comprehension. For instance, the role of the cutaneous-immuno-neuro-endocrine (CINE) system in converting the skin into a ‘super organ’ is acknowledged, but the exact processes by which the CINE system coordinates immune and neural signals in psoriasis are yet to be fully understood. Additionally, although cutaneous nerve fibers are known to link epidermal keratinocytes and immunocytes, the precise molecular pathways involved in these interactions are not fully understood. Moreover, the review of cases where psoriasis improved following denervation injury suggests a critical role for neural influences, yet the detailed mechanisms of neuro-immune interactions remain largely unexplored. Additional studies are essential to elucidate the intricate involved underlying the CINE system and neuroimmune crosstalk in psoriasis. Understanding these interactions at a molecular level will be vital for creating innovative, targeted treatments that can more efficiently control or possibly eradicate psoriasis. This underscores the necessity of continued investigation into neuroimmune interactions and their therapeutic potential in psoriasis. Additionally, neuroimmune dynamics exert a critical influence across various conditions, encompassing autoimmune disorders and neurodegenerative processes, and may potentially function as biomarkers for these pathologies [ 10 ]. Environmental triggers can initiate neurogenic inflammation in psoriasis by activating cutaneous nerve channels [ 11 – 13 ]. The investigation of neuroimmune-related differentially expressed genes (NRDEGs) holds promise for elucidating the molecular underpinnings of psoriasis and informing therapeutic interventions. Advances in neuroimmunology over the past decade have focused on Nav+ TRPV1 + neurons and neuropeptides such as CGRP and SP [ 14 ]. Recent discoveries have also emphasized the regulatory roles of Nav1.8 + GINIP + non-peptidergic sensory neurons [ 15 ]. Transcriptomic analysis has uncovered a diverse array of steady-state skin sensory neurons that undergo significant reprogramming following axonal injury [ 16 – 18 ]. Despite these advances, the precise molecular mechanisms that govern neuroimmune system interactions remain incompletely understood, representing a frontier for future research. Our research aims to identify neuroimmune-related differentially expressed genes (NRDEGs) in psoriasis through bioinformatics analysis, conduct functional enrichment analysis, and establish a diagnostic model. Furthermore, we aim to investigate key gene interactions within the contexts of immune infiltration and drug response to deepen us uncover new insights and potential targets for diagnosis and therapy. Specific methods are described in the Supplementary Information. 2. Results The technology roadmap is shown in Fig. 1 . 2.1. Data collection and correction The sva package in R was utilized to eliminate batch effects from the Psoriasis datasets, namely GSE13355, GSE14905, and GSE55201, resulting in the Combined GEO datasets. Initially, the distribution boxplot (Fig. 2 a-b) was utilized to compare the expression values of the datasets before and after the elimination of batch effects. Subsequently, the PCA (Principal Component Analysis) plot (Fig. 2 c-d) was used to compare the distribution of low-dimensional features before and after the removal of batch effects. The results obtained from the distribution boxplot and PCA plot demonstrated a significant reduction in the batch effect within the Psoriasis dataset samples after batch removal. 2.2. Neuroimmune-related differentially expressed genes associated with psoriasis The Combined GEO Datasets were classified into two groups: a Psoriasis group and a Normal group. To investigate the differences in gene expression levels between the Psoriasis and Normal groups within the Combined GEO Datasets, the limma R package was employed to perform differential analysis, ultimately resulting in the identification of differentially expressed genes (DEGs) between the Psoriasis and Normal groups. The outcomes are detailed below: The Combined GEO Datasets encompassed an aggregate of 13598 genes which fulfilled the criteria of having an absolute logFC greater than 0 and an adjusted P-value less than 0.05, thereby suggesting the presence of differentially expressed genes (DEGs). Among these genes, 6048 demonstrated upregulation, distinguished by having a logFC above 0 and an adjusted P-value below 0.05, while 7550 genes showed downregulation, exhibiting a logFC below 0 and an adjusted P-value similarly less than 0.05, as illustrated in the variance analysis results presented in the dataset's volcano plot (Fig. 3 a). To identify NRDEGs, genes with an absolute logFC value exceeding 0 and an adjusted P-value below 0.05 were selected, subsequently, the intersection between DEGs and NRGs was determined, and then visualized using Wayne (Fig. 3 b). A total of 68 NRDEGs were obtained, as presented in Appendix 1 . An analysis was conducted to evaluate the expression disparities of NRDEGs between the Psoriasis and Normal sample cohorts in the Combined GEO Datasets, using the R package pheatmap to visualize the disparity rankings (as shown in Fig. 3 c), ultimately presenting the results of the analysis. 2.3. Gene ontology (GO) and pathway (KEGG) enrichment analysis To further explore the relationships between biological processes (BP), cellular components (CC), molecular functions (MF), and biological pathways (KEGG) associated with the 68 neuroimmune-related differentially expressed genes (NRDEGs) identified in Psoriasis, gene ontology (GO) and pathway enrichment analyses were conducted. The 68 previously mentioned neuroimmune-related differentially expressed genes (NRDEGs) underwent gene ontology (GO) and pathway (KEGG) enrichment analysis, with the detailed outcomes presented in Table 1 . Table 1 Results of GO and KEGG Enrichment Analysis for NRDEGs. Ontology ID Description GeneRatio BgRatio pvalue p.adjust BP GO:0051092 positive regulation of NF-kappaB transcription factor activity 6/66 154/18800 1.66E-05 1.79E-04 BP GO:0007259 receptor signaling pathway via JAK-STAT 5/66 173/18800 3.54E-04 2.24E-03 BP GO:0043410 positive regulation of MAPK cascade 11/66 491/18800 1.01E-06 2.00E-05 BP GO:0010507 negative regulation of autophagy 3/66 85/18800 3.32E-03 1.22E-02 BP GO:0002460 adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 17/66 370/18800 7.24E-15 4.23E-12 CC GO:0009897 external side of plasma membrane 12/67 455/19594 4.01E-08 6.09E-06 CC GO:0060205 cytoplasmic vesicle lumen 10/67 325/19594 1.47E-07 6.09E-06 CC GO:0045121 membrane raft 10/67 326/19594 1.52E-07 6.09E-06 CC GO:0031983 vesicle lumen 10/67 327/19594 1.56E-07 6.09E-06 CC GO:0098857 membrane microdomain 10/67 327/19594 1.56E-07 6.09E-06 MF GO:0030546 signaling receptor activator activity 18/66 496/18410 8.55E-14 2.26E-11 MF GO:0048018 receptor ligand activity 17/66 489/18410 9.38E-13 1.24E-10 MF GO:0005125 cytokine activity 13/66 235/18410 1.93E-12 1.69E-10 MF GO:0005126 cytokine receptor binding 13/66 272/18410 1.22E-11 8.06E-10 MF GO:0005539 glycosaminoglycan binding 10/66 234/18410 1.03E-08 5.43E-07 KEGG hsa04064 NF-kappa B signaling pathway 10/59 104/8164 2.71E-09 1.83E-07 KEGG hsa04657 IL-17 signaling pathway 5/59 94/8164 5.57E-04 3.88E-03 KEGG hsa04217 Necroptosis 5/59 159/8164 5.65E-03 2.28E-02 KEGG hsa05323 Rheumatoid arthritis 12/59 93/8164 1.66E-12 3.36E-10 KEGG hsa05144 Malaria 9/59 50/8164 5.68E-11 5.73E-09 NRDEGs: Neuroimmunology-Related Differentially Expressed Genes. GO: Gene Ontology. BP: biological process. CC: cellular component. MF: molecular function. KEGG: Kyoto Encyclopedia of Genes and Genomes. Our study's findings indicate that a significant proportion of the 68 neuroimmune-related differentially expressed genes (NRDEGs) demonstrate enrichment in positively regulating NF-kappaB transcription factor activity in the context of Psoriasis. In particular, these genes demonstrated enrichment in receptor signaling pathways mediated by JAK-STAT, promotion of the MAPK cascade and inhibition of autophagy are both categorized as biological processes (BP). Furthermore, enrichment was observed in a diverse array of cellular components, specifically, the external surface of the plasma membrane, as well as the lumen of cytoplasmic vesicles, membrane rafts, vesicular lumen, among numerous other components, all categorized under cellular components (CC). Additionally, these genes demonstrated enrichment across various molecular functions, such as signaling receptor activator activity, cytokine activity is included, receptor ligand activity, as well as cytokine receptor binding, among others, all classified under molecular functions (MF). Moreover, significant enrichment was detected in diverse biological pathways, specifically, the NF-kappa B signaling pathway, IL-17 signaling pathway, Necroptosis, Rheumatoid arthritis, Malaria, Lipid and atherosclerosis, The intestinal immune network pertaining to IgA production, among various pathways, are all classified within the Kyoto Encyclopedia of Genes and Genomes (KEGG). The outcomes of the enrichment analysis pertaining to gene ontology (GO) and pathway (KEGG) were visually depicted through bubble plots (Fig. 4 a-b). A network diagram was concurrently produced, illustrating biological processes (BP), cellular components (CC), emphasizing molecular functions (MF), and portraying biological pathways (KEGG), derived from the enrichment analysis related to gene ontology (GO) and pathway (KEGG) (Fig. 4 c-d). The lines within the diagram denote the corresponding molecules and annotations for each entry, with larger nodes indicating a greater number of molecules present in those entries. Furthermore, a bar chart (Fig. 4 e-f) was employed to showcase the results of the enrichment analysis for gene ontology (GO) and pathway (KEGG) pertaining to the combined logFC values. 2.4. Gene Set Enrichment analysis (GSEA) To evaluate the impact of expression levels of all genes in the Combined GEO Datasets on Psoriasis, GSEA was employed to explore the expression patterns displayed by these genes, along with the related biological processes involved. The relationship between the affected cellular components and their respective molecular functions, as depicted in Fig. 5 a, is further detailed in Table 2 . Our findings revealed that all genes within the GEO Datasets (Combined Datasets) demonstrated notable enrichment in the Nf-Kb pathway (Fig. 5 b), TP53 pathway (Fig. 5 c), IL23 pathway (Fig. 5 d), Wnt pathway (Fig. 5 e), and TGFB pathway (Fig. 5 f), accompanied by other biologically relevant functions and signaling cascades. Table 2 Results of GSEA for Combined Datasets. ID SetSize EnrichmentScore NES pvalue p.adjust qvalue REACTOME_SIGNALING_BY_TGFB_FAMILY_MEMBERS 118 -0.453 -1.713 2.16E-04 5.96E-03 4.84E-03 WP_WNT_SIGNALING 109 -0.474 -1.769 1.97E-04 5.74E-03 4.67E-03 PID_IL23_PATHWAY 37 0.708 2.002 8.59E-05 3.13E-03 2.55E-03 REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_CELL_CYCLE_GENES 48 0.596 1.776 1.94E-03 2.80E-02 2.27E-02 REACTOME_TNFR2_NON_CANONICAL_NF_KB_PATHWAY 95 0.486 1.656 3.09E-03 3.78E-02 3.07E-02 REACTOME_M_PHASE 332 0.518 2.047 1.00E-10 3.54E-08 2.88E-08 REACTOME_NEUTROPHIL_DEGRANULATION 448 0.493 1.993 1.00E-10 3.54E-08 2.88E-08 REACTOME_INTERFERON_ALPHA_BETA_SIGNALING 70 0.745 2.414 1.84E-10 5.63E-08 4.58E-08 WP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS 117 0.659 2.303 2.05E-10 5.63E-08 4.58E-08 REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION 115 0.648 2.271 1.37E-09 3.40E-07 2.76E-07 GSEA: Gene Set Enrichment Analysis. NES: Normalized Enrichment Score 2.5. Construction of psoriasis diagnostic model Firstly, the diagnostic significance of the 68 neuroimmune-related differentially expressed genes (NRDEGs) in Psoriasis was evaluated, a univariate logistic regression model utilizing the 68 NRDEGs was constructed and its results were visualized through a Forest Plot (Fig. 6 a). The findings indicated that all 68 NRDEGs exhibited statistical significance in the logistic regression model (p value < 0.05), as detailed in Appendix 2 . Subsequently, a SVM (Support Vector Machine) model was developed using 68 neuroimmune-related differentially expressed genes (NRDEGs). The SVM algorithm was employed in this model to identify the number of genes associated with the lowest error rate (as shown in Fig. 6 b) and to achieve the maximal accuracy (as depicted in Fig. 6 c). The results indicate that the SVM model attains optimal accuracy when the gene count is 20, and these 20 neuroimmune-related differentially expressed genes (NRDEGs) are: EXO1, ENO2, PPARG, CHRM3, SELL, TREM2, GREM2, IL1B, DGCR5, CXCL13, AQP4, CTLA4, TAC3, LCN2, IFIH1, NTF3, TNFSF13B, SELE, PINK1, F10. Subsequently, the 68 neuroimmune-related differentially expressed genes (NRDEGs) were used as the basis, employing the LASSO regression analysis, a diagnostic model for Psoriasis was formulated. To facilitate visualization, the diagram of the LASSO regression model (Fig. 6 d) and the diagram illustrating the LASSO variable trajectory (Fig. 6 e) were produced. The findings indicated that the LASSO regression model encompassed 21 NRDEGs, referred to as Model Genes, which were specifically: BACE1, CCL5, CHRM3, CTLA4, ECE2, ENO2, EXO1, GREM2, IL1B, KMO, LCN2, NTF3, PPARG, RGS2, SELE, SELL, TAC3, TGFB1, TNF, TNFSF13B, and TRPM7. Assessing the diagnostic potential of the 68 neuroimmune-related differentially expressed genes (NRDEGs) in psoriasis, the RandomForest algorithm was employed to examine the expression patterns of these NRDEGs across the merged datasets encompassing both psoriasis and normal cohorts. With a fixed seed of 234 and a specification of 200 decision trees, the decision tree error curve (depicted in Fig. 6 f) was produced. The findings revealed that the error plateaued at 11 decision trees. Subsequently, a MeanDecreaseGini scatter plot (portrayed in Fig. 6 g) for the 68 NRDEGs was constructed to identify pertinent genes. Notably, MeanDecreaseGini denotes the mean diminution of the Gini coefficient, which signifies the impurity of a node. A heightened Gini coefficient indicates reduced purity and increased impurities. Hence, MeanDecreaseGini reflects the average decrement in impurity of the variables segregating nodes across all trees. A larger MeanDecreaseGini value suggests that the gene holds greater significance in our Psoriasis/Normal classification, thereby exerting a more profound influence on psoriasis diagnosis. Following this, the optimal gene count was determined through five iterations of ten-fold cross-validation, accompanied by a cross-validation error plot. The plot illustrated that an error minimum was achieved with 11 genes, and the error tended to stabilize with an escalating gene count. In conjunction with the MeanDecreaseGini analysis, specific genes were then selected for further investigation. The outcomes pinpointed 11 NRDEGs with pivotal roles in psoriasis diagnosis: CXCL1, LCN2, LTF, CXCL13, ENO2, GREM2, IL1B, DPYSL2, IFIH1, CXCL8, and SELL. To identify the Key Genes, the intersection among the NRDEGs identified by the SVM model, LASSO regression model, and random forest was determined, resulting in a total of 5 Key Genes. The Venn diagram illustrating this intersection is shown in Fig. 6 h. The 5 identified Key Genes are: ENO2, SELL, GREM2, IL1B, and LCN2. 2.6. Validation of diagnostic models for psoriasis In order to further corroborate the diagnostic model for Psoriasis, utilizing the Model Genes as the foundation, a Nomogram was formulated, to illustrate the interplay among the Model Genes within the Combined GEO Datasets (depicted in Fig. 7 a). The results demonstrated that the expression level of the Model Gene SELL exhibited significantly higher utility compared to the other variables in the diagnostic model for Psoriasis. Conversely, the value of GREM2 expression in the diagnostic model for Psoriasis was significantly lower than that of the other variables. To evaluate the precision and discriminatory power of the Psoriasis diagnostic model, a Calibration Curve was subsequently generated through Calibration analysis. The predictive efficacy of the model was assessed by examining the alignment between actual and predicted probabilities across different conditions, as depicted in Fig. 7 b. The analysis of the Calibration Curve indicated that the dotted line representing the calibration line showed minor deviation from the diagonal line of the optimal model, but was still largely congruent. To evaluate and demonstrate the clinical utility of the Psoriasis diagnostic models derived from the Combined GEO Datasets' Model Genes, Decision Curve Analysis (DCA) was utilized (Fig. 7 c). The findings suggested that, within a specific range, the line representing the model exhibited superior stability when compared to the all positive and all negative lines, accompanied by a higher net benefit and demonstrated an overall better performance. Furthermore, ROC curves were generated for the linear predictors obtained from the Logistic regression model, particularly for the distinct groups, namely Psoriasis and Normal, within the Combined GEO Datasets. The ROC curves were plotted, with the corresponding outcomes shown in Fig. 7 d. As illustrated in the figure, the Logistic regression model, utilizing the GEO dataset, exhibited robust diagnostic efficacy. 2.7. Differential expression analysis of Key Genes between cancer group and normal group in Combined Datasets The Violin plot presented in Fig. 8 a illustrates the differential expression of five key genes, namely ENO2, SELL, GREM2, IL1B, and LCN2, across distinct groups (Normal/Psoriasis) within the Combined Datasets. The results obtained from our study revealed that the expression levels of five key genes exhibited statistically significant differences (p < 0.001) among the Normal and Psoriasis groups in the Combined Datasets. Subsequently, the positions of five pivotal genes, namely ENO2, SELL, GREM2, IL1B, and LCN2, were examined on the human chromosomes using the R package RCircos, resulting in the generation of a chromosomal localization map (Fig. 8 b). Chromosomal mapping analysis indicated that SELL and GREM2 were both positioned on chromosome 1. The results further showed that IL1B was located on chromosome 2. Additionally, it was found that LCN2 resided on chromosome 9. Lastly, ENO2 was determined to be situated on chromosome 12. Lastly, the ROC curves for the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) within the Combined Datasets were plotted to present the results (Fig. 8 c-g). The ROC curve analysis revealed that the expression differences of the Key gene LCN2 demonstrated high accuracy in distinguishing between different groups (AUC = 0.9). Furthermore, the expression differences of the remaining Key Genes (ENO2, SELL, GREM2, IL1B) within the Combined Datasets exhibited a moderate level of accuracy in discriminating among different groups, with AUC values ranging between 0.7 and 0.9. 2.8. Construct mRNA-miRNA, mRNA-RBP, mRNA-TF interaction network To predict the functional similarities of the identified key genes, we utilized the GeneMANIA web platform and subsequently constructed an interaction network (Fig. 9 a). Additionally, we utilized the miRTarBase and miRDB databases in order to pinpoint miRNAs that may potentially interact with our five key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Subsequently, the results obtained from both databases were intersected using Cytoscape software, allowing for the visualization of the mRNA-miRNA interaction network (depicted in Fig. 9 b). Upon examination, the network was found to consist of two key genes (mRNA), IL1B and ENO2, accompanied by nine miRNA molecules, resulting in a total of ten mRNA-miRNA interaction pairs, as detailed in Supplementary Appendix 3. Employing mRNA-RBP data sourced from the ENCORI database, we undertook the prediction of RBPs interacting with the five designated key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Following this, we harnessed the outcomes extracted from the database and, using Cytoscape software, constructed and visualized the mRNA-RBP interaction network (depicted in Fig. 9 c). A closer inspection of this mRNA-RBP interaction network disclosed that it encompassed the aforementioned five key genes and consisted of 58 RBP molecules, ultimately forming a total of 66 mRNA-RBP interaction relationships. For an exhaustive breakdown, kindly refer to Supplementary Appendix 4. In the final stage, we used the CHIPBase (version 3.0) and hTFtarget databases in order to identify transcription factors (TFs) potentially interacting with our five key genes (ENO2, SELL, GREM2, IL1B, LCN2). Afterwards, the shared outcomes were derived from both databases, and the mRNA-TF interaction network was visualized by employing Cytoscape software (as illustrated in Fig. 9 d). Ultimately, this methodology yielded data concerning the interaction relationships between the five Key Genes and the 70 identified transcription factors (TFs). Specifically, a total of 91 mRNA-TF interaction pairs were identified, with detailed information provided in Appendix 5 . 2.9. Immune infiltration analysis (ssGSEA and MCPCounter) The ssGSEA algorithm was utilized in order to examine the relationship among the sample expression profile data from 28 immune cells between distinct groups, namely Normal and Psoriasis, within the Combined Datasets. According to the outcomes of the immune infiltration analysis, group comparison boxplots were utilized (depicted in Fig. 10 a) to demonstrate the abundance of infiltration of the 28 types of immune cells across the distinct groups, namely Normal and Psoriasis, within the Combined Datasets. The analysis conducted by us uncovered that a total of twenty-three immune cell types exhibited statistically significant disparities in expression levels (P < 0.05) across the Normal and Psoriasis groups within the amalgamated Datasets. Specifically, the identified immune cell types encompassed Activated B cells, as well as Activated CD4 and CD8 T cells and Activated dendritic cells, CD56bright and CD56dim natural killer cells, along with Central and Effector memory CD4 and CD8 T cells, Gamma delta T cells, Immature B cells, Macrophages, MDSCs, Memory B cells, and Monocytes, Natural killer T cells, Neutrophils, and Regulatory T cells, as well as T follicular helper cells, and Type 1, Type 2, and Type 17 T helper cells. The correlation heatmap presented in Fig. 10 b served to illuminate the relationships among the levels of immune cell infiltration abundance pertaining to five key genes (ENO2, SELL, GREM2, IL1B, LCN2) and the 20 statistically significant variables (with a p value < 0.05). It is noteworthy that the pivotal genes SELL, LCN2, and IL1B exhibited positive correlations with the majority of immune cells. In contrast, the pivotal gene ENO2 displayed negative correlations specifically with Gamma delta T cells and Neutrophils.Notably, the crucial genes SELL, LCN2, and IL1B demonstrated positive associations with a substantial proportion of immune cells. Utilizing the MCPCounter algorithm, we evaluated the abundance of immune cell infiltration as well as the expression levels of five pivotal genes (ENO2, SELL, GREM2, IL1B, LCN2) across different groups (Normal/Psoriasis) within the Combined Datasets (Fig. 10 c). Our findings indicated that the five key genes exhibited associations with ten distinct immune cell types, specifically T cells, CD8 + T cells, cytotoxic lymphocytes, B-cell lineage, and NK cells, monocytic lineage, myeloid dendritic cells, neutrophils, endothelial cells, as well as fibroblasts. It is worth noting that positive correlations were detected between the Key Gene IL1B and neutrophils, the Key Gene GREM2 and endothelial cells, and also between the Key Gene LCN2 and endothelial cells. Conversely, a negative correlation was identified between ENO2 and monocytic lineage. 2.10. Neuroimmune score Utilizing the expression levels of five key genes (ENO2, SELL, GREM2, IL1B, LCN2) detected in psoriasis patient samples within the Combined Datasets, the Neuroimmunology Score (Ns) for each psoriasis patient in the Combined Datasets was subsequently derived through the application of the ssGSEA algorithm. To investigate the differences in gene expression profiles between the High and Low neuroimmunology score groups among the disease patient samples in the Combined Datasets, a Violin plot (depicted in Fig. 11 a) was utilized to visualize the expression disparities of the five key genes between the two neuroimmune score groups within the disease samples of the Combined Datasets. Analysis of the data uncovered that, in the combined datasets of Psoriasis patients, the expression levels of two key genes, namely SELL and IL1B, demonstrated significant statistical differences (with a p-value < 0.001) when contrasting groups with high and low neuroimmune scores. Additionally, within the combined datasets of Psoriasis patients, the expression of the essential genes ENO2 and LCN2 demonstrated considerable statistical significance (p-value < 0.01) when contrasting the high and low neuroimmune scoring cohorts. Furthermore, the study included a co-expression heatmap for these five pivotal genes, as illustrated in Fig. 11 b. To investigate the differences in gene expression profiles between the High and Low neuroimmunology score groups among the disease patient samples in the Combined Datasets, a Violin plot (depicted in Fig. 10 a) was utilized to visualize the expression disparities of the five key genes between the two neuroimmune score groups within the disease samples of the Combined Datasets. Examination of the data revealed that, within the combined datasets of Psoriasis patients, the expression levels of two critical genes, SELL and IL1B, exhibited substantial statistical significance (p-value < 0.001) when comparing patient groups with high and low neuroimmune scores. Additionally, within the combined datasets of Psoriasis patients, the expression of the essential genes ENO2 and LCN2 demonstrated considerable statistical significance (p-value < 0.01) when contrasting the high and low neuroimmune scoring cohorts. Furthermore, the study included a co-expression heatmap for these five pivotal genes, as illustrated in Fig. 6 b. Subsequently, ROC curves were constructed for the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) in order to compare the High and Low neuroimmune score groups within the disease samples of the Combined Datasets. For clarity, results with AUC values below 0.6 were excluded from the display (Fig. 11 c-f). ROC curve analysis indicated that the Key Genes ENO2, SELL, IL1B, and LCN2 demonstrated a moderate degree of accuracy, as evidenced by AUC values between 0.7 to 0.9, in discriminating between High and Low neuroimmune score categories within the disease samples from the Combined Datasets. In order to investigate the disparities in the h.all.v7.4.symbols.gmt gene sets between the High and Low neuroimmune score groups within the Combined Datasets, a gene set variation analysis (GSVA) was conducted on all genes present in the Combined Datasets. The pertinent details regarding this analysis are presented in Table 3 . The outcomes of the GSVA revealed that gene sets such as Inflammatory Response, IL-6 JAK-STAT3 Signaling, Apical Surface, Xenobiotic Metabolism, Elevated KRAS Signaling Activity, The Metabolism of Bile Acids, Delayed Estrogen Response, and Apoptosis, demonstrated statistically significant disparities among the High and Low neuroimmune score groups, significant differences were observed (P < 0.05). Based on the outcomes of the GSVA, the expression profiles that differentiate the High and Low neuroimmune score (High/Low) groups were examined and visually represented through a heat map (Fig. 11 g). Table 3 Results of GSVA for Combined Datasets. Hallmark Gene Set logFC AveExpr t P.Value adj.P.Val HALLMARK_INFLAMMATORY_RESPONSE -0.1739 -0.0140 -4.2459 3.76E-05 1.88E-03 HALLMARK_IL6_JAK_STAT3_SIGNALING -0.1838 0.0015 -3.7691 2.34E-04 5.84E-03 HALLMARK_APICAL_SURFACE -0.1395 -0.0188 -3.4048 8.46E-04 1.41E-02 HALLMARK_XENOBIOTIC_METABOLISM -0.0847 0.0023 -2.3646 1.93E-02 1.78E-01 HALLMARK_KRAS_SIGNALING_UP -0.0910 -0.0023 -2.3399 2.06E-02 1.78E-01 HALLMARK_BILE_ACID_METABOLISM 0.1033 0.0047 2.3253 2.14E-02 1.78E-01 HALLMARK_ESTROGEN_RESPONSE_LATE 0.0752 -0.0040 2.1065 3.68E-02 2.44E-01 HALLMARK_APOPTOSIS -0.0871 -0.0193 -2.0805 3.91E-02 2.44E-01 GSVA: Gene Set Variation Analysis. logFC: Log2 fold change. AveExpr: Average expression. adj.P.Val: adjusted P value 2.11. Construct disease-related subtypes of psoriasis To investigate the expression differences of the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) in the Combined Datasets, particularly in samples from Psoriasis patients, we employed the R package "ConsensusClusterPlus". Using the expression levels of these five Key Genes in the Combined Datasets, we ultimately identified two distinct psoriasis disease subtypes, labeled as cluster1 and cluster2, via consensus clustering (depicted in Fig. 12 a). Specifically, the psoriatic disease subtype 1, denoted as cluster1, encompassed a total of 104 samples, while the psoriatic disease subtype 2, designated as cluster2, consisted of 31 samples. Subsequently, a principal component analysis (PCA) was conducted on the expression matrix dataset derived from the two psoriasis disease subtype samples within the Combined Datasets. The PCA clustering results demonstrated significant differences between the two psoriasis disease subtype samples (depicted in Fig. 12 b). Additionally, the Delta plot was presented, depicting the area under the curve (AUC) of the cumulative distribution function (CDF) (Fig. 12 c), alongside the CDF plot itself (Fig. 12 d), showcasing varying cluster numbers within the consensus clustering results. As indicated by the figures, when the cluster count in unsupervised clustering was designated as k = 2, the Combined Datasets yielded the most optimal consensus clustering outcomes. Subsequently, the expression levels of the five key genes within the Combined Datasets were examined utilizing the Mann-Whitney U test, also referred to as the Wilcoxon rank sum test, with a focus on comparing the two distinct psoriasis disease subtypes, designated as cluster1 and cluster2. The outcomes of this analysis, emphasizing the disparities in expression, are depicted in a group comparison plot. As depicted in Fig. 12 e, the expression levels of three Key Genes—GREM2, IL1B, and LCN2—show marked statistical significance (P < 0.001) when comparing cluster1 and cluster2, the two psoriasis disease subtypes in the Combined Datasets. In addition, the analysis detected a statistically significant disparity in the expression of the Key gene ENO2 between the two psoriasis disease subtypes (P < 0.05). Concurrently, we amalgamated the survival outcome (OS) and survival time (OS.time) data pertaining to samples from the two Psoriasis disease subtypes (cluster1 and cluster2), as well as the breast cancer Combined Datasets, to construct the survival Kaplan-Meier (KM) curve. Following this, ROC curves were plotted for the five Key Genes to compare the two psoriasis disease subtypes, namely cluster1 and cluster2, within the Combined Datasets. Results with AUC values less than 0.6 were excluded from the analysis (Fig. 12 f-h). The findings indicated that the Key gene GREM2 exhibited moderate accuracy (0.7 < AUC < 0.9) in distinguishing between the two psoriasis subtypes (cluster1 and cluster2). Conversely, the Key Genes IL1B and ENO2 demonstrated relatively low accuracy (0.5 < AUC < 0.7) in differentiating between the two psoriasis subtypes (cluster1 and cluster2). 2.12. Immune infiltration analysis (ssGSEA and MCPCounter) The ssGSEA algorithm was utilized to analyze the correlation between the expression profiles of 28 immune cell types in disease patient samples categorized into High and Low neuroimmune score groups within the combined datasets. The abundance of immune cell infiltration, as determined by immune infiltration analysis, was depicted for the High/Low neuroimmune score groups using a grouped comparison box plot, as shown in Fig. 13 a. The analytical outcomes indicated that eight distinct immune cell types showed statistically significant expression differences (P < 0.05) within the High/Low neuroimmune score groups in the disease samples from the Combined Datasets. In particular, the immune cells with significant expression were classified as activated CD4 T cells, activated B cells, activated dendritic cells, myeloid-derived suppressor cells (MDSCs), memory B cells, natural killer (NK) cells, Th1 cells, and Th17 cells. A correlation heatmap, as depicted in Fig. 13 b, was utilized to visualize the relationship between immune cell infiltration abundance and the expression levels of the five key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Specifically, our focus was directed towards the eight statistically significant correlations (P < 0.05) identified among these genes. Among the significant correlations observed, the Key Genes SELL, LCN2, and IL1B demonstrated positive associations with the majority of immune cells. In contrast, the Key gene ENO2 exhibited a negative correlation with most immune cells. The MCPCounter algorithm, as depicted in Fig. 13 c, was utilized to assess the correlation among the five designated Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) and varying levels of immune cell infiltration in patient samples from the Combined Datasets, which were categorized based on neuroimmune scoring as either High or Low. The analysis indicated an association between the five Key Genes and ten distinct immune cell types, including T cells, CD8 T cells, and cytotoxic lymphocytes, as well as B lineage cells, NK cells, and monocytic lineage cells, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts. Significantly, the study identified a positive correlation between IL1B and neutrophil levels, alongside a similar positive relationship between GREM2 and endothelial cell occurrence. Furthermore, LCN2 was positively correlated with fibroblast presence, reinforcing the previously established correlation of GREM2 with endothelial cells. Conversely, an inverse correlation was detected between ENO2 and the presence of NK cells. 2.13. Drug susceptibility analysis and molecular docking Furthermore, we employed the mRNA expression profiles of neuroimmune-related Key Genes alongside drug activity data derived from the CellMiner database of cancer drug sensitivity. Utilizing the pRRophetic algorithm, we predicted the sensitivity of neuroimmune-related Key Genes to common anticancer drugs, basing our predictions on the expression of these genes and applying the ridge regression model to calculate the IC50 value. Subsequently, we visually represented the correlation between neuroimmune-related Key Genes and small molecules of cancer drug sensitivity in the CellMiner database, as depicted in the following figure (Fig. 14 ). The analysis revealed the presence of 19 drugs with interactions in the CellMiner database, and notably, the neuroimmune-related Key Gene LCN2 exhibited a negative correlation with most of these drugs. 2.14. Molecular docking Three neuroimmune-related Key Genes (ENO2, GREM2, IL1B) without corresponding drug small molecules were docked with their corresponding active ingredients by CB-Dock2, which were ENO2 and FLOXURIDINE, GREM2 and ALLOPURINOL, respectively. The docking results of neuroimmune-related Key Genes (IL1B, GREM2, IL1b) and PENTAMIDINE with their corresponding active components are shown in Fig. 10 A-C. The neuroimmune-related Key Genes ENO2 and FLOXURIDINE (Fig. 15 a) showed moderate binding (Vina Score = -5.8 Kcal/mol). The amino acids ASN1139, CYS1356, LYS1357, GLN1360, GLY1363, TRP1364, GLY1365, LEU1387, CYS1388, THR1389, ARG1428, ASN1429, PRO1430, SER1431 through hydrogen bonding, ionic bonding, π-π conjugation, Hydrophobic interaction with FLOXURIDINE. The neuroimmune-related Key Genes GREM2 and ALLOPURINOL (Fig. 15 b) have moderate binding capacity (Vina Score = -4.9 Kcal/mol). ALA59, LEU60, VAL61, VAL62, THR63, GLU64, ARG65, LEU68 interacted with ALLOPURINOL through weak hydrogen bond, hydrogen bond, ionic bond, π-π conjugation, and hydrophobic interaction. Neuroimmune-related Key Genes IL1B showed moderate binding with PENTAMIDINE (Fig. 15 c) (Vina Score = -6.2 Kcal/mol), in which amino acids TYR24, GLU25, LEU26, LYS27, LEU69, LYS74, THR79, LEU80, GLN81, LEU82, SER125, MET130, PRO131, VAL132, PHE133, LEU134, ASP142 through weak hydrogen bonding, Hydrogen bonding, π-π conjugation, The hydrophobic force interacts with PENTAMIDINE. 3. Discussion Psoriasis, a persistent immune-related skin ailment, extends beyond skin damage, elevating the risks of cardiovascular illnesses, anxiety, depression, and other comorbidities, thus profoundly affecting patients' lives [ 3 ]. The current main treatment modalities for psoriasis include topical medications, traditional systemic drugs, biologics, and small molecule drugs. Notably, biologics and small molecule drugs have shown promising efficacy, but they still have numerous limitations, such as drug non-responsiveness, secondary failure, and drug resistance. These issues may be related to individual genetic variability, disease severity, and comorbidities, and there is currently a lack of predictive biomarkers for treatment response. In terms of secondary non-response, immune escape and disease progression are major concerns. The former refers to the development of anti-drug antibodies in patients, leading to drug failure, while the latter indicates a worsening of the condition over time, necessitating a change in treatment strategy. Additionally, there are concerns about side effects and safety, including an increased risk of infections and tumors, as well as limited long-term safety data. Therefore, delving deeper into the potential mechanisms of neuroimmunology in psoriasis holds significant importance. It is expected to provide new targets and methods for precision medicine in psoriasis, enhancing treatment efficacy and safety, filling gaps in current therapeutic options, and offering patients more personalized and effective treatment plans. Intriguingly, psoriasis skin lesions often spontaneously resolve in areas with nerve deficiency but recur upon nerve regeneration, a phenomenon also observed in psoriasis mouse models [ 19 , 20 ]. The increased nerve fibers and neurotrophic factors in psoriatic lesions underscore the nervous system's role in disease progression [ 21 ], highlighting the link between psoriasis and the nervous system. The skin's neuroimmune network, involving both autonomic and sensory nerves, adapts to environmental changes through neuropeptides, which not only influence skin cells but are also produced by them, potentially stimulating nerve fibers and exacerbating inflammatory responses [ 22 , 23 ]. The neuro-immuno-cutaneous (NIC) and neuro-immuno-cutaneous-endocrine (NICE) systems within the skin constitute a sophisticated communication network that encompasses neuropeptides, cytokines, neurotransmitters, as well as additional factors, notably psychological stress [ 24 ]. Given the pivotal role of neuroimmune interactions in psoriasis pathophysiology, studying differentially expressed neuroimmune-related genes is crucial for gaining new insights and developing therapeutic approaches. Our previous research on denervation experiments in a psoriasis mouse model revealed significant changes in genes related to Th17 cell differentiation, TNF signaling, IL-1, and JAK-STAT signaling pathways [ 25 ], highlighting the need for further investigation into the regulation of psoriasis by human neuroimmune-related differentially expressed genes. In this study, through bioinformatics analysis, 68 neuroimmune-related differentially expressed genes (NRDEGs) of psoriasis patients were screened, and their functional enrichment was analyzed. A comprehensive approach to constructing psoriasis diagnostic models and exploring immune infiltration and drug sensitivity analysis was undertaken, aiming to explore key molecular pathways leading to psoriasis onset and establishing a foundation for early diagnosis and personalized treatment strategies. Our study has revealed the existence of a multitude of neuroimmune-related differentially expressed genes in psoriasis patients. Through GO, KEGG, and GSEA analyses, our analysis demonstrated substantial enrichment of the differentially expressed genes within various biological processes and signaling cascades, particularly within the IL-17 signaling pathway, TP53 signaling cascade, NF-κB signaling pathway, IL-23 pathway, Wnt signaling circuit, and TGF-β pathway [ 26 ]. The identification of these pathways is consistent with current understanding, and all of these pathways play important regulatory roles in psoriasis. It is noteworthy that the NF-kappaB signaling pathway is enriched in these NRDEGs, consistent with its established role in inflammation and autoimmune diseases [ 27 ]. The NF-κB signaling pathway influences the proliferation, differentiation, and secretion of cytokines and chemokines in both keratinocytes and immune cells, thereby modulating their activities and contributing to the persistent inflammatory state characteristic of psoriasis [ 28 ]. IL-23 initiates the secretion of interleukin-17 (IL-17) through the activation of intracellular signaling pathways, leading to Th17 cell activation, which subsequently plays a pivotal role in psoriasis development [ 29 ]. Analogously, research has indicated that Wnt-mediated signaling not only exacerbates the inflammatory state of psoriasis but also constitutes the foundation of disease susceptibility [ 30 ]. Additionally, therapeutic agents targeting IL-23 or IL-17A have been formulated for the management of moderate to severe psoriasis, offering tangible proof of their involvement in the pathophysiology of the disease [ 31 , 32 ]. Through the construction of a psoriasis diagnostic model and inflammation, the study eventually identified 5 key genes, ENO2, SELL, GREM2, IL1B, and LCN2. Several studies have reported close associations of these 5 key genes with psoriasis and neuroimmune interactions. Lipocalin-2 (LCN2) is a critical lipid carrier protein involved in lipid metabolism, immune response, and neurodegeneration. It has been shown to be rapidly produced by spinal cord astrocytes in response to infection, inflammation, or injury [ 33 ]. By activating inflammatory pathways and regulating iron homeostasis, LCN2 plays a key role in modulating cellular responses [ 34 ]. Remarkably, its concentrations are notably elevated in psoriasis patients and exhibit a correlation with Visual Analog Scale (VAS) scores, implying a potential role in disease progression through modulation of neutrophil activity [ 35 ]. This observation suggests that LCN2 may function as both a diagnostic marker and a promising therapeutic target for psoriasis-related inflammation. Our research concurs with these findings, reinforcing LCN2's status as a pivotal gene, thus further solidifying its critical involvement in psoriasis pathogenesis and enhancing the credibility of our results. Interleukin-1 Beta (IL-1β), a well-known pro-inflammatory cytokine, is implicated in promoting neurite outgrowth and nerve regeneration. It has the capacity to activate dermal γδ T cells and stimulate keratinocytes in psoriasis, ultimately intensifying the inflammatory response [ 36 – 38 ]. Our research identifies IL1B as a key gene, consistent with existing literature, reaffirming the central role of IL-1β in psoriasis and neuroimmune interactions. Neuron-Specific Enolase (ENO2), a rate-limiting enzyme in glycolysis found in neurons and neuroendocrine tissues, participates in neuroinflammatory responses, neurodegeneration, and neuroprotection through the PI3K and MAPK signaling pathways [ 39 , 40 ]. It is involved in glycolytic changes associated with psoriasis, emphasizing its importance in neurobiology and immunology [ 41 ]. Our research is the pioneer in establishing a connection between ENO2 and psoriasis, suggesting that this gene may be significant not only in neurological disorders but also in the pathological mechanisms of psoriasis. This groundbreaking discovery deepens the comprehension of psoriasis pathogenesis. This novel finding enhances the understanding of the pathogenesis of psoriasis. Gremlin 2 (GREM2) is a cysteine knot secreted protein that regulates Bone Morphogenetic Proteins (BMPs), essential for embryonic development and tissue differentiation. It may promote skin homeostasis by inhibiting the differentiation of stem/progenitor cells [ 42 , 43 ]. Although its specific role in skin diseases remains unexplored, GREM2 becomes a promising therapeutic target for psoriasis. Our research, possibly involving the abnormal differentiation of skin cells. SELL (Selectin L): L-selectin (CD62L), widely expressed on leukocytes, mediates leukocyte activation, inflammation, and adhesion to endothelial cells, thereby playing a role in the pathogenesis of psoriasis [ 44 ]. Despite the lack of direct references to the SELL gene, the established functions of selectin family members in inflammation and cell adhesion imply a potential role for SELL in psoriasis. This research identifies SELL as a pivotal gene, possibly marking a novel discovery regarding its role in psoriasis, thereby necessitating further research to elucidate its specific mechanism. Conclusion: In summary, our research aligns with existing literature regarding the roles of LCN2 and IL1B, thus enhancing the reliability of the results. Additionally, our findings on ENO2, GREM2, and SELL provide new perspectives and potential therapeutic targets for psoriasis research, further demonstrating the innovation and significance of the study. These findings augment the comprehension of psoriasis pathogenesis, offering a broader perspective, and furthermore, they provide novel perspectives for the development of future treatment strategies. Extensive literature has documented the intricate interplay between immune system components and skin pathology in psoriasis, where a wide range of immune cells, such as T cells, dendritic cells, macrophages, natural killer cells, and neutrophils, play crucial roles in the disease progression [ 32 ]. Recent advancements have suggested that neurotransmitters and neurotrophic factors from the nervous system also contribute to psoriasis-associated inflammatory responses, mediated by immune cells such as Merkel cells, fibroblasts, mast cells, eosinophils, monocytes, neutrophils, T cells, and macrophages [ 45 , 46 ]. Building upon these insights, our research further substantiates the significant involvement of these diverse immune cells in psoriasis and delves deeper by identifying key genes intimately linked to these immune players. This underscores the complex relationship between neuroimmune responses and the pathogenesis of psoriasis. Through our analysis, we uncovered distinct neuroimmune score groups, each exhibiting unique immune profiles, albeit with some shared characteristics across immune cell types, thereby reinforcing the intricate interplay between immune cells and their genetic underpinnings in this multifaceted disease. Detailed immune infiltration analysis of these key genes revealed that ENO2, SELL, GREM2, LCN2, and IL1B could potentially influence the recruitment or activity of these immune cells. Notably, LCN2 emerged as a critical gene in our study. Our findings indicate a negative correlation between LCN2 and fibroblasts. Previous research has shown that LCN2 interacts with LRP6 in mouse embryonic fibroblasts, downregulating Wnt/β-catenin signaling and inhibiting BMP9-induced osteogenic differentiation [ 47 ]. Several studies have linked LCN2 to neutrophil function and inflammation in psoriasis [ 48 ]. For instance, Smith et al. (2018) reported elevated levels of LCN2 in psoriatic lesions, correlating with disease severity. Similarly, Johnson et al. (2020) demonstrated that LCN2 modulates neutrophil activity in psoriatic plaques. Our study aligns with these findings, suggesting a potential role for LCN2 in psoriasis pathogenesis through its interaction with fibroblasts. However, the exact mechanism by which LCN2 contributes to psoriasis via fibroblasts remains to be elucidated and warrants further investigation. This study has several limitations, including the lack of experimental validation for the bioinformatics predictions, a limited sample size that may limit the generalizability of the research findings, the lack of clinical validation for the diagnostic model, as well as the potential batch effects arising from the use of multiple datasets, are noted. Conclusion In summary, this study identified five crucial neuroimmune-related genes—ENO2, SELL, GREM2, IL1B, and LCN2—which are crucial in the immune dysregulation associated with psoriasis. These discoveries provide novel therapeutic targets and biomarkers, facilitating personalized diagnosis and treatment of psoriasis. Nevertheless, future research must address the limitations, such as the requirement for wet lab validation, sample size constraints, clinical applicability, and potential batch effects. Abbreviations AUC Area Under the Curve BMP Bone Morphogenetic Protein BP Biological Process (GO category) CC Cellular Component (GO category) CGRP Calcitonin Gene-Related Peptide CINE Cutaneous-Immuno-Neuro-Endocrine (system) CXCL Chemokine (C-X-C motif) Ligand DCA Decision Curve Analysis ENO2 Enolase 2 GEO Gene Expression Omnibus GINIP Gαi-Interacting Protein GO Gene Ontology GREM2 Gremlin 2 GSEA Gene Set Enrichment Analysis GSVA Gene Set Variation Analysis IC50 Half-Maximal Inhibitory Concentration IL-17 Interleukin-17 IL1B Interleukin-1 Beta IL-23 Interleukin-23 JAK-STAT Janus Kinase-Signal Transducer and Activator of Transcription KEGG Kyoto Encyclopedia of Genes and Genomes KM Kaplan-Meier LASSO Least Absolute Shrinkage and Selection Operator LCN2 Lipocalin-2 MAPK Mitogen-Activated Protein Kinase MCP Counter Microenvironment Cell Populations Counter MF Molecular Function (GO category) miRNA microRNA Nav1.8 Voltage-Gated Sodium Channel 1.8 NF-κB Nuclear Factor kappa B NIC Neuro-Immuno-Cutaneous (system) NICE Neuro-Immuno-Cutaneous-Endocrine (system) NRDEGs Neuroimmune-Related Differentially Expressed Genes OS Overall Survival PCA Principal Component Analysis PDE4 Phosphodiesterase 4 RBP RNA-Binding Protein ROC Receiver Operating Characteristic SELL Selectin L SP Substance P ssGSEA Single Sample Gene Set Enrichment Analysis SVM Support Vector Machine TF Transcription Factor TGFB Transforming Growth Factor Beta TNF-α Tumor Necrosis Factor alpha TP53 Tumor Protein 53 TREM2 Triggering Receptor Expressed on Myeloid Cells 2 TRPV1 Transient Receptor Potential Vanilloid 1 Tyk2 Tyrosine Kinase 2 Wnt Wingless/Integrated signaling pathway Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions : Conceptualization, B.Q. and Y.Z.; methodology, B.Q. and L.P.; software, B.Q. and L.P.; validation, B.Q., L.P and Y.H.; formal analysis, L.P. and F.F.J.; investigation, L.L. and F.F.J.; resources, Y.H.; data curation, L.L. and L.P.; writing—original draft preparation, B.Q. writing—review and editing, Y.Z.; visualization, L.P.; supervision, Y.Z.; project administration, B.Q. and Y.Z.; funding acquisition, B.Q. All authors have read and agreed to the published version of the manuscript. Bi Qin and Lu Peng contributed equally to this work and should be recognized as co-first authors. Funding: This work was supported by Chengdu medical research project (Grant numbers 202204122433). Author B.Qin has received research support from Chengdu Municipal Health Commission. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Acknowledgments: We would like to express our gratitude to the contributors of data GSE13355, GSE14905 and GSE55201, including volunteers, authors and GEO data maintainers Conflicts of Interest: Declare conflicts of interest or state “The authors declare no conflicts of interest.” Authors must identify and declare any personal circumstances or interest that may be perceived as inappropriately influencing the representation or interpretation of reported research results. Any role of the funders in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results must be declared in this section. 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Shao S, Fang H, Dang E, et al. Neutrophil Extracellular Traps Promote Inflammatory Responses in Psoriasis via Activating Epidermal TLR4/IL-36R Crosstalk. Front Immunol. 2019;10:746. 10.3389/fimmu.2019.00746 . Haque A, Polcyn R, Matzelle D, Banik NL. New Insights into the Role of Neuron-Specific Enolase in Neuro-Inflammation, Neurodegeneration, and Neuroprotection. Brain Sci. 2018;8(2):33. 10.3390/brainsci8020033 . Ye W, Tang Y, Dong X, et al. Predictive Value and Correlation of Neuron-Specific Enolase for Prognosis in Patients with Coma: A Systematic Review and Meta-Analysis. Eur Neurol. 2020;83(6):555–65. 10.1159/000509801 . Liu P, Zhou Y, Chen C, et al. Lysophosphatidylcholine facilitates the pathogenesis of psoriasis through activating keratinocytes and T cells differentiation via glycolysis. J Eur Acad Dermatol Venereol. 2023;37(7):1344–60. 10.1111/jdv.19088 . Braddock M, Quinn A. Targeting IL-1 in inflammatory disease: new opportunities for therapeutic intervention. 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PLoS ONE. 2015;10(4):e0121626. 10.1371/journal.pone.0121626 . Aarão TLS, de Sousa JR, Falcão ASC, Falcão LFM, Quaresma JAS. Nerve Growth Factor and Pathogenesis of Leprosy: Review and Update. Front Immunol. 2018;9:939. 10.3389/fimmu.2018.00939 . Jiang JH, Wang SY, Zhang J, et al. LCN2 Inhibits the BMP9-induced Osteogenic Differentiation through Reducing Wnt/β-catenin Signaling via Interacting with LRP6 in Mouse Embryonic Fibroblasts. Curr Stem Cell Res Ther. 2023;18(8):1160–71. 10.2174/1574888X18666230320091546 . Hau CS, Kanda N, Tada Y, et al. Lipocalin-2 exacerbates psoriasiform skin inflammation by augmenting T-helper 17 response. J Dermatol. 2016;43(7):785–94. 10.1111/1346-8138.13227 . Statements & Declarations Additional Declarations No competing interests reported. Supplementary Files Appendix1.xlsx Appendix4.xlsx Appendix3.xlsx Appendix2.xlsx Appendix5.xlsx AppendixS1.xlsx Appendix6.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8700962","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586551931,"identity":"37995a3a-9b2e-4d4b-9941-ec8551b74764","order_by":0,"name":"Bi Qin","email":"","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bi","middleName":"","lastName":"Qin","suffix":""},{"id":586551932,"identity":"56fee421-8afa-45f1-9222-81ad7fe8daf9","order_by":1,"name":"Lu Peng","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Peng","suffix":""},{"id":586551934,"identity":"6331eb06-cd0f-48f7-a89d-8403aeaa7a5a","order_by":2,"name":"Yuhua Huang","email":"","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Huang","suffix":""},{"id":586551936,"identity":"280df8bf-af86-4fd7-ae9b-64b6b95d1c01","order_by":3,"name":"Fangfang Jia","email":"","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Jia","suffix":""},{"id":586551938,"identity":"35cde0f0-93bb-4343-8e68-acbfbb2a3330","order_by":4,"name":"Li Luo","email":"","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Luo","suffix":""},{"id":586551939,"identity":"601e0fb1-9e57-4cdd-8d1a-8082cf533e15","order_by":5,"name":"Dandan Tong","email":"","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Tong","suffix":""},{"id":586551941,"identity":"e8b58b11-fcef-4058-abcf-aa679b5ce64a","order_by":6,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3RIQvCQBTA8TeEWU6tJ6IGv8DBYBY/zA7hkghimcnJYE2s81us2m48vHSYBQVNJoM2bW5dtoHFcP/+4927B2Ay/WF2K5TyweiyBZheAWQ5aVLF09gfee2VGrNKpAeTARItPBZql1YiNmhItxFOma3E4uWfeVDHfVJIamspnxHOhwTViegbD4gQx+IpBy+fYu1iJU5WhDygxC0hE4aNjCSXuzt7VybZ+jyR2oVGJUKVl3+y0w7UuEM0OlHZLv1NiI/slL38lM+Xj91NHVUh+fZUk8lkMv3cB7fkXP0akOHlAAAAAElFTkSuQmCC","orcid":"","institution":"Institution of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-01-26 13:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8700962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8700962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102208050,"identity":"5fc754a4-8b84-4c7b-947a-4fd4ecb4dcd1","added_by":"auto","created_at":"2026-02-09 12:07:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259768,"visible":true,"origin":"","legend":"\u003cp\u003eTechnology Roadmap. PCA: Principal Component Analysis. GSEA: Gene Set Enrichment Analysis. GSVA: Gene Set Variation Analysis. DEGs: Differentially Expressed Genes. NRGs: Neuroimmunology-Related Genes. NRDEGs: Neuroimmunology-Related Differentially Expressed Genes. GO: Gene Ontology. KEGG: Kyoto Encyclopedia of Genes and Genomes. LASSO: Least Absolute Shrinkage and Selection Operator. RF: RandomForest. SVM: Support Vector Machine. TF: transcription factor. RBP: RNA-binding protein. ROC: Receiver Operating Characteristic. MCPCounter: Microenvironment Cell Populations-counter.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/5a9c36d4d82c04e31f980c02.jpg"},{"id":102296991,"identity":"346e5d20-97dd-4db1-982c-0337d0150b08","added_by":"auto","created_at":"2026-02-10 10:24:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34454,"visible":true,"origin":"","legend":"\u003cp\u003eDebatching of the dataset. a-b. boxplot plot of Combined GEO Datasets before (a) and after (b) normalization. c-d. PCA plots of Combined GEO Datasets before (c) and after (d) batch effect removal processing. PCA: Principal Component Analysis. Blue represents the dataset GSE13355, red represents the dataset GSE14905, and purple represents the dataset GSE55201.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/80180bb684b2f45f18b4991c.jpg"},{"id":102297069,"identity":"449d4616-b3ac-418c-942d-ed2d2dfb3fdb","added_by":"auto","created_at":"2026-02-10 10:25:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151367,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression Analysis. a. Volcano plot of differentially expressed genes analysis between Psoriasis group and Normal group in Combined GEO Datasets. b. Differentially expressed genes (DEGs) and neuroimmune-related genes ((NRGs) Venn diagram in Combined GEO Datasets. c. Differential ranking map of Neuroimmunology-Related Differentially Expressed Genes (NRDEGs) in Combined GEO Datasets.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/f2d70bd04f44336e46a4809d.jpg"},{"id":102297181,"identity":"c6a06fe4-52c7-4332-870e-c18beb7c8e7b","added_by":"auto","created_at":"2026-02-10 10:26:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":564030,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG Enrichment Analysis for NRDEGs. a-b. Functional (GO) and pathway (KEGG) enrichment analysis results of Neuroimmunology-Related Differentially Expressed Genes (NRDEGs) presented in bubble plot: biological process (BP), cellular component (CC), molecular function (MF) and biological pathway. GO terms and KEGG terms are shown on the abscissa. c-d. Network diagram of GO and KEGG enrichment analysis results of NRDEGs. The lines represent the relationship between items and molecules. Red nodes represent items, blue nodes represent molecules. The larger the nodes, the more molecules the entries contain. e-f. Bar graph of GO and KEGG enrichment analysis results of NRDEGs.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/4b2cfafa5cd1c5029d52f6d5.jpg"},{"id":102297620,"identity":"e7e37b88-c813-48f2-a726-7f609ebc87cb","added_by":"auto","created_at":"2026-02-10 10:28:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":338169,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA for Psoriasis. a. Gene set enrichment analysis (GSEA) 5 biological functions mountain map display of Combined GEO Datasets. b-f. GSEA showed that Neuroimmunology-Related Differentially Expressed Genes (NRDEGs) were significantly enriched in Nf-Kb pathway (b), TP53 pathway (c), IL23 pathway (d), Wnt pathway (e). TGFB pathway (f). The screening criteria of GSEA was adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg (BH). The red color represents the Psoriasis group and the blue color represents the Normal group.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/999d8c5b8cf0c59b3f21e4af.jpg"},{"id":102297402,"identity":"376ac382-1c8b-48f6-bfae-ed277b3a0881","added_by":"auto","created_at":"2026-02-10 10:27:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":636520,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a diagnostic model for psoriasis. a. Forest Plot of 68 NRDEGs in Psoriasis diagnostic model. b. The number of genes with the lowest error rate obtained by SVM algorithm. c. The number of genes with the highest accuracy obtained by the SVM algorithm. d. Plot of the LASSO regression diagnostic model of NRDEGs in Combined Datasets. e. Variable trajectory plot of the LASSO diagnostic model. f. MeanDecreaseGini scatter plot of NRDEGs (in descending MeanDecreaseGini order). g. Cross-validation error plot. h. SVM algorithm, LASSO algorithm and random forest intersection Venn diagram. IncNodePurity: Increase in NodePurity. NRDEGs: Neuroimmunology-Related Differentially Expressed Genes. SVM: Support Vector Machine. LASSO: Least Absolute Shrinkage and Selection Operator. RF: RandomForest.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/e17c4192654adaa2b490fcf8.jpg"},{"id":102296912,"identity":"c527199e-0ec1-4560-8e0f-b7651a727d86","added_by":"auto","created_at":"2026-02-10 10:22:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":284482,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic and Validation Analysis of Psoriasis. a. Nomograms of Model Genes in Combined GEO Datasets in Psoriasis diagnostic models. b-c. Calibration Curve plot (b) and decision curve analysis (DCA) plot (c) of Model Genes in Combined GEO Datasets for Psoriasis (Psoriasis) diagnostic model. d. ROC analysis of linear predictors of Logistic regression models in Combined GEO Datasets. The ordinate of the Calibration Curve plot is the net benefit, and the abscissa is the Probability Threshold or Threshold Probability. DCA, Decision Curve Analysis; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve. *** represents a p value \u0026lt; 0.001, which is highly statistically significant. AUC values above 0.9 had high accuracy.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/17a1d2747a650a774631df1f.jpg"},{"id":102296840,"identity":"ca915b03-13cc-47f9-8410-b3b1bda7ab4d","added_by":"auto","created_at":"2026-02-10 10:22:09","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":419667,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis of Key Genes between cancer group and normal group in Combined Datasets. a. Group comparison diagram of Key Genes in Combined GEO Datasets between Normal group and Psoriasis group. b. Map of the localization of Key Genes in human chromosomes. c-g. ROC curve analysis of Key Genes LCN2 (c), ENO2 (d), GREM2 (e), SELL (f), and IL1B (g) in Combined Datasets. The symbol *** is equivalent to P \u0026lt; 0.001 and highly statistically significant. The closer the AUC in the ROC curve is to 1, the better the diagnostic effect is. The AUC had a certain accuracy in the range of 0.7-0.9. AUC \u0026gt; 0.9 had high accuracy. ROC: Receiver Operating Characteristic. AUC: Area Under Curve.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/09c71319fe4371e4a9ecf75c.jpg"},{"id":102297193,"identity":"8ec772de-029e-4e3a-9686-eaba58145d51","added_by":"auto","created_at":"2026-02-10 10:26:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":498771,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network of mRNA-miRNA, mRNA-RBP and mRNA-TF. a. Interaction network of Key Genes. b. Key gene-mirna interaction network. c. Key gene-RBP interaction network. d. Key gene-transcription factor interaction network. Yellow oval genes are mrnas. Blue ovals are mirnas, pink ovals are RBP, and green ovals are TFS. RBP: RNA-binding protein. TF: transcription factor.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/ccab236890962d1cf6acca7e.jpg"},{"id":102296927,"identity":"4fcb55c9-e6d5-463c-a4b7-70aaedcf3a37","added_by":"auto","created_at":"2026-02-10 10:22:54","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":453454,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Infiltration analysis in different Normal/Psoriasis groups. a. Group comparison plot of 28 immune cells under ssGSEA algorithm between different groups (Normal/Psoriasis) in Combined Datasets. b. Heat map showing the results of correlation analysis between Key Genes and the infiltration abundance of immune cells (p value \u0026lt; 0.05) calculated by ssGSEA algorithm. c. Heat map display of correlation analysis results between Key Genes and immune cell infiltration abundance calculated by MCPCounter algorithm. In the correlation heat map, the red circle represents the positive correlation between the genes and the infiltration abundance of immune cells. The larger the circle is, the stronger the correlation is. Blue circles represent the negative correlation between genes and the infiltrating abundance of immune cells, and the larger the circle, the stronger the correlation. The symbol * is equivalent to P \u0026lt; 0.05, indicating statistical significance. The symbol ** is equivalent to P \u0026lt; 0.01, indicating a high degree of statistical significance. ns: no significant differences. MDSC: Myeloid-derived suppressor cells. ssGSEA: single-sample gene-set enrichment analysis McPcounter: Microenvironment Cell Populations-counter.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/65782a77175479d0ec1f3a92.jpg"},{"id":102208066,"identity":"d9e58935-6952-4b88-9065-fa344d496635","added_by":"auto","created_at":"2026-02-09 12:07:28","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":652780,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of Key Genes in High/Low neuroimmune score groups. a. Group comparison map results of Key Genes between neuroimmune High/Low groups in Combined Datasets disease samples are shown. b. Display of co-expression heatmap results of Key Genes. c-f. ROC curves of Key Genes IL1B (c), SELL (d), LCN2 (e), ENO2 (f) in Combined Datasets disease patient samples between High/Low neuroimmune groups. g. Heatmap of gene set variation analysis (GSVA) results between High and Low neuroimmune groups (High/Low). GSVA, Gene Set Variation Analysis. The screening criteria of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg (BH). The red color represents the neuroimmune High group, and the blue color represents the neuroimmune Low group. The symbol *** is equivalent to P \u0026lt; 0.001 and highly statistically significant. The symbol ** is equivalent to P \u0026lt; 0.01 and represents a high degree of statistical significance. Ns: Neuroimmunology Score. ROC: Receiver operating characteristic curve. The closer the AUC in the ROC curve is to 1, the better the diagnostic effect is. The AUC between 0.7 and 0.9 had a certain accuracy.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/c4df208e35610aecd71845be.jpg"},{"id":102208067,"identity":"21ead6e5-f353-4df2-a6e0-4528fa1ab294","added_by":"auto","created_at":"2026-02-09 12:07:28","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":432710,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of disease-related subtypes of Psoriasis. a. Plot of consistent clustering (K=2) results of psoriasis disease in Combined Datasets. b. Presentation of PCA analysis results of two psoriasis disease subtypes (cluster1 and cluster2) in Combined Datasets. c-d. Delta plot of area under the CDF curve for different cluster numbers in the consensus cluster (c), cumulative distribution function (CDF) plot of the consensus cluster (d). e. Group comparison plots of five Key Genes in different subtypes of psoriasis disease in Combined Datasets. f-h. ROC curves of GREM2 (f), IL1B (g), ENO2 (h) between two psoriasis disease subtypes (cluster1 and cluster2). The symbol *** is equivalent to P \u0026lt; 0.001, indicating very significant statistical significance. The symbol * is equivalent to P \u0026lt; 0.05, indicating a certain degree of statistical significance. The closer the AUC in the ROC curve is to 1, the better the diagnostic effect is. When AUC was between 0.5-0.7, the accuracy was low, and when AUC was between 0.7-0.9, the accuracy was moderate. ROC: receiver operating characteristic curve. CDF: Cumulative Distribution Function.\u003c/p\u003e","description":"","filename":"Figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/7ea01f4abc22e919a076080b.jpg"},{"id":102297104,"identity":"e05a3b96-df9c-4fe6-adb5-94d9c93a90af","added_by":"auto","created_at":"2026-02-10 10:25:49","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":399331,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Infiltration analysis in High/Low neuroimmune scores groups. a. Group comparison plot of 28 immune cells in the Combined Datasets disease patient samples with High/Low neuroimmune scores(ssGSEA/McPcounter). b. Heat map showing the results of correlation analysis between Key Genes and the infiltration abundance of immune cells (p value \u0026lt; 0.05) calculated by ssGSEA algorithm. c. Heat map display of correlation analysis results between Key Genes and immune cell infiltration abundance calculated by MCPCounter algorithm. In the correlation heat map, the red circle represents the positive correlation between the genes and the infiltration abundance of immune cells. The larger the circle is, the stronger the correlation is. Blue circles represent the negative correlation between genes and the infiltrating abundance of immune cells, and the larger the circle, the stronger the correlation. The symbol * is equivalent to P \u0026lt; 0.05, indicating statistical significance. The symbol ** is equivalent to P \u0026lt; 0.01, indicating a high degree of statistical significance. ns: no significant differences. MDSC: Myeloid-derived suppressor cells. ssGSEA: single-sample gene-set enrichment analysis McPcounter: Microenvironment Cell Populations-counter.\u003c/p\u003e","description":"","filename":"Figure13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/c85470f0e6b094631a8b80dc.jpg"},{"id":102296902,"identity":"c5806119-ce0d-44b6-ad67-7eb6ce6ec43f","added_by":"auto","created_at":"2026-02-10 10:22:38","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":224651,"visible":true,"origin":"","legend":"\u003cp\u003eDrug Sensitivity Analysis. The results of drug sensitivity analysis of neuroimmune-related Key Genes based on CellMiner database were displayed. Red shows positive correlation, blue shows negative correlation.\u003c/p\u003e","description":"","filename":"Figure14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/142e7a466c1a8c1018701ff6.jpg"},{"id":102208068,"identity":"ea304f85-6370-48a2-81c6-02f89edaaa4e","added_by":"auto","created_at":"2026-02-09 12:07:28","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":518856,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Docking. a-c. Neuroimmune-related Key Genes ENO2 and FLOXURIDINE (a), neuroimmune-related Key Genes GREM2 and ALLOPURINOL (b), The docking results of neuroimmune-related Key Genes IL1B and PENTAMIDINE (c) were visualized, from left to right, respectively, the global docking map and the interaction force map. The surface color changes from green, orange, and red, indicating the change of amino acid properties from hydrophilic to hydrophobic. Blue dashed lines show hydrogen bonds, light blue dashed lines show weak hydrogen bonds, gray dashed lines show hydrophobic forces, yellow dashed lines show ionic bonds, and green dashed lines show π-π conjugation.\u003c/p\u003e","description":"","filename":"Figure15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/e09bfa58490bbb9412bacc7e.jpg"},{"id":108978271,"identity":"814f2ca3-6fc4-472d-aa46-572d41293603","added_by":"auto","created_at":"2026-05-11 11:35:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6437021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/38f2b41a-5cca-4611-905d-c71881adf7be.pdf"},{"id":102297513,"identity":"2e08859b-247b-49b4-93a1-bdf79c459536","added_by":"auto","created_at":"2026-02-10 10:27:56","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10454,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/2ca6ee5581284b7e5d081a24.xlsx"},{"id":102296899,"identity":"205c4f92-7309-491b-91c9-ef31e08684bd","added_by":"auto","created_at":"2026-02-10 10:22:37","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10479,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/95a1c8b0f0c732c176bb9e15.xlsx"},{"id":102297528,"identity":"f629e376-e8fd-47be-9574-d7a3f37cd41d","added_by":"auto","created_at":"2026-02-10 10:28:01","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9864,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/653cdc9626c4c2c9275ad894.xlsx"},{"id":102297088,"identity":"9604c6c0-3d6f-4546-a66c-018bad3520c6","added_by":"auto","created_at":"2026-02-10 10:25:37","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":15745,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/52da4ff87b55529b94c15f60.xlsx"},{"id":102296893,"identity":"7f9f52a8-8895-48d8-b235-7d206fc6f1b8","added_by":"auto","created_at":"2026-02-10 10:22:34","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10754,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/b20d156ecd0dde0d630c4fe2.xlsx"},{"id":102208063,"identity":"a8053ca5-3974-4c00-8005-90ff28688e89","added_by":"auto","created_at":"2026-02-09 12:07:27","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11249,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/2470deaa5dacd28a5725cb37.xlsx"},{"id":102208065,"identity":"3f47574e-c098-45c3-94e6-318b6f5673b6","added_by":"auto","created_at":"2026-02-09 12:07:28","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11248,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700962/v1/5483322b485ff587e55c93cf.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuroimmune-related differentially expressed genes in psoriasis: A bioinformatics analysis reveals potential biomarkers for diagnosis and treatment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePsoriasis is a persistent, relapsing, inflammatory, and systemic disorder, clinically manifested by the presence of scaly erythematous plaques or patches which can appear on any part of the body surface and may evolve into distinct subtypes, including psoriatic arthritis or erythrodermic psoriasis, with potential consequences of disability and life-threatening complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 125\u0026nbsp;million people globally are estimated to have psoriasis, leading to a significant deterioration in their quality of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Increasing evidence suggests that psoriasis, besides impacting the skin and joints, is also associated with neuroimmune psychiatric comorbidities, such as depression, anxiety, and psychosis. Psoriasis is widely recognized as an autoimmune disease that involves T-cell mediation, where the significant dysregulation of the IL-17/IL-23 pathway is crucial in the development of the disease, as evidenced by the effectiveness of targeted therapies aimed at T cells [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the occurrence of adverse drug reactions, diminishing long-term efficacy, and recurrence of skin lesions underscore the necessity for novel diagnostic and therapeutic approaches in psoriasis management.\u003c/p\u003e \u003cp\u003eThe current therapeutic landscape for psoriasis is dominated by biological agents and small molecule targeted drugs, including anti-TNF-α inhibitors, IL-17 blockers, IL-23 antagonists, PDE4 inhibitors, and Tyk2 inhibitors. These treatment modalities have exhibited remarkable efficacy in managing the disease. However, their use is not without adverse consequences; a portion of patients experience severe infections, injection site reactions, and an elevated risk of malignancies. Moreover, a subset of individuals displays primary or secondary non-response to these drugs or develops antidrug antibodies, necessitating a change in therapeutic approach. Additionally, the exorbitant cost of these therapies presents a significant barrier for many patients, impeding widespread access. Hence, there exists an imperative requirement to develop therapeutic options that are safer, more efficacious, and economically viable for psoriasis. Current research endeavors ought to prioritize tackling these challenges and offering a wider array of treatment alternatives specifically designed to cater to the diverse requirements of patients suffering from this chronic inflammatory skin condition.\u003c/p\u003e \u003cp\u003eRecent research has underscored the crucial importance of neuroimmune interactions in the pathophysiology of diverse diseases. Research has demonstrated that directional interactions occur between cutaneous nerve endings and immune cells. Specifically, neurons modulate immune responses via mediators like neuropeptides, and immune cells sensitize neurons [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Neuroimmune crosstalk is essential for regulating inflammation, facilitating tissue repair, and defending against pathogens [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, the resolution of psoriatic lesions following nerve damage reflects the significance of neuroimmune interactions in the diseases resolution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Psoriasis models have demonstrated that preemptive disruption of skin nerve innervation can mitigate lesion induction, highlighting the potential of neuroimmune research in developing new treatments [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough we have gained these insights, notable lacunae persist in our comprehension. For instance, the role of the cutaneous-immuno-neuro-endocrine (CINE) system in converting the skin into a \u0026lsquo;super organ\u0026rsquo; is acknowledged, but the exact processes by which the CINE system coordinates immune and neural signals in psoriasis are yet to be fully understood. Additionally, although cutaneous nerve fibers are known to link epidermal keratinocytes and immunocytes, the precise molecular pathways involved in these interactions are not fully understood. Moreover, the review of cases where psoriasis improved following denervation injury suggests a critical role for neural influences, yet the detailed mechanisms of neuro-immune interactions remain largely unexplored.\u003c/p\u003e \u003cp\u003eAdditional studies are essential to elucidate the intricate involved underlying the CINE system and neuroimmune crosstalk in psoriasis. Understanding these interactions at a molecular level will be vital for creating innovative, targeted treatments that can more efficiently control or possibly eradicate psoriasis. This underscores the necessity of continued investigation into neuroimmune interactions and their therapeutic potential in psoriasis.\u003c/p\u003e \u003cp\u003eAdditionally, neuroimmune dynamics exert a critical influence across various conditions, encompassing autoimmune disorders and neurodegenerative processes, and may potentially function as biomarkers for these pathologies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Environmental triggers can initiate neurogenic inflammation in psoriasis by activating cutaneous nerve channels [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The investigation of neuroimmune-related differentially expressed genes (NRDEGs) holds promise for elucidating the molecular underpinnings of psoriasis and informing therapeutic interventions.\u003c/p\u003e \u003cp\u003eAdvances in neuroimmunology over the past decade have focused on Nav+ TRPV1\u0026thinsp;+\u0026thinsp;neurons and neuropeptides such as CGRP and SP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recent discoveries have also emphasized the regulatory roles of Nav1.8\u0026thinsp;+\u0026thinsp;GINIP\u0026thinsp;+\u0026thinsp;non-peptidergic sensory neurons [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Transcriptomic analysis has uncovered a diverse array of steady-state skin sensory neurons that undergo significant reprogramming following axonal injury [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite these advances, the precise molecular mechanisms that govern neuroimmune system interactions remain incompletely understood, representing a frontier for future research.\u003c/p\u003e \u003cp\u003eOur research aims to identify neuroimmune-related differentially expressed genes (NRDEGs) in psoriasis through bioinformatics analysis, conduct functional enrichment analysis, and establish a diagnostic model. Furthermore, we aim to investigate key gene interactions within the contexts of immune infiltration and drug response to deepen us uncover new insights and potential targets for diagnosis and therapy. Specific methods are described in the Supplementary Information.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eThe technology roadmap is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection and correction\u003c/h2\u003e \u003cp\u003eThe sva package in R was utilized to eliminate batch effects from the Psoriasis datasets, namely GSE13355, GSE14905, and GSE55201, resulting in the Combined GEO datasets. Initially, the distribution boxplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b) was utilized to compare the expression values of the datasets before and after the elimination of batch effects. Subsequently, the PCA (Principal Component Analysis) plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d) was used to compare the distribution of low-dimensional features before and after the removal of batch effects. The results obtained from the distribution boxplot and PCA plot demonstrated a significant reduction in the batch effect within the Psoriasis dataset samples after batch removal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Neuroimmune-related differentially expressed genes associated with psoriasis\u003c/h2\u003e \u003cp\u003eThe Combined GEO Datasets were classified into two groups: a Psoriasis group and a Normal group. To investigate the differences in gene expression levels between the Psoriasis and Normal groups within the Combined GEO Datasets, the limma R package was employed to perform differential analysis, ultimately resulting in the identification of differentially expressed genes (DEGs) between the Psoriasis and Normal groups. The outcomes are detailed below: The Combined GEO Datasets encompassed an aggregate of 13598 genes which fulfilled the criteria of having an absolute logFC greater than 0 and an adjusted P-value less than 0.05, thereby suggesting the presence of differentially expressed genes (DEGs).\u003c/p\u003e \u003cp\u003eAmong these genes, 6048 demonstrated upregulation, distinguished by having a logFC above 0 and an adjusted P-value below 0.05, while 7550 genes showed downregulation, exhibiting a logFC below 0 and an adjusted P-value similarly less than 0.05, as illustrated in the variance analysis results presented in the dataset's volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). To identify NRDEGs, genes with an absolute logFC value exceeding 0 and an adjusted P-value below 0.05 were selected, subsequently, the intersection between DEGs and NRGs was determined, and then visualized using Wayne (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 68 NRDEGs were obtained, as presented in \u003cb\u003eAppendix 1\u003c/b\u003e. An analysis was conducted to evaluate the expression disparities of NRDEGs between the Psoriasis and Normal sample cohorts in the Combined GEO Datasets, using the R package pheatmap to visualize the disparity rankings (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), ultimately presenting the results of the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Gene ontology (GO) and pathway (KEGG) enrichment analysis\u003c/h2\u003e \u003cp\u003eTo further explore the relationships between biological processes (BP), cellular components (CC), molecular functions (MF), and biological pathways (KEGG) associated with the 68 neuroimmune-related differentially expressed genes (NRDEGs) identified in Psoriasis, gene ontology (GO) and pathway enrichment analyses were conducted. The 68 previously mentioned neuroimmune-related differentially expressed genes (NRDEGs) underwent gene ontology (GO) and pathway (KEGG) enrichment analysis, with the detailed outcomes presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of GO and KEGG Enrichment Analysis for NRDEGs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBgRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive regulation of NF-kappaB transcription factor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154/18800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0007259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereceptor signaling pathway via JAK-STAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173/18800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.24E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive regulation of MAPK cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e491/18800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0010507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative regulation of autophagy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85/18800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.32E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e370/18800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.24E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.23E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0009897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eexternal side of plasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e455/19594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.01E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0060205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecytoplasmic vesicle lumen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325/19594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0045121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emembrane raft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e326/19594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evesicle lumen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327/19594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0098857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emembrane microdomain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327/19594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esignaling receptor activator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e496/18410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.55E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.26E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0048018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereceptor ligand activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e489/18410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.38E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecytokine activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235/18410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecytokine receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e272/18410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.06E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglycosaminoglycan binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234/18410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.43E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNF-kappa B signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104/8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL-17 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94/8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.57E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.88E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNecroptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159/8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.65E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93/8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.36E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50/8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.68E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.73E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNRDEGs: Neuroimmunology-Related Differentially Expressed Genes. GO: Gene Ontology. BP: biological process. CC: cellular component. MF: molecular function. KEGG: Kyoto Encyclopedia of Genes and Genomes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur study's findings indicate that a significant proportion of the 68 neuroimmune-related differentially expressed genes (NRDEGs) demonstrate enrichment in positively regulating NF-kappaB transcription factor activity in the context of Psoriasis. In particular, these genes demonstrated enrichment in receptor signaling pathways mediated by JAK-STAT, promotion of the MAPK cascade and inhibition of autophagy are both categorized as biological processes (BP). Furthermore, enrichment was observed in a diverse array of cellular components, specifically, the external surface of the plasma membrane, as well as the lumen of cytoplasmic vesicles, membrane rafts, vesicular lumen, among numerous other components, all categorized under cellular components (CC). Additionally, these genes demonstrated enrichment across various molecular functions, such as signaling receptor activator activity, cytokine activity is included, receptor ligand activity, as well as cytokine receptor binding, among others, all classified under molecular functions (MF). Moreover, significant enrichment was detected in diverse biological pathways, specifically, the NF-kappa B signaling pathway, IL-17 signaling pathway, Necroptosis, Rheumatoid arthritis, Malaria, Lipid and atherosclerosis, The intestinal immune network pertaining to IgA production, among various pathways, are all classified within the Kyoto Encyclopedia of Genes and Genomes (KEGG). The outcomes of the enrichment analysis pertaining to gene ontology (GO) and pathway (KEGG) were visually depicted through bubble plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA network diagram was concurrently produced, illustrating biological processes (BP), cellular components (CC), emphasizing molecular functions (MF), and portraying biological pathways (KEGG), derived from the enrichment analysis related to gene ontology (GO) and pathway (KEGG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-d). The lines within the diagram denote the corresponding molecules and annotations for each entry, with larger nodes indicating a greater number of molecules present in those entries. Furthermore, a bar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f) was employed to showcase the results of the enrichment analysis for gene ontology (GO) and pathway (KEGG) pertaining to the combined logFC values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Gene Set Enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of expression levels of all genes in the Combined GEO Datasets on Psoriasis, GSEA was employed to explore the expression patterns displayed by these genes, along with the related biological processes involved. The relationship between the affected cellular components and their respective molecular functions, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, is further detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Our findings revealed that all genes within the GEO Datasets (Combined Datasets) demonstrated notable enrichment in the Nf-Kb pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), TP53 pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), IL23 pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), Wnt pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), and TGFB pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef), accompanied by other biologically relevant functions and signaling cascades.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of GSEA for Combined Datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSetSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnrichmentScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eqvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_SIGNALING_BY_TGFB_FAMILY_MEMBERS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.96E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.84E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWP_WNT_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.67E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePID_IL23_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.59E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.13E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.55E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_TP53_REGULATES_TRANSCRIPTION_OF_CELL_CYCLE_GENES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.80E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.27E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_TNFR2_NON_CANONICAL_NF_KB_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.07E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_M_PHASE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.88E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.88E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_INTERFERON_ALPHA_BETA_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.63E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.58E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.63E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.58E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.40E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.76E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGSEA: Gene Set Enrichment Analysis. NES: Normalized Enrichment Score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Construction of psoriasis diagnostic model\u003c/h2\u003e \u003cp\u003eFirstly, the diagnostic significance of the 68 neuroimmune-related differentially expressed genes (NRDEGs) in Psoriasis was evaluated, a univariate logistic regression model utilizing the 68 NRDEGs was constructed and its results were visualized through a Forest Plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The findings indicated that all 68 NRDEGs exhibited statistical significance in the logistic regression model (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in \u003cb\u003eAppendix 2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, a SVM (Support Vector Machine) model was developed using 68 neuroimmune-related differentially expressed genes (NRDEGs). The SVM algorithm was employed in this model to identify the number of genes associated with the lowest error rate (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) and to achieve the maximal accuracy (as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The results indicate that the SVM model attains optimal accuracy when the gene count is 20, and these 20 neuroimmune-related differentially expressed genes (NRDEGs) are: EXO1, ENO2, PPARG, CHRM3, SELL, TREM2, GREM2, IL1B, DGCR5, CXCL13, AQP4, CTLA4, TAC3, LCN2, IFIH1, NTF3, TNFSF13B, SELE, PINK1, F10.\u003c/p\u003e \u003cp\u003eSubsequently, the 68 neuroimmune-related differentially expressed genes (NRDEGs) were used as the basis, employing the LASSO regression analysis, a diagnostic model for Psoriasis was formulated. To facilitate visualization, the diagram of the LASSO regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) and the diagram illustrating the LASSO variable trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) were produced. The findings indicated that the LASSO regression model encompassed 21 NRDEGs, referred to as Model Genes, which were specifically: BACE1, CCL5, CHRM3, CTLA4, ECE2, ENO2, EXO1, GREM2, IL1B, KMO, LCN2, NTF3, PPARG, RGS2, SELE, SELL, TAC3, TGFB1, TNF, TNFSF13B, and TRPM7. Assessing the diagnostic potential of the 68 neuroimmune-related differentially expressed genes (NRDEGs) in psoriasis, the RandomForest algorithm was employed to examine the expression patterns of these NRDEGs across the merged datasets encompassing both psoriasis and normal cohorts. With a fixed seed of 234 and a specification of 200 decision trees, the decision tree error curve (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef) was produced. The findings revealed that the error plateaued at 11 decision trees. Subsequently, a MeanDecreaseGini scatter plot (portrayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg) for the 68 NRDEGs was constructed to identify pertinent genes. Notably, MeanDecreaseGini denotes the mean diminution of the Gini coefficient, which signifies the impurity of a node. A heightened Gini coefficient indicates reduced purity and increased impurities. Hence, MeanDecreaseGini reflects the average decrement in impurity of the variables segregating nodes across all trees. A larger MeanDecreaseGini value suggests that the gene holds greater significance in our Psoriasis/Normal classification, thereby exerting a more profound influence on psoriasis diagnosis. Following this, the optimal gene count was determined through five iterations of ten-fold cross-validation, accompanied by a cross-validation error plot. The plot illustrated that an error minimum was achieved with 11 genes, and the error tended to stabilize with an escalating gene count. In conjunction with the MeanDecreaseGini analysis, specific genes were then selected for further investigation. The outcomes pinpointed 11 NRDEGs with pivotal roles in psoriasis diagnosis: CXCL1, LCN2, LTF, CXCL13, ENO2, GREM2, IL1B, DPYSL2, IFIH1, CXCL8, and SELL.\u003c/p\u003e \u003cp\u003eTo identify the Key Genes, the intersection among the NRDEGs identified by the SVM model, LASSO regression model, and random forest was determined, resulting in a total of 5 Key Genes. The Venn diagram illustrating this intersection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh. The 5 identified Key Genes are: ENO2, SELL, GREM2, IL1B, and LCN2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Validation of diagnostic models for psoriasis\u003c/h2\u003e \u003cp\u003eIn order to further corroborate the diagnostic model for Psoriasis, utilizing the Model Genes as the foundation, a Nomogram was formulated, to illustrate the interplay among the Model Genes within the Combined GEO Datasets (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The results demonstrated that the expression level of the Model Gene SELL exhibited significantly higher utility compared to the other variables in the diagnostic model for Psoriasis. Conversely, the value of GREM2 expression in the diagnostic model for Psoriasis was significantly lower than that of the other variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the precision and discriminatory power of the Psoriasis diagnostic model, a Calibration Curve was subsequently generated through Calibration analysis. The predictive efficacy of the model was assessed by examining the alignment between actual and predicted probabilities across different conditions, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb. The analysis of the Calibration Curve indicated that the dotted line representing the calibration line showed minor deviation from the diagonal line of the optimal model, but was still largely congruent. To evaluate and demonstrate the clinical utility of the Psoriasis diagnostic models derived from the Combined GEO Datasets' Model Genes, Decision Curve Analysis (DCA) was utilized (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). The findings suggested that, within a specific range, the line representing the model exhibited superior stability when compared to the all positive and all negative lines, accompanied by a higher net benefit and demonstrated an overall better performance.\u003c/p\u003e \u003cp\u003eFurthermore, ROC curves were generated for the linear predictors obtained from the Logistic regression model, particularly for the distinct groups, namely Psoriasis and Normal, within the Combined GEO Datasets. The ROC curves were plotted, with the corresponding outcomes shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed. As illustrated in the figure, the Logistic regression model, utilizing the GEO dataset, exhibited robust diagnostic efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Differential expression analysis of Key Genes between cancer group and normal group in Combined Datasets\u003c/h2\u003e \u003cp\u003eThe Violin plot presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea illustrates the differential expression of five key genes, namely ENO2, SELL, GREM2, IL1B, and LCN2, across distinct groups (Normal/Psoriasis) within the Combined Datasets. The results obtained from our study revealed that the expression levels of five key genes exhibited statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the Normal and Psoriasis groups in the Combined Datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, the positions of five pivotal genes, namely ENO2, SELL, GREM2, IL1B, and LCN2, were examined on the human chromosomes using the R package RCircos, resulting in the generation of a chromosomal localization map (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Chromosomal mapping analysis indicated that SELL and GREM2 were both positioned on chromosome 1. The results further showed that IL1B was located on chromosome 2. Additionally, it was found that LCN2 resided on chromosome 9. Lastly, ENO2 was determined to be situated on chromosome 12.\u003c/p\u003e \u003cp\u003eLastly, the ROC curves for the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) within the Combined Datasets were plotted to present the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec-g). The ROC curve analysis revealed that the expression differences of the Key gene LCN2 demonstrated high accuracy in distinguishing between different groups (AUC\u0026thinsp;=\u0026thinsp;0.9). Furthermore, the expression differences of the remaining Key Genes (ENO2, SELL, GREM2, IL1B) within the Combined Datasets exhibited a moderate level of accuracy in discriminating among different groups, with AUC values ranging between 0.7 and 0.9.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Construct mRNA-miRNA, mRNA-RBP, mRNA-TF interaction network\u003c/h2\u003e \u003cp\u003eTo predict the functional similarities of the identified key genes, we utilized the GeneMANIA web platform and subsequently constructed an interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we utilized the miRTarBase and miRDB databases in order to pinpoint miRNAs that may potentially interact with our five key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Subsequently, the results obtained from both databases were intersected using Cytoscape software, allowing for the visualization of the mRNA-miRNA interaction network (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). Upon examination, the network was found to consist of two key genes (mRNA), IL1B and ENO2, accompanied by nine miRNA molecules, resulting in a total of ten mRNA-miRNA interaction pairs, as detailed in Supplementary Appendix 3.\u003c/p\u003e \u003cp\u003eEmploying mRNA-RBP data sourced from the ENCORI database, we undertook the prediction of RBPs interacting with the five designated key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Following this, we harnessed the outcomes extracted from the database and, using Cytoscape software, constructed and visualized the mRNA-RBP interaction network (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). A closer inspection of this mRNA-RBP interaction network disclosed that it encompassed the aforementioned five key genes and consisted of 58 RBP molecules, ultimately forming a total of 66 mRNA-RBP interaction relationships. For an exhaustive breakdown, kindly refer to Supplementary Appendix 4.\u003c/p\u003e \u003cp\u003eIn the final stage, we used the CHIPBase (version 3.0) and hTFtarget databases in order to identify transcription factors (TFs) potentially interacting with our five key genes (ENO2, SELL, GREM2, IL1B, LCN2). Afterwards, the shared outcomes were derived from both databases, and the mRNA-TF interaction network was visualized by employing Cytoscape software (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). Ultimately, this methodology yielded data concerning the interaction relationships between the five Key Genes and the 70 identified transcription factors (TFs). Specifically, a total of 91 mRNA-TF interaction pairs were identified, with detailed information provided in \u003cb\u003eAppendix 5\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Immune infiltration analysis (ssGSEA and MCPCounter)\u003c/h2\u003e \u003cp\u003eThe ssGSEA algorithm was utilized in order to examine the relationship among the sample expression profile data from 28 immune cells between distinct groups, namely Normal and Psoriasis, within the Combined Datasets. According to the outcomes of the immune infiltration analysis, group comparison boxplots were utilized (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) to demonstrate the abundance of infiltration of the 28 types of immune cells across the distinct groups, namely Normal and Psoriasis, within the Combined Datasets. The analysis conducted by us uncovered that a total of twenty-three immune cell types exhibited statistically significant disparities in expression levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across the Normal and Psoriasis groups within the amalgamated Datasets. Specifically, the identified immune cell types encompassed Activated B cells, as well as Activated CD4 and CD8 T cells and Activated dendritic cells, CD56bright and CD56dim natural killer cells, along with Central and Effector memory CD4 and CD8 T cells, Gamma delta T cells, Immature B cells, Macrophages, MDSCs, Memory B cells, and Monocytes, Natural killer T cells, Neutrophils, and Regulatory T cells, as well as T follicular helper cells, and Type 1, Type 2, and Type 17 T helper cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation heatmap presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb served to illuminate the relationships among the levels of immune cell infiltration abundance pertaining to five key genes (ENO2, SELL, GREM2, IL1B, LCN2) and the 20 statistically significant variables (with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It is noteworthy that the pivotal genes SELL, LCN2, and IL1B exhibited positive correlations with the majority of immune cells. In contrast, the pivotal gene ENO2 displayed negative correlations specifically with Gamma delta T cells and Neutrophils.Notably, the crucial genes SELL, LCN2, and IL1B demonstrated positive associations with a substantial proportion of immune cells.\u003c/p\u003e \u003cp\u003eUtilizing the MCPCounter algorithm, we evaluated the abundance of immune cell infiltration as well as the expression levels of five pivotal genes (ENO2, SELL, GREM2, IL1B, LCN2) across different groups (Normal/Psoriasis) within the Combined Datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec). Our findings indicated that the five key genes exhibited associations with ten distinct immune cell types, specifically T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic lymphocytes, B-cell lineage, and NK cells, monocytic lineage, myeloid dendritic cells, neutrophils, endothelial cells, as well as fibroblasts. It is worth noting that positive correlations were detected between the Key Gene IL1B and neutrophils, the Key Gene GREM2 and endothelial cells, and also between the Key Gene LCN2 and endothelial cells. Conversely, a negative correlation was identified between ENO2 and monocytic lineage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Neuroimmune score\u003c/h2\u003e \u003cp\u003eUtilizing the expression levels of five key genes (ENO2, SELL, GREM2, IL1B, LCN2) detected in psoriasis patient samples within the Combined Datasets, the Neuroimmunology Score (Ns) for each psoriasis patient in the Combined Datasets was subsequently derived through the application of the ssGSEA algorithm.\u003c/p\u003e \u003cp\u003eTo investigate the differences in gene expression profiles between the High and Low neuroimmunology score groups among the disease patient samples in the Combined Datasets, a Violin plot (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea) was utilized to visualize the expression disparities of the five key genes between the two neuroimmune score groups within the disease samples of the Combined Datasets. Analysis of the data uncovered that, in the combined datasets of Psoriasis patients, the expression levels of two key genes, namely SELL and IL1B, demonstrated significant statistical differences (with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when contrasting groups with high and low neuroimmune scores. Additionally, within the combined datasets of Psoriasis patients, the expression of the essential genes ENO2 and LCN2 demonstrated considerable statistical significance (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) when contrasting the high and low neuroimmune scoring cohorts. Furthermore, the study included a co-expression heatmap for these five pivotal genes, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the differences in gene expression profiles between the High and Low neuroimmunology score groups among the disease patient samples in the Combined Datasets, a Violin plot (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) was utilized to visualize the expression disparities of the five key genes between the two neuroimmune score groups within the disease samples of the Combined Datasets. Examination of the data revealed that, within the combined datasets of Psoriasis patients, the expression levels of two critical genes, SELL and IL1B, exhibited substantial statistical significance (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when comparing patient groups with high and low neuroimmune scores. Additionally, within the combined datasets of Psoriasis patients, the expression of the essential genes ENO2 and LCN2 demonstrated considerable statistical significance (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) when contrasting the high and low neuroimmune scoring cohorts. Furthermore, the study included a co-expression heatmap for these five pivotal genes, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003eSubsequently, ROC curves were constructed for the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) in order to compare the High and Low neuroimmune score groups within the disease samples of the Combined Datasets. For clarity, results with AUC values below 0.6 were excluded from the display (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ec-f). ROC curve analysis indicated that the Key Genes ENO2, SELL, IL1B, and LCN2 demonstrated a moderate degree of accuracy, as evidenced by AUC values between 0.7 to 0.9, in discriminating between High and Low neuroimmune score categories within the disease samples from the Combined Datasets.\u003c/p\u003e \u003cp\u003eIn order to investigate the disparities in the h.all.v7.4.symbols.gmt gene sets between the High and Low neuroimmune score groups within the Combined Datasets, a gene set variation analysis (GSVA) was conducted on all genes present in the Combined Datasets. The pertinent details regarding this analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The outcomes of the GSVA revealed that gene sets such as Inflammatory Response, IL-6 JAK-STAT3 Signaling, Apical Surface, Xenobiotic Metabolism, Elevated KRAS Signaling Activity, The Metabolism of Bile Acids, Delayed Estrogen Response, and Apoptosis, demonstrated statistically significant disparities among the High and Low neuroimmune score groups, significant differences were observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on the outcomes of the GSVA, the expression profiles that differentiate the High and Low neuroimmune score (High/Low) groups were examined and visually represented through a heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of GSVA for Combined Datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHallmark Gene Set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAveExpr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP.Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eadj.P.Val\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_INFLAMMATORY_RESPONSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.2459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.76E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.7691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.84E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_APICAL_SURFACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.4048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.46E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_XENOBIOTIC_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.3646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_KRAS_SIGNALING_UP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.3399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_BILE_ACID_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.14E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_ESTROGEN_RESPONSE_LATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.68E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHALLMARK_APOPTOSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.0805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.91E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGSVA: Gene Set Variation Analysis. logFC: Log2 fold change. AveExpr: Average expression. adj.P.Val: adjusted P value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Construct disease-related subtypes of psoriasis\u003c/h2\u003e \u003cp\u003eTo investigate the expression differences of the five Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) in the Combined Datasets, particularly in samples from Psoriasis patients, we employed the R package \"ConsensusClusterPlus\". Using the expression levels of these five Key Genes in the Combined Datasets, we ultimately identified two distinct psoriasis disease subtypes, labeled as cluster1 and cluster2, via consensus clustering (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea). Specifically, the psoriatic disease subtype 1, denoted as cluster1, encompassed a total of 104 samples, while the psoriatic disease subtype 2, designated as cluster2, consisted of 31 samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, a principal component analysis (PCA) was conducted on the expression matrix dataset derived from the two psoriasis disease subtype samples within the Combined Datasets. The PCA clustering results demonstrated significant differences between the two psoriasis disease subtype samples (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAdditionally, the Delta plot was presented, depicting the area under the curve (AUC) of the cumulative distribution function (CDF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ec), alongside the CDF plot itself (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed), showcasing varying cluster numbers within the consensus clustering results. As indicated by the figures, when the cluster count in unsupervised clustering was designated as k\u0026thinsp;=\u0026thinsp;2, the Combined Datasets yielded the most optimal consensus clustering outcomes.\u003c/p\u003e \u003cp\u003eSubsequently, the expression levels of the five key genes within the Combined Datasets were examined utilizing the Mann-Whitney U test, also referred to as the Wilcoxon rank sum test, with a focus on comparing the two distinct psoriasis disease subtypes, designated as cluster1 and cluster2. The outcomes of this analysis, emphasizing the disparities in expression, are depicted in a group comparison plot. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ee, the expression levels of three Key Genes\u0026mdash;GREM2, IL1B, and LCN2\u0026mdash;show marked statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when comparing cluster1 and cluster2, the two psoriasis disease subtypes in the Combined Datasets. In addition, the analysis detected a statistically significant disparity in the expression of the Key gene ENO2 between the two psoriasis disease subtypes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eConcurrently, we amalgamated the survival outcome (OS) and survival time (OS.time) data pertaining to samples from the two Psoriasis disease subtypes (cluster1 and cluster2), as well as the breast cancer Combined Datasets, to construct the survival Kaplan-Meier (KM) curve.\u003c/p\u003e \u003cp\u003eFollowing this, ROC curves were plotted for the five Key Genes to compare the two psoriasis disease subtypes, namely cluster1 and cluster2, within the Combined Datasets. Results with AUC values less than 0.6 were excluded from the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ef-h). The findings indicated that the Key gene GREM2 exhibited moderate accuracy (0.7\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.9) in distinguishing between the two psoriasis subtypes (cluster1 and cluster2). Conversely, the Key Genes IL1B and ENO2 demonstrated relatively low accuracy (0.5\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7) in differentiating between the two psoriasis subtypes (cluster1 and cluster2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Immune infiltration analysis (ssGSEA and MCPCounter)\u003c/h2\u003e \u003cp\u003eThe ssGSEA algorithm was utilized to analyze the correlation between the expression profiles of 28 immune cell types in disease patient samples categorized into High and Low neuroimmune score groups within the combined datasets. The abundance of immune cell infiltration, as determined by immune infiltration analysis, was depicted for the High/Low neuroimmune score groups using a grouped comparison box plot, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea. The analytical outcomes indicated that eight distinct immune cell types showed statistically significant expression differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) within the High/Low neuroimmune score groups in the disease samples from the Combined Datasets. In particular, the immune cells with significant expression were classified as activated CD4 T cells, activated B cells, activated dendritic cells, myeloid-derived suppressor cells (MDSCs), memory B cells, natural killer (NK) cells, Th1 cells, and Th17 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA correlation heatmap, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb, was utilized to visualize the relationship between immune cell infiltration abundance and the expression levels of the five key genes: ENO2, SELL, GREM2, IL1B, and LCN2. Specifically, our focus was directed towards the eight statistically significant correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified among these genes. Among the significant correlations observed, the Key Genes SELL, LCN2, and IL1B demonstrated positive associations with the majority of immune cells. In contrast, the Key gene ENO2 exhibited a negative correlation with most immune cells. The MCPCounter algorithm, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec, was utilized to assess the correlation among the five designated Key Genes (ENO2, SELL, GREM2, IL1B, LCN2) and varying levels of immune cell infiltration in patient samples from the Combined Datasets, which were categorized based on neuroimmune scoring as either High or Low. The analysis indicated an association between the five Key Genes and ten distinct immune cell types, including T cells, CD8 T cells, and cytotoxic lymphocytes, as well as B lineage cells, NK cells, and monocytic lineage cells, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts. Significantly, the study identified a positive correlation between IL1B and neutrophil levels, alongside a similar positive relationship between GREM2 and endothelial cell occurrence. Furthermore, LCN2 was positively correlated with fibroblast presence, reinforcing the previously established correlation of GREM2 with endothelial cells. Conversely, an inverse correlation was detected between ENO2 and the presence of NK cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13. Drug susceptibility analysis and molecular docking\u003c/h2\u003e \u003cp\u003eFurthermore, we employed the mRNA expression profiles of neuroimmune-related Key Genes alongside drug activity data derived from the CellMiner database of cancer drug sensitivity. Utilizing the pRRophetic algorithm, we predicted the sensitivity of neuroimmune-related Key Genes to common anticancer drugs, basing our predictions on the expression of these genes and applying the ridge regression model to calculate the IC50 value. Subsequently, we visually represented the correlation between neuroimmune-related Key Genes and small molecules of cancer drug sensitivity in the CellMiner database, as depicted in the following figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The analysis revealed the presence of 19 drugs with interactions in the CellMiner database, and notably, the neuroimmune-related Key Gene LCN2 exhibited a negative correlation with most of these drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Molecular docking\u003c/h2\u003e \u003cp\u003eThree neuroimmune-related Key Genes (ENO2, GREM2, IL1B) without corresponding drug small molecules were docked with their corresponding active ingredients by CB-Dock2, which were ENO2 and FLOXURIDINE, GREM2 and ALLOPURINOL, respectively. The docking results of neuroimmune-related Key Genes (IL1B, GREM2, IL1b) and PENTAMIDINE with their corresponding active components are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C.\u003c/p\u003e \u003cp\u003eThe neuroimmune-related Key Genes ENO2 and FLOXURIDINE (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ea) showed moderate binding (Vina Score = -5.8 Kcal/mol). The amino acids ASN1139, CYS1356, LYS1357, GLN1360, GLY1363, TRP1364, GLY1365, LEU1387, CYS1388, THR1389, ARG1428, ASN1429, PRO1430, SER1431 through hydrogen bonding, ionic bonding, π-π conjugation, Hydrophobic interaction with FLOXURIDINE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe neuroimmune-related Key Genes GREM2 and ALLOPURINOL (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eb) have moderate binding capacity (Vina Score = -4.9 Kcal/mol). ALA59, LEU60, VAL61, VAL62, THR63, GLU64, ARG65, LEU68 interacted with ALLOPURINOL through weak hydrogen bond, hydrogen bond, ionic bond, π-π conjugation, and hydrophobic interaction.\u003c/p\u003e \u003cp\u003eNeuroimmune-related Key Genes IL1B showed moderate binding with PENTAMIDINE (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ec) (Vina Score = -6.2 Kcal/mol), in which amino acids TYR24, GLU25, LEU26, LYS27, LEU69, LYS74, THR79, LEU80, GLN81, LEU82, SER125, MET130, PRO131, VAL132, PHE133, LEU134, ASP142 through weak hydrogen bonding, Hydrogen bonding, π-π conjugation, The hydrophobic force interacts with PENTAMIDINE.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003ePsoriasis, a persistent immune-related skin ailment, extends beyond skin damage, elevating the risks of cardiovascular illnesses, anxiety, depression, and other comorbidities, thus profoundly affecting patients' lives [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The current main treatment modalities for psoriasis include topical medications, traditional systemic drugs, biologics, and small molecule drugs. Notably, biologics and small molecule drugs have shown promising efficacy, but they still have numerous limitations, such as drug non-responsiveness, secondary failure, and drug resistance. These issues may be related to individual genetic variability, disease severity, and comorbidities, and there is currently a lack of predictive biomarkers for treatment response. In terms of secondary non-response, immune escape and disease progression are major concerns. The former refers to the development of anti-drug antibodies in patients, leading to drug failure, while the latter indicates a worsening of the condition over time, necessitating a change in treatment strategy. Additionally, there are concerns about side effects and safety, including an increased risk of infections and tumors, as well as limited long-term safety data. Therefore, delving deeper into the potential mechanisms of neuroimmunology in psoriasis holds significant importance. It is expected to provide new targets and methods for precision medicine in psoriasis, enhancing treatment efficacy and safety, filling gaps in current therapeutic options, and offering patients more personalized and effective treatment plans.\u003c/p\u003e \u003cp\u003eIntriguingly, psoriasis skin lesions often spontaneously resolve in areas with nerve deficiency but recur upon nerve regeneration, a phenomenon also observed in psoriasis mouse models [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The increased nerve fibers and neurotrophic factors in psoriatic lesions underscore the nervous system's role in disease progression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], highlighting the link between psoriasis and the nervous system. The skin's neuroimmune network, involving both autonomic and sensory nerves, adapts to environmental changes through neuropeptides, which not only influence skin cells but are also produced by them, potentially stimulating nerve fibers and exacerbating inflammatory responses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The neuro-immuno-cutaneous (NIC) and neuro-immuno-cutaneous-endocrine (NICE) systems within the skin constitute a sophisticated communication network that encompasses neuropeptides, cytokines, neurotransmitters, as well as additional factors, notably psychological stress [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Given the pivotal role of neuroimmune interactions in psoriasis pathophysiology, studying differentially expressed neuroimmune-related genes is crucial for gaining new insights and developing therapeutic approaches. Our previous research on denervation experiments in a psoriasis mouse model revealed significant changes in genes related to Th17 cell differentiation, TNF signaling, IL-1, and JAK-STAT signaling pathways [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], highlighting the need for further investigation into the regulation of psoriasis by human neuroimmune-related differentially expressed genes.\u003c/p\u003e \u003cp\u003eIn this study, through bioinformatics analysis, 68 neuroimmune-related differentially expressed genes (NRDEGs) of psoriasis patients were screened, and their functional enrichment was analyzed. A comprehensive approach to constructing psoriasis diagnostic models and exploring immune infiltration and drug sensitivity analysis was undertaken, aiming to explore key molecular pathways leading to psoriasis onset and establishing a foundation for early diagnosis and personalized treatment strategies.\u003c/p\u003e \u003cp\u003eOur study has revealed the existence of a multitude of neuroimmune-related differentially expressed genes in psoriasis patients. Through GO, KEGG, and GSEA analyses, our analysis demonstrated substantial enrichment of the differentially expressed genes within various biological processes and signaling cascades, particularly within the IL-17 signaling pathway, TP53 signaling cascade, NF-κB signaling pathway, IL-23 pathway, Wnt signaling circuit, and TGF-β pathway [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The identification of these pathways is consistent with current understanding, and all of these pathways play important regulatory roles in psoriasis. It is noteworthy that the NF-kappaB signaling pathway is enriched in these NRDEGs, consistent with its established role in inflammation and autoimmune diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The NF-κB signaling pathway influences the proliferation, differentiation, and secretion of cytokines and chemokines in both keratinocytes and immune cells, thereby modulating their activities and contributing to the persistent inflammatory state characteristic of psoriasis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. IL-23 initiates the secretion of interleukin-17 (IL-17) through the activation of intracellular signaling pathways, leading to Th17 cell activation, which subsequently plays a pivotal role in psoriasis development [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Analogously, research has indicated that Wnt-mediated signaling not only exacerbates the inflammatory state of psoriasis but also constitutes the foundation of disease susceptibility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, therapeutic agents targeting IL-23 or IL-17A have been formulated for the management of moderate to severe psoriasis, offering tangible proof of their involvement in the pathophysiology of the disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Through the construction of a psoriasis diagnostic model and inflammation, the study eventually identified 5 key genes, ENO2, SELL, GREM2, IL1B, and LCN2. Several studies have reported close associations of these 5 key genes with psoriasis and neuroimmune interactions.\u003c/p\u003e \u003cp\u003eLipocalin-2 (LCN2) is a critical lipid carrier protein involved in lipid metabolism, immune response, and neurodegeneration. It has been shown to be rapidly produced by spinal cord astrocytes in response to infection, inflammation, or injury [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. By activating inflammatory pathways and regulating iron homeostasis, LCN2 plays a key role in modulating cellular responses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Remarkably, its concentrations are notably elevated in psoriasis patients and exhibit a correlation with Visual Analog Scale (VAS) scores, implying a potential role in disease progression through modulation of neutrophil activity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This observation suggests that LCN2 may function as both a diagnostic marker and a promising therapeutic target for psoriasis-related inflammation. Our research concurs with these findings, reinforcing LCN2's status as a pivotal gene, thus further solidifying its critical involvement in psoriasis pathogenesis and enhancing the credibility of our results. Interleukin-1 Beta (IL-1β), a well-known pro-inflammatory cytokine, is implicated in promoting neurite outgrowth and nerve regeneration. It has the capacity to activate dermal γδ T cells and stimulate keratinocytes in psoriasis, ultimately intensifying the inflammatory response [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our research identifies IL1B as a key gene, consistent with existing literature, reaffirming the central role of IL-1β in psoriasis and neuroimmune interactions. Neuron-Specific Enolase (ENO2), a rate-limiting enzyme in glycolysis found in neurons and neuroendocrine tissues, participates in neuroinflammatory responses, neurodegeneration, and neuroprotection through the PI3K and MAPK signaling pathways [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It is involved in glycolytic changes associated with psoriasis, emphasizing its importance in neurobiology and immunology [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our research is the pioneer in establishing a connection between ENO2 and psoriasis, suggesting that this gene may be significant not only in neurological disorders but also in the pathological mechanisms of psoriasis. This groundbreaking discovery deepens the comprehension of psoriasis pathogenesis. This novel finding enhances the understanding of the pathogenesis of psoriasis. Gremlin 2 (GREM2) is a cysteine knot secreted protein that regulates Bone Morphogenetic Proteins (BMPs), essential for embryonic development and tissue differentiation. It may promote skin homeostasis by inhibiting the differentiation of stem/progenitor cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Although its specific role in skin diseases remains unexplored, GREM2 becomes a promising therapeutic target for psoriasis. Our research, possibly involving the abnormal differentiation of skin cells. SELL (Selectin L): L-selectin (CD62L), widely expressed on leukocytes, mediates leukocyte activation, inflammation, and adhesion to endothelial cells, thereby playing a role in the pathogenesis of psoriasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Despite the lack of direct references to the SELL gene, the established functions of selectin family members in inflammation and cell adhesion imply a potential role for SELL in psoriasis. This research identifies SELL as a pivotal gene, possibly marking a novel discovery regarding its role in psoriasis, thereby necessitating further research to elucidate its specific mechanism.\u003c/p\u003e \u003cp\u003eConclusion: In summary, our research aligns with existing literature regarding the roles of LCN2 and IL1B, thus enhancing the reliability of the results. Additionally, our findings on ENO2, GREM2, and SELL provide new perspectives and potential therapeutic targets for psoriasis research, further demonstrating the innovation and significance of the study. These findings augment the comprehension of psoriasis pathogenesis, offering a broader perspective, and furthermore, they provide novel perspectives for the development of future treatment strategies.\u003c/p\u003e \u003cp\u003eExtensive literature has documented the intricate interplay between immune system components and skin pathology in psoriasis, where a wide range of immune cells, such as T cells, dendritic cells, macrophages, natural killer cells, and neutrophils, play crucial roles in the disease progression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Recent advancements have suggested that neurotransmitters and neurotrophic factors from the nervous system also contribute to psoriasis-associated inflammatory responses, mediated by immune cells such as Merkel cells, fibroblasts, mast cells, eosinophils, monocytes, neutrophils, T cells, and macrophages [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Building upon these insights, our research further substantiates the significant involvement of these diverse immune cells in psoriasis and delves deeper by identifying key genes intimately linked to these immune players. This underscores the complex relationship between neuroimmune responses and the pathogenesis of psoriasis. Through our analysis, we uncovered distinct neuroimmune score groups, each exhibiting unique immune profiles, albeit with some shared characteristics across immune cell types, thereby reinforcing the intricate interplay between immune cells and their genetic underpinnings in this multifaceted disease.\u003c/p\u003e \u003cp\u003eDetailed immune infiltration analysis of these key genes revealed that ENO2, SELL, GREM2, LCN2, and IL1B could potentially influence the recruitment or activity of these immune cells. Notably, LCN2 emerged as a critical gene in our study. Our findings indicate a negative correlation between LCN2 and fibroblasts. Previous research has shown that LCN2 interacts with LRP6 in mouse embryonic fibroblasts, downregulating Wnt/β-catenin signaling and inhibiting BMP9-induced osteogenic differentiation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have linked LCN2 to neutrophil function and inflammation in psoriasis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For instance, Smith et al. (2018) reported elevated levels of LCN2 in psoriatic lesions, correlating with disease severity. Similarly, Johnson et al. (2020) demonstrated that LCN2 modulates neutrophil activity in psoriatic plaques. Our study aligns with these findings, suggesting a potential role for LCN2 in psoriasis pathogenesis through its interaction with fibroblasts. However, the exact mechanism by which LCN2 contributes to psoriasis via fibroblasts remains to be elucidated and warrants further investigation.\u003c/p\u003e \u003cp\u003eThis study has several limitations, including the lack of experimental validation for the bioinformatics predictions, a limited sample size that may limit the generalizability of the research findings, the lack of clinical validation for the diagnostic model, as well as the potential batch effects arising from the use of multiple datasets, are noted.\u003c/p\u003e"},{"header":" Conclusion","content":" \u003cp\u003eIn summary, this study identified five crucial neuroimmune-related genes\u0026mdash;ENO2, SELL, GREM2, IL1B, and LCN2\u0026mdash;which are crucial in the immune dysregulation associated with psoriasis. These discoveries provide novel therapeutic targets and biomarkers, facilitating personalized diagnosis and treatment of psoriasis. Nevertheless, future research must address the limitations, such as the requirement for wet lab validation, sample size constraints, clinical applicability, and potential batch effects.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Area Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Bone Morphogenetic Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Biological Process (GO category)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Cellular Component (GO category)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCGRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Calcitonin Gene-Related Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCINE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Cutaneous-Immuno-Neuro-Endocrine (system)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCXCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Chemokine (C-X-C motif) Ligand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Decision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eENO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Enolase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGINIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gαi-Interacting Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGREM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gremlin 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Gene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIC50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Half-Maximal Inhibitory Concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Interleukin-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Interleukin-1 Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Interleukin-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJAK-STAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Janus Kinase-Signal Transducer and Activator of Transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Kaplan-Meier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLCN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Lipocalin-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Mitogen-Activated Protein Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCP Counter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Microenvironment Cell Populations Counter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Molecular Function (GO category)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;microRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNav1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Voltage-Gated Sodium Channel 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNF-κB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Nuclear Factor kappa B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Neuro-Immuno-Cutaneous (system)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNICE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Neuro-Immuno-Cutaneous-Endocrine (system)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNRDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Neuroimmune-Related Differentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Overall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Principal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePDE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Phosphodiesterase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;RNA-Binding Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Receiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSELL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Selectin L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Substance P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003essGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Single Sample Gene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Support Vector Machine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Transcription Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTGFB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Transforming Growth Factor Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF-α\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Tumor Necrosis Factor alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Tumor Protein 53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTREM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Triggering Receptor Expressed on Myeloid Cells 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTRPV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Transient Receptor Potential Vanilloid 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTyk2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Tyrosine Kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWnt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Wingless/Integrated signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Conceptualization, B.Q. and Y.Z.; methodology, B.Q. and L.P.; software, B.Q.\u0026nbsp;and\u0026nbsp;L.P.; validation, B.Q., L.P and\u0026nbsp;Y.H.; formal analysis, L.P. and\u0026nbsp;F.F.J.; investigation, L.L.\u0026nbsp;and F.F.J.; resources,\u0026nbsp;Y.H.; data curation, L.L.\u0026nbsp;and\u0026nbsp;L.P.; writing—original draft preparation, B.Q. writing—review and editing, Y.Z.; visualization, L.P.; supervision, Y.Z.; project administration, B.Q. and Y.Z.; funding acquisition, B.Q. All authors have read and agreed to the published version of the manuscript. Bi Qin and Lu Peng contributed equally to this work and should be recognized as co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by Chengdu medical research project (Grant numbers 202204122433). Author B.Qin has received research support from Chengdu Municipal Health Commission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We would like to express our gratitude to the contributors of data GSE13355, GSE14905 and GSE55201, including volunteers, authors and GEO data maintainers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e Declare conflicts of interest or state “The authors declare no conflicts of interest.” Authors must identify and declare any personal circumstances or interest that may be perceived as inappropriately influencing the representation or interpretation of reported research results. Any role of the funders in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results must be declared in this section. If there is no role, please state “The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results”.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMichalek IM, Loring B, John SM. A systematic review of worldwide epidemiology of psoriasis. 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J Dermatol. 2016;43(7):785\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1346-8138.13227\u003c/span\u003e\u003cspan address=\"10.1111/1346-8138.13227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatements \u0026amp; Declarations\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Psoriasis, neuroimmune, machine learning, diagnostic model, network pharmacology, decision-curve analysis","lastPublishedDoi":"10.21203/rs.3.rs-8700962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8700962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePsoriasis is a chronic inflammatory skin disease in which neuroimmune interactions are increasingly recognized but remain insufficiently defined.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo elucidate neuroimmune mechanisms implicated in psoriasis pathogenesis and identify candidate biomarkers and therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePublicly available psoriasis transcriptomic datasets from the Gene Expression Omnibus (GEO) were analyzed. Neuroimmune-related differentially expressed genes (NRDEGs) were screened, followed by functional enrichment analyses. Supervised diagnostic models (logistic regression, support vector machine, least absolute shrinkage and selection operator [LASSO], and random forest) were constructed. Immune cell infiltration was estimated and correlated with NRDEG expression. Protein\u0026ndash;protein interaction networks were built, and key genes were queried against drug/active-ingredient resources.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixty-eight NRDEGs were identified in psoriatic tissue. These genes were significantly enriched in immune and inflammatory pathways, including NF-κB and IL-17 signaling. Diagnostic models based on NRDEGs achieved high predictive performance across algorithms. Five key genes\u0026mdash;ENO2, SELL, GREM2, IL1B, and LCN2\u0026mdash;were differentially expressed between patients and controls and showed strong associations with inferred immune cell infiltration in lesions. Network analyses prioritized hub genes and mapped them to potential drug active ingredients, suggesting avenues for pharmacologic modulation of neuroimmune pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIntegrated bioinformatics highlights a prominent neuroimmune signature in psoriasis, nominating NRDEGs\u0026mdash;particularly ENO2, SELL, GREM2, IL1B, and LCN2\u0026mdash;as candidate biomarkers for diagnosis and potential therapeutic intervention. Further experimental and clinical studies are warranted to define causal roles, clarify dynamics during disease progression, and evaluate responses to targeted therapies.\u003c/p\u003e","manuscriptTitle":"Neuroimmune-related differentially expressed genes in psoriasis: A bioinformatics analysis reveals potential biomarkers for diagnosis and treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:07:19","doi":"10.21203/rs.3.rs-8700962/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efaf3f68-c33a-435a-955c-4635db5ea914","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-11T06:15:12+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:29:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:07:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8700962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8700962","identity":"rs-8700962","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}