Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes and Immune Infiltration Characteristics in Psoriasis Patients 

Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes and Immune Infiltration Characteristics in Psoriasis Patients | 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 Article Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes and Immune Infiltration Characteristics in Psoriasis Patients Rui Yue, Peng Yang, Jie Xie, Xinxin Long, Aidi Xie, Yuxiang Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5326931/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 Psoriasis, a chronic and recurring disease, has a strong link to lipid metabolism. This study aims to identify differentially expressed genes (DEGs) and their functional pathways associated with psoriasis to uncover new therapeutic targets. We leveraged GEO datasets for DEGs analysis in psoriasis. GSEA, GSVA, WGCNA, & ssGSEA probed lipid metabolism genes' roles in psoriasis processes & immune changes. An RBP-mRNA network illuminated post-transcriptional regulatory mechanisms. Our findings revealed 3,839 DEGs, with 1,775 upregulated and 2,064 downregulated genes in psoriatic samples compared to controls. GSEA highlighted significant enrichment of immune-related pathways such as the cytosolic DNA sensing pathway and NOD-like receptor signaling pathway. WGCNA identified a black module strongly positive correlated with lipid metabolism containing 151 genes (r = 0.9216, p < 0.05). Enrichment analysis pointed to fatty acid metabolism and peroxisome pathways as critical in disease pathogenesis. Hub genes like AACS, HSD11B1 and GATA6 demonstrated high diagnostic potential with area under the ROC curve values exceeding 0.85. Immune infiltration analysis revealed significant differences in 27 types of immune cells between psoriasis and healthy control groups. Those findings provided a comprehensive molecular landscape of psoriasis, identifying potential new targets for therapeutic intervention and enhancing our understanding of the disease's underlying mechanisms. Biological sciences/Computational biology and bioinformatics Health sciences/Medical research Psoriasis Transcriptomics Lipid Metabolism Immune Infiltration RNA-Binding Proteins Bioinformatic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Psoriasis is a chronic, immune-mediated inflammatory skin disorder that affects approximately 2%-3% of the global population 1 . Characterized by red, scaly plaques on the skin, this condition is not only a cosmetic concern but also impacts the quality of the patients’ lives 2 . Despite the availability of treatments ranging from topical agents to systemic therapies, many patients experience inadequate responses or adverse effects, psoriasis remains incurable 3 . Research found that severe psoriasis are associated with an increased mortality risk of metabolic syndrome 4 . Consequently, there is an urgent need to identify novel therapeutic targets and elucidate the underlying mechanisms of psoriasis to develop more effective and safer treatments. Lipid metabolism as the central to psoriasis pathogenesis, influencing inflammatory cascades and keratinocyte proliferation 5 , abnormalities accumulate lipids, exacerbating skin inflammation 6 . Immune cells, lipids, and inflammatory mediators perpetuate a cycle of damage. Modulating lipid metabolism to disrupt this cycle holds promise for effective, targeted psoriasis therapies, enhancing patient quality of life. By integrating multiple datasets and employing advanced bioinformatics methods, this study seeks to provide a more detailed and reliable understanding of the molecular mechanisms driving psoriasis. The findings from this research are expected to contribute to the development of more effective treatments, ultimately improving the quality of life for patients suffering from this debilitating condition. Materials and Methods Data Sources and Preprocessing All the information utilized in this investigation is readily available to the public, primarily sourced from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). The psoriasis whole genome-wide expression profiles were retrospectively retrieved via the R package ‘GEOquery’ from the GEO repository. GSE30999 comprises skin biopsy samples from 85 psoriasis patients and 85 healthy controls. GSE54456 encompasses skin biopsy samples from 92 psoriasis patients and 82 healthy controls. Batch effects resulting from non-biological technical biases were rectified using the ComBat method from the R package “sva” 7 . Principal component analysis (PCA) was employed to assess the extent of correction. The current study adhered to the data access policies of each database. A comprehensive total of 1564 genes associated with lipid metabolism were retrieved from the Msigdb database ( http://www.gseamsigdb.org/gsea/msigdb/index.jsp) 8,9 (Table 1 ). Differentially Expressed Genes Associated with psoriasis Differentially expressed genes (DEGs) between control (n = 167) and psoriasis (n = 177) samples were discerned utilizing the “limma (version 3.50.0)” 10 package in R, applying the criteria of |log2Fold Change |> 0.5 and an adj.p < 0.05, which were incorporated in the subsequent research. Thereafter, the heatmap was produced using the R package “pheatmap” with Euclidean distance and complete linkage clustering methodology. Gene Set Enrichment Analysis (GSEA) The Gene Set Enrichment Analysis (GSEA) 8 is a computational technique that assesses whether a priori defined groups of genes exhibit statistically significant, concordant variations between two biological conditions. GSEA was executed utilizing the R package “clusterProfiler (version 4.2.2)” on a ranked list of all genes according to their log2Fold Change values. Gene set permutations were conducted 1,000 times for each analysis. The study selected c2.cp.kegg.v7.5.1.symbols from the Molecular Signatures Database (MSigDB) 8 , 9 , 11 as the reference gene collection. Gene sets with an adj.p < 0.05 were deemed significantly enriched. Gene Set Variation Analysis (GSVA) GSVA (Gene Set Variation Analysis) is an unsupervised and non-parametric method for gene set enrichment that enables the utilization of gene expression profiles to evaluate associations between biological pathways and gene characteristics. To explore the disparities in biological functions between the control and psoriasis cohorts, gene set variation analysis (GSVA) was conducted using “c2.cp.kegg.v7.5.1.symbols” with the R package “GSVA (version 1.42.0).” The R package “pheatmap (version 1.0.12)” was employed to illustrate the findings. Furthermore, we retrieved 50 hallmark gene sets from the MSigDB database ( http://software.broadinstitute.org/gsea/msigdb ) to serve as reference gene sets. We utilized the ssGSEA function within the GSVA package to compute the GSVA score for each gene set across different samples. Subsequently, the Limma package was used to analyze the differences in GSVA scores of various gene sets between the control and psoriasis groups. Weighted Gene Co‑expression Network Analysis (WGCNA) and Identification of Significant Modules Co-expression networks were formulated utilizing the WGCNA algorithm implemented in the R WGCNA package (version 1.70-3) 12 . The Pearson correlation coefficient was computed to evaluate the similarity of gene expression profiles, and subsequently, the correlation coefficients among genes were weighted by a power function to create a scale-free network. Utilizing the R package ‘PickSoftThreshold’, we established a weighted adjacency matrix by elevating the co-expression similarity to a power β = 8. A gene module is defined as a cluster of densely interconnected genes regarding co-expression. WGCNA employs hierarchical clustering to discern gene modules, with colors representing distinct modules. The dynamic tree cut method was employed to identify various modules; during module selection, the adjacency matrix (a metric of topological similarity) was transformed into a topology overlap matrix (TOM), and modules were detected through cluster analysis. To examine associations of modules with lipid metabolism, the relationships of the module eigengene (ME, the first principal component of the module, reflecting the overall expression level of the module) to lipid metabolism were calculated via Pearson’s correlation analysis. Modules exhibiting significant associations with lipid metabolism were identified. The co-expression module structure was illustrated through heatmap plots of topological overlap within the gene network. Interrelations among modules were summarized by a hierarchical clustering dendrogram of the eigengenes and by a heatmap plot of the corresponding eigengene network. The lipid metabolism-related differentially expressed genes (lipid metabolism-related DEGs) were derived from the intersection of DEGs and genes from the lipid metabolism-related module. GO and KEGG Pathway Enrichment Analysis Gene Ontology (GO) 13 enrichment analysis encompasses Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) assessments. The Kyoto Encyclopedia of Genes and Genomes (KEGG) 14 serves as a bioinformatics resource for investigating significantly modified metabolic pathways that are enriched within the gene list. The R package “clusterProfiler (version 4.2.2)” 15 was utilized to conduct GO and KEGG enrichment analysis (p < 0.05) on the lipid metabolism-related differentially expressed genes (DEGs). GeneMANIA The protein–protein interaction (PPI) networks of the central genes were established using the GeneMANIA website ( http://genemania.org ) 16 , which can also forecast the connections among functionally analogous genes and central genes, encompassing protein-protein interactions, protein-DNA interactions, pathways, physiological and biochemical processes, co-expression, and co-localization. The Receiver Operating Characteristic (ROC) Curve The receiver operating characteristic (ROC) curve, defined as a graphical representation of test sensitivity plotted as the y coordinate against its 1-specificity or false positive rate (FPR) as the x coordinate, serves as a robust method for assessing the efficacy of diagnostic tests. The most prevalent metric is the area-under-the-curve (AUC), derived from the receiver operating characteristic plot of sensitivity versus 1 – specificity. We employed the R package “pROC” 17 to generate ROC curves for determining the AUC of screening signature genes and appraising their diagnostic significance. This value is quantified on a continuum from 0.5 (indicative of a “coin flip”) to 1 (denoting perfect discrimination). Generally, an AUC of 0.5 signifies no predictive capability, 0.6 to 0.8 is deemed acceptable, 0.8 to 0.9 is classified as excellent, and exceeding 0.9 is regarded as exceptional. Immune Infiltration Analysis Single-sample Gene Set Enrichment Analysis (ssGSEA) 18 , a refinement of Gene Set Enrichment Analysis (GSEA), computes distinct enrichment scores for every combination of a sample and gene set. Each ssGSEA enrichment score signifies the extent to which the genes in a specific gene set are collectively up- or down-regulated within a sample. Single-sample GSEA (ssGSEA) is an enhancement of the GSEA algorithm that, rather than calculating enrichment scores for groups of samples (e.g., control vs. disease) and sets of genes (e.g., pathways), delivers a score for each sample and gene set pairing. Based on the 28 categories of immune cells obtained from the TISIDB (Tumor and Immune System Interactions Database) ( http://cis.hku.hk/TISIDB/index.php ) 19 , which include the following immune cell types: Activated CD8 T cell, Central memory CD8 T cell, Effector memory CD8 T cell, Activated CD4 T cell, Central memory CD4 T cell, Effector memory CD4 T cell, T follicular helper cell, Gamma delta T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, Regulatory T cell, Activated B cell, Immature B cell, Memory B cell, Natural killer cell, CD56 bright natural killer cell, CD56 dim natural killer cell, Myeloid derived suppressor cell, Natural killer T cell, Activated dendritic cell, Plasmacytoid dendritic cell, Immature dendritic cell, Macrophage, Eosinophil, Mast cell, Monocyte, and Neutrophil, the relative enrichment score of each immunocyte was quantified from the gene expression profile of each sample. Variations in the infiltration levels of immune cells among samples in the psoriasis and control groups were depicted using the R package ggplot2 (version 3.3.6) 20 . Construction of the RBP–mRNA network StarBase ( https://starbase.sysu.edu.cn/tutorialAPI.php#RBPTarget ), a widely utilized open-source platform for examining ncRNA interactions through CLIP-seq, degradome-seq, and RNA–RNA interactome data, was employed to explore the relationships between mRNA and RBP (RNA binding protein) expression. P < 0.05, clusterNum ≥ 5, and clipExpNum ≥ 5 were established as the threshold criteria for identifying the significant mRNA-RBP pairs in psoriasis. Subsequently, the RBP-mRNA network was constructed using Cytoscape. Statistical Analysis Statistical evaluation was conducted utilizing R software v4.1.2. The relationship between two variables was determined through Spearman’s correlation analysis. Inter-group variations were assessed using the Wilcoxon test, whereas the Kruskal–Wallis test was employed to compare differences among three or more groups. Two-sided p-values < 0.05 were regarded as statistically significant. Results DEGs Identification Through the analysis of psoriasis samples in comparison to controls, a total of 3,839 differentially expressed genes (DEGs) were identified as statistically significant between the two cohorts (adjusted p-value 0.5). In psoriasis samples, 1,775 genes were found to be upregulated, while 2,064 genes were downregulated (Table 2). All DEGs were illustrated using a volcano plot (Fig. 1 A). Moreover, the top five upregulated genes (PLA2G4D, VNN3, TMPRSS11D, S100A12, SERPINB4) alongside the top five downregulated DEGs (BTC, KRT77, BCAR3, RORC, SNTB1) were presented in a heatmap (Fig. 1 B). According to Wilcoxon tests, these top ten genes exhibited significant differences in expression levels between the two groups (p < 0.05, Fig. 1 C). GSEA To further investigate the potential mechanisms underlying the differentially expressed genes (DEGs), we conducted a Gene Set Enrichment Analysis (GSEA). Utilizing the MSigDB Collection, we identified the most significantly enriched signaling pathways based on their normalized enrichment score (NES) (Table 3). GSEA revealed that the CYTOSOLIC DNA SENSING PATHWAY (NES = 2.042, adj.p = 0.019, FDR = 0.014), NOD LIKE RECEPTOR SIGNALING PATHWAY (NES = 2.036, adj.p = 0.019, FDR = 0.014), PRIMARY IMMUNODEFICIENCY (NES = 2.036, adj.p = 0.019, FDR = 0.014), CARDIAC MUSCLE CONTRACTION (NES = -1.83, adj.p = 0.019, FDR = 0.014), HYPERTROPHIC CARDIOMYOPATHY (HCM) (NES = -1.921, adj.p = 0.019, FDR = 0.014), and DILATED CARDIOMYOPATHY (NES = -1.952, adj.p = 0.019, FDR = 0.014) (Fig. 2 A-F) were significantly enriched in psoriasis. GSVA To delve deeper into the functional annotation between psoriasis and control samples, we conducted GSVA analyses to assess the relative expression differences of the pathways in the two groups. GSVA analysis revealed a multitude of differentially expressed pathways, which were illustrated by the heatmap. In contrast to the control groups, the expression of pathways linked to KEGG_BASAL_CELL_CARCINOMA and KEGG_RIBOSOME was markedly diminished in psoriasis, while the expression of pathways associated with KEGG_DNA_REPLICATION and KEGG_HOMOLOGOUS_RECOMBINATION was significantly elevated (Fig. 2 G, Table 4). Weighted gene Co-expression Network Construction and Module Identification WGCNA was utilized to explore gene sets associated with lipid metabolism. The scale independence and mean connectivity analysis revealed that when the weighted value equaled 8 (Fig. 3 A), the average degree of connectivity approached 0, while scale independence exceeded 0.85. Thirteen co-expressed modules were identified, and uncorrelated genes were allocated to a gray module, which was disregarded in subsequent analyses (Fig. 3 B). To examine the interrelationships among modules and ascertain their correlation, we computed the MEs. The eigengene network, represented through a dendrogram and a heatmap plot, is illustrated (Fig. 3 C). To comprehend the physiological relevance of the modules, we correlated the 13 MEs with lipid metabolism and sought the most significant associations. According to the heatmap of module-trait correlation (Fig. 3 D), genes clustered in the black module (n = 151, Table 5) exhibited the strongest positive correlation with lipid metabolism (r = 0.9216, p < 0.05). Therefore, we will primarily focus on the black module moving forward, as it may more accurately reflect lipid metabolism. A cumulative total of 109 differentially expressed genes (DEGs) associated with lipid metabolism was derived from the overlap of DEGs and genes within the lipid metabolism-related module (Fig. 3 E, Table 6), which were regarded as pivotal genes. Analysis through Wilcoxon tests indicated that these top 10 genes exhibited significant disparities in expression levels between the two groups (p < 0.05, Fig. 3 F). Enrichment Analyses (GO/KEGG) To explore the biological roles of the lipid metabolism-associated DEGs, we conducted enrichment analyses for GO terms (Table 7) and KEGG pathways (Table 8). The GO findings revealed that these genes were significantly enriched in the fatty acid metabolic process (GO:0006631), long-chain fatty acid metabolic process (GO:0001676), fatty acid biosynthetic process (GO:0006633) (BP), peroxisome (GO:0005777), microbody (GO:0042579), peroxisomal membrane (GO:0005778), microbody membrane (GO:0031903) (CC), O-acyltransferase activity (GO:0008374), acyltransferase activity, transferring groups other than amino-acyl groups (GO:0016747), and acyltransferase activity (GO:0016746) (MF) (Fig. 4 A, 4 C- 4 E). Beyond the GO terms, the enriched KEGG pathways included Peroxisome (hsa04146), Fatty acid metabolism (hsa01212), and Glycerolipid metabolism (hsa00561) (Fig. 4 B, 4 F). Validation of the Hub Genes To further substantiate the diagnostic significance of pivotal genes, we conducted receiver operating characteristic (ROC) analysis. AACS (AUC = 0.941), HSD11B1 (AUC = 0.939), GATA6 (AUC = 0.916), PECR (AUC = 0.9), FA2H (AUC = 0.9), HACL1 (AUC = 0.898), TMEM56 (AUC = 0.89), ACOX2 (AUC = 0.889), THRSP (AUC = 0.886), DGAT2 (AUC = 0.885), LPIN1 (AUC = 0.884), DTX4 (AUC = 0.882), CRAT (AUC = 0.88), AGPAT1 (AUC = 0.879), PEX11A (AUC = 0.875), APOC1 (AUC = 0.864), TMPRSS11E (AUC = 0.863), INSIG1 (AUC = 0.862), ACSL1 (AUC = 0.861), FBP1 (AUC = 0.855), MGST1 (AUC = 0.854) were identified to possess comparable area under the ROC curve (AUC) values, regarded as hub genes (Fig. 5 A-L, Supplementary Fig. 1). This indicates that the recognized hub genes exhibited a satisfactory discriminatory capacity as prospective biomarkers for psoriasis. Trait Gene Interaction Analysis We utilized the GeneMANIA database to construct a PPI network for the signature genes, revealing 20 genes present within the PPI network (Fig. 6 A). To further explore the functions of the signature genes, GO and KEGG analyses were conducted on 41 genes, comprising 20 hub genes and 21 associated genes. The GO results indicate that these genes are significantly enriched in the fatty acid metabolic process (GO:0006631), triglyceride metabolic process (GO:0006641), acylglycerol metabolic process (GO:0006639), among others (Fig. 6 B, Table 9). The primary enriched pathways, as determined by KEGG analysis, included the Peroxisome (hsa04146), Glycerolipid metabolism (hsa00561), Fatty acid metabolism (hsa01212), and others (Fig. 6 C, Table 10). Immune Cells Infiltration The infiltration of immune cells may play a crucial role in the pathogenesis of psoriasis. Consequently, we explored the relationships between psoriasis and control samples concerning infiltrated immune cells. Among 28 varieties of immune cells, the infiltration levels of 27 types were markedly different between the two groups (p < 0.05) (Fig. 7 A, Table 11). For 20 types of immune cells (Activated CD8 T cell, Effector memory CD8 T cell, Activated CD4 T cell, T follicular helper cell, Gamma delta T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, Regulatory T cell, Activated B cell, Immature B cell, Memory B cell, CD56dim natural killer cell, Myeloid-derived suppressor cell, Natural killer T cell, Activated dendritic cell, Macrophage, Eosinophil, Monocyte, Neutrophil), a significantly elevated infiltration level was noted in the psoriasis group compared to the control group. As illustrated in Fig. 7 B, the overall infiltration levels of immune cells differed substantially between the psoriasis and control groups. Moreover, the notable correlations between each hub gene and the respective immune cells were also identified. It is important to highlight that HSD11B1 was markedly linked to Neutrophil (R = -0.781, p < 0.001) (Fig. 7 C); HSD11B1 was significantly correlated with Activated CD4 T cell (R = -0.736, p < 0.001) (Fig. 7 D). Signaling Pathways Involved in Signature Genes The disparities between psoriasis patients and controls across 50 HALLMARK signaling pathways were further examined utilizing GSVA. In psoriasis patients, 25 HALLMARK signaling pathways were notably up-regulated: HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_APOPTOSIS, HALLMARK_COMPLEMENT, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGETS, HALLMARK_G2M_CHECKPOINT, HALLMARK_GLYCOLYSIS, HALLMARK_IL2_STAT5_SIGNALING, HALLMARK_IL6_JAK_STAT3_SIGNALING, HALLMARK_INFLAMMATORY_RESPONSE, HALLMARK_INTERFERON_ALPHA_RESPONSE, HALLMARK_INTERFERON_GAMMA_RESPONSE, HALLMARK_MITOTIC_SPINDLE, HALLMARK_MTORC1_SIGNALING, HALLMARK_MYC_TARGETS_V1, HALLMARK_MYC_TARGETS_V2, HALLMARK_OXIDATIVE_PHOSPHORYLATION, HALLMARK_P53_PATHWAY, HALLMARK_PI3K_AKT_MTOR_SIGNALING, HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY, HALLMARK_SPERMATOGENESIS, HALLMARK_TNFA_SIGNALING_VIA_NFKB, HALLMARK_UNFOLDED_PROTEIN_RESPONSE, HALLMARK_UV_RESPONSE_UP, HALLMARK_XENOBIOTIC_METABOLISM. Twenty pathways were significantly down-regulated in psoriasis patients, including: HALLMARK_ADIPOGENESIS, HALLMARK_ANDROGEN_RESPONSE, HALLMARK_ANGIOGENESIS, HALLMARK_APICAL_JUNCTION, HALLMARK_BILE_ACID_METABOLISM, HALLMARK_CHOLESTEROL_HOMEOSTASIS, HALLMARK_COAGULATION, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_ESTROGEN_RESPONSE_EARLY, HALLMARK_FATTY_ACID_METABOLISM, HALLMARK_HEDGEHOG_SIGNALING, HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_DN, HALLMARK_MYOGENESIS, HALLMARK_NOTCH_SIGNALING, HALLMARK_PANCREAS_BETA_CELLS, HALLMARK_PEROXISOME, HALLMARK_TGF_BETA_SIGNALING, HALLMARK_UV_RESPONSE_DN, HALLMARK_WNT_BETA_CATENIN_SIGNALING (Fig. 8A, Table 12). We also assessed the correlations of the five most significant differentially expressed hub genes with the 50 HALLMARK signaling pathways (Fig. 8B). Figure 8. Correlation between hub genes and the 50 HALLMARK signaling pathways. (A) Comparison of the 50 HALLMARK signaling pathways between the psoriasis group and controls. (B) Correlation between the hub genes and the 50 HALLMARK signaling pathways. Asterisks represented p value (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05). Construction and Functional Annotation of the Crosstalk between the hub mRNAs and RBPs As RNA binding proteins (RBPs) associate with mRNA, we investigated 21 central mRNAs utilizing StarBase, resulting in the identification and acquisition of 16 mRNA/RBP pairs. Based on the connections between target genes presented in the online dataset, we established an RBP-mRNA network comprising 73 nodes, 57 RBPs, 16 mRNAs, and 345 edges. The specifics of the nodes and their interactions are detailed in Table 13, while the network is illustrated in Fig. 9 . Discussion Psoriasis is not only a skin disease but also a systemic condition with complex pathophysiology involving immune dysregulation and metabolic disturbances 4 . Previous studies 21 , 22 have shown that lipid metabolism plays a critical role in the pathogenesis of psoriasis, influencing inflammation and keratinocyte proliferation. By analyzing the expression profiles of lipid metabolism-related genes, we aim to uncover novel insights that could lead to more effective diagnostic and therapeutic strategies for psoriasis. The DEGs identified in this study provided crucial insights into the molecular mechanisms of psoriasis. Notably, genes such as PLA2G4D, VNN3, TMPRSS11D, and S100A12 were among the top differentially expressed genes. Genes like S100A12 and SERPINB4 have been reported in earlier studies 23 , 24 , suggesting their consistent involvement in the disease pathology and enhancing the validity of our results 25 , 26 . Furthermore, the upregulated and downregulated genes appear to cluster within specific pathways, such as immune response and keratinocyte differentiation, which are known to be critical in psoriasis. This clustering not only reinforces the relevance of our findings but also opens avenues for identifying novel therapeutic targets. Future studies should focus on validating these DEGs in larger cohorts and exploring their potential as biomarkers or therapeutic targets. Among the co-expressed modules identified by WGCNA, the black module showed the strongest positive correlation with lipid metabolism. The DEGs related to lipid metabolism included in this module are significantly enriched in processes such as fatty acid metabolism and peroxisome function. such as AACS and HSD11B1and GATA6. Meanwhile, ROC curve analysis demonstrated that those hub genes had high diagnostic value with AUC values greater than 0.85. they could serve as potential biomarkers for the early diagnosis of psoriasis, improving diagnostic accuracy. The high diagnostic value of these genes suggests their significant roles in the disease's pathogenesis. AACS is involved in the synthesis of acetoacetyl-CoA, a key intermediate in the synthesis of cholesterol and fatty acids, while HSD11B1 regulates glucocorticoid hormone action, which can influence inflammation and lipid metabolism 27 , 28 . The dysregulation of these processes may contribute to the altered skin barrier function and systemic inflammation observed in psoriasis patients. AACS (Acetoacetyl-CoA Synthetase) is a key enzyme involved in the synthesis of ketone bodies and cholesterol, playing a pivotal role in lipid metabolism 27 . In the context of psoriasis, the upregulation of AACS may reflect an altered lipid metabolic state, which is consistent with the observed dysregulation of fatty acid metabolism in our study. The significant association of AACS with psoriasis suggests that it may contribute to the pathogenesis of the disease by affecting the balance of lipid homeostasis in the skin, potentially serving as a novel therapeutic target. HSD11B1 (Hydroxysteroid 11-Beta Dehydrogenase 1) is involved in the conversion of inactive cortisone to its active form, cortisol, within cells. This enzyme has been implicated in the regulation of inflammatory responses, which are central to the pathophysiology of psoriasis 29 . Moreover, targeting HSD11B1 could modulate local cortisol levels and thus inflammatory pathways, offering a promising avenue for therapeutic intervention 28 . A research 30 found GATA6(GATA Binding Protein 6)regulates the expression of HIF as well as VEGF. The expression of GATA6 was lower in the psoriatic dermal MSCs (Mesenchymal stem cells) than in the control group. This indicates that the inflammatory cytokines presented in the psoriatic microenvironment might involve in the pathogenesis of psoriasis through influencing the angiogenesis-related gene. In our study, the significant enrichment of immune-related pathways such as the cytosolic DNA sensing pathway and NOD-like receptor signaling pathway in psoriasis patients underscores the pivotal role of innate immunity in the pathogenesis of the disease. The cytosolic DNA sensing pathway is crucial for the recognition of pathogen and host-derived DNA in the cytoplasm, which in turn activates a host defense response including the production of type I interferons 31 – 33 . Dysregulation of this pathway has been implicated in various autoimmune and inflammatory diseases, suggesting its potential involvement in the aberrant immune response observed in psoriasis 34 . Similarly, the NOD-like receptor signaling pathway is integral to the host's immune defense against a wide range of pathogens and is involved in the regulation of inflammation and apoptosis 35 . The enrichment of these pathways in our analysis indicates an ongoing immune response, possibly driven by the recognition of self-DNA and microbial components, leading to the chronic inflammation characteristic of psoriasis. Immune infiltration analysis revealed a significant increase in the infiltration of 20 types of immune cells in the psoriatic lesions compared to the control group, supporting the hypothesis that psoriasis is an autoimmune skin disease caused by abnormal activation and infiltration of immune cells into the skin. Among these, T helper (Th) cells, particularly Th1 and Th17, have been implicated in the pathogenesis of psoriasis, as they secrete pro-inflammatory cytokines that contribute to the inflammatory milieu characteristic of the disease 1 . The observed upregulation of the NOD-like receptor signaling pathway in our GSEA analysis is consistent with the role of these innate immune receptors in recognizing microbial motifs and endogenous danger signals, leading to the production of inflammatory cytokines and chemokines that can exacerbate psoriatic inflammation 2 . Furthermore, the significant enrichment of the cytosolic DNA sensing pathway suggests the involvement of antiviral defense mechanisms that may be aberrantly activated in psoriasis, contributing to the chronic inflammatory state 34 . The identification of these immune-related pathways and the differential infiltration of immune cells underscore the complex interplay between the innate and adaptive immune systems in psoriasis. Limitations Despite the comprehensive bioinformatics analysis conducted in this study, several limitations should be acknowledged. Firstly, the research was conducted purely through bioinformatics analysis without the integration of wet lab experiments, which could have provided additional validation for the computational predictions. Secondly, the sample size used in the analysis was relatively small, which may limit the generalizability of the results. Thirdly, there was a lack of clinical validation analysis to correlate the identified biomarkers with actual patient outcomes. Finally, the use of multiple datasets could introduce batch effects, even though efforts were made to correct for these using the ComBat method. Conclusion Our comprehensive bioinformatics analysis has identified significant differentially expressed genes and enriched pathways associated with psoriasis, providing new insights into its molecular mechanisms. The integration of multiple datasets and advanced analytical methods has revealed potential therapeutic targets, particularly in lipid metabolism and immune response pathways. These findings not only enhanced our understanding of psoriasis pathogenesis but also pave the way for the development of more effective treatment strategies. Declarations Competing interests The authors declare no competing interests. Author Contribution RY and XP performed the data analyses. PY, JX and XL interpreted the data. XC and AX reviewed the paper. XC, YH and LQ contributed to the revision stage. All authors approved the final manuscript. All authors reviewed the manuscript. Data Availability The datasets generated and/or analyzed during the current study are available in the GEO repository， including GSE30999 and GSE54456（https://www.ncbi.nlm.nih.gov/geo/）. References Guo, J. et al. Signaling pathways and targeted therapies for psoriasis. Signal. Transduct. Target. Ther. 8 , 437. 10.1038/s41392-023-01655-6 (2023). Ghoreschi, K., Balato, A., Enerbäck, C. & Sabat, R. Therapeutics targeting the IL-23 and IL-17 pathway in psoriasis. 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J Oncol 3140263, doi: (2022). 10.1155/2022/3140263 (2022). Ru, B. et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics . 35 , 4200–4202. 10.1093/bioinformatics/btz210 (2019). Ito, K. & Murphy, D. Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst. Pharmacol. 2 , e79. 10.1038/psp.2013.56 (2013). Rendon, A. & Schäkel, K. Psoriasis Pathogenesis and Treatment. Int. J. Mol. Sci. 20 10.3390/ijms20061475 (2019). Wójcik, P., Gęgotek, A., Žarković, N. & Skrzydlewska, E. Oxidative Stress and Lipid Mediators Modulate Immune Cell Functions in Autoimmune Diseases. Int. J. Mol. Sci. 22 10.3390/ijms22020723 (2021). Borsky, P. et al. Alarmins HMGB1, IL-33, S100A7, and S100A12 in Psoriasis Vulgaris. Mediators Inflamm 8465083, doi: (2020). 10.1155/2020/8465083 (2020). Izuhara, K. et al. Squamous Cell Carcinoma Antigen 2 (SCCA2, SERPINB4): An Emerging Biomarker for Skin Inflammatory Diseases. Int. J. Mol. Sci. 19 10.3390/ijms19041102 (2018). Wilsmann-Theis, D. et al. Among the S100 proteins, S100A12 is the most significant marker for psoriasis disease activity. J. Eur. Acad. Dermatol. Venereol. 30 , 1165–1170. 10.1111/jdv.13269 (2016). Sivaprasad, U. et al. SERPINB3/B4 contributes to early inflammation and barrier dysfunction in an experimental murine model of atopic dermatitis. J. Invest. Dermatol. 135 , 160–169. 10.1038/jid.2014.353 (2015). Bergstrom, J. D. The lipogenic enzyme acetoacetyl-CoA synthetase and ketone body utilization for denovo lipid synthesis, a review. J. Lipid Res. 64 , 100407. 10.1016/j.jlr.2023.100407 (2023). Staab, C. A. & Maser, E. 11beta-Hydroxysteroid dehydrogenase type 1 is an important regulator at the interface of obesity and inflammation. J. Steroid Biochem. Mol. Biol. 119 , 56–72. 10.1016/j.jsbmb.2009.12.013 (2010). Verma, M. et al. 11β-hydroxysteroid dehydrogenase-1 deficiency alters brain energy metabolism in acute systemic inflammation. Brain Behav. Immun. 69 , 223–234. 10.1016/j.bbi.2017.11.015 (2018). Hou, R. et al. Gene expression profile of dermal mesenchymal stem cells from patients with psoriasis. J. Eur. Acad. Dermatol. Venereol. 28 , 1782–1791. 10.1111/jdv.12420 (2014). Willemsen, J. et al. TNF leads to mtDNA release and cGAS/STING-dependent interferon responses that support inflammatory arthritis. Cell. Rep. 37 , 109977. 10.1016/j.celrep.2021.109977 (2021). Chen, Q., Sun, L. & Chen, Z. J. Regulation and function of the cGAS-STING pathway of cytosolic DNA sensing. Nat. Immunol. 17 , 1142–1149. 10.1038/ni.3558 (2016). Hopfner, K. P. & Hornung, V. Molecular mechanisms and cellular functions of cGAS-STING signalling. Nat. Rev. Mol. Cell. Biol. 21 , 501–521. 10.1038/s41580-020-0244-x (2020). Pan, Y. et al. The STING antagonist H-151 ameliorates psoriasis via suppression of STING/NF-κB-mediated inflammation. Br. J. Pharmacol. 178 , 4907–4922. 10.1111/bph.15673 (2021). Griebel, T., Maekawa, T. & Parker, J. E. NOD-like receptor cooperativity in effector-triggered immunity. Trends Immunol. 35 , 562–570. 10.1016/j.it.2014.09.005 (2014). Tables Tables 1 to 13 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx tables.zip 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-5326931","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":380203627,"identity":"8490d886-c2de-483e-bb44-955b5e8213bf","order_by":0,"name":"Rui Yue","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yue","suffix":""},{"id":380203630,"identity":"3b4aabc8-41be-4cbe-b378-1a08f9ef3b1b","order_by":1,"name":"Peng Yang","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yang","suffix":""},{"id":380203631,"identity":"7aab0fa2-4d59-4531-9f36-f2226ceda520","order_by":2,"name":"Jie Xie","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Xie","suffix":""},{"id":380203632,"identity":"090f4ce0-95d9-4779-9b29-fc4dfd8155fd","order_by":3,"name":"Xinxin Long","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Long","suffix":""},{"id":380203634,"identity":"d7242dac-9b0a-451f-a317-40e103b6a0e2","order_by":4,"name":"Aidi Xie","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aidi","middleName":"","lastName":"Xie","suffix":""},{"id":380203636,"identity":"24731d8e-908b-46e6-9ab8-5e6f30758856","order_by":5,"name":"Yuxiang Chen","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Chen","suffix":""},{"id":380203638,"identity":"0bd56a17-4f8c-4694-b576-a2319282ab09","order_by":6,"name":"Xiaoli Chen","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Chen","suffix":""},{"id":380203639,"identity":"e3646d1f-2d76-4c37-96d6-e9c422113c8f","order_by":7,"name":"Yundi Huang","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yundi","middleName":"","lastName":"Huang","suffix":""},{"id":380203641,"identity":"b740f33a-fbf0-4880-b9d4-586b0be77620","order_by":8,"name":"Lifang Qiu","email":"","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Qiu","suffix":""},{"id":380203643,"identity":"c8680572-785d-454e-82ce-fd475ceb97fd","order_by":9,"name":"Xuebiao Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBCDBDYGBsYHCRU2pGlhNnhwJo0ELUDMJvmw7RBhpebsvYdf87bZ5PGxHz5WkcB2gIG/vTsBrxbLnnNpljPb0orZeNLSbiTw3GGQOHN2A14tBjdyzAw+th1ObGPIMbuRIPGMwUAil4CW+2/MDBLb/ie28b8xK0gwOEyElhs8xg8+th1IbJPIMWNISCBCi2VPjhnjjHPJxWwSz5IlEg6k8RD0izn7GePPPGV2efL9yQc//vxnI8ff3kvAYcDokEAW4MGrHKqF+QNBVaNgFIyCUTCyAQDIzEopWiFSOAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanfang Hospital Zengchen Campus,Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xuebiao","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-10-24 15:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5326931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5326931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71628555,"identity":"636c43bd-6b87-4161-ad13-58a3d4db87ba","added_by":"auto","created_at":"2024-12-17 09:11:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408216,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs identification. (A) A volcano plot depicting the distribution of DEGs between psoriasis and control samples. Orange, blue and gray dots represent gene expression levels corresponding to upregulated, downregulated, and insignificant expression. (B) A heatmap depicting the top 5 upregulated and top 5 downregulated DEGs. (C) The variations of top10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. Asterisks represented p-value (****p \u0026lt; 0.0001, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/aa75fbcc6d2fe2e0449b0955.png"},{"id":71628556,"identity":"d040d496-00db-452c-a6ad-3bcba23ea677","added_by":"auto","created_at":"2024-12-17 09:11:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":475042,"visible":true,"origin":"","legend":"\u003cp\u003eSignificantly enriched pathways. (A) CYTOSOLIC DNA SENSING PATHWAY. (B) NOD LIKE RECEPTOR SIGNALING PATHWAY. (C) PRIMARY IMMUNODEFICIENCY. (D) CARDIAC MUSCLE CONTRACTION. (E) HYPERTROPHIC CARDIOMYOPATHY HCM. (F) DILATED CARDIOMYOPATHY. (G) Heatmap illustrating the result of GSVA analysis.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/db147ea3b476179f5c6ef04d.png"},{"id":71628557,"identity":"caa7fd1d-d288-4b8b-9e3f-b378a64ac4f6","added_by":"auto","created_at":"2024-12-17 09:11:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":359030,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of WGCNA co–expression network. (A) Soft threshold β = 8 and scale–free topological fit index (R2). (B) Network analysis of gene expression in psoriasis identifies distinct modules of co-expression data. (C) Relationships among modules. Top: Hierarchical clustering of module eigengenes that summarize the modules found in the clustering analysis. Branches of the dendrogram (the meta-modules) group together eigengenes that are positively correlated. Bottom: Heatmap plot of the adjacencies in the eigengene network. Each row and column in the heatmap corresponds to one module eigengene (labeled by color). In the heatmap, red represents high adjacency, while blue color represents low adjacency. Squares of red color along the diagonal are the meta-modules. (D) Relationships of consensus module eigengenes and Lipid metabolism. Each row in the table corresponds to a consensus module, and each column to a sample or trait. Numbers in the table report the correlations of the corresponding module eigengenes and traits, with the P-values printed below the correlations in parentheses. The table is color coded by correlation according to the color legend. (E) Venn plot showed the interaction of the module genes and the DEGs. (F) The variations of top10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. Asterisks represented p-value (****p \u0026lt; 0.0001, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/10ef593b4e668a6b8666609e.png"},{"id":71629124,"identity":"b556149c-7f87-4d24-8f38-cda5af198466","added_by":"auto","created_at":"2024-12-17 09:19:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":318142,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment based on Lipid metabolism -related DEGs. (A) GO pathways results. (B) KEGG pathways results. (C) Bar plot of BP pathways. (D) Bar plot of CC pathways. (E) Bar plot of MF pathways. (F) Circle plot of KEGG pathways.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/b402591b0fae00f7a1516af3.png"},{"id":71629128,"identity":"987fb84b-521e-4e2e-a64f-72946d6e8773","added_by":"auto","created_at":"2024-12-17 09:19:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":362390,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the hub genes.(A) ROC curves of AACS. (B) ROC curves of HSD11B1. (C) ROC curves of GATA6. (D) ROC curves of PECR. (E) ROC curves of FA2H. (F) ROC curves of HACL1. (G) ROC curves of TMEM56. (H) ROC curves of ACOX2. (I) ROC curves of THRSP. (J) ROC curves of DGAT2. (K) ROC curves of LPIN1. (L) ROC curves of DTX4.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/a9506b20ce1b4ec51de5623a.png"},{"id":71629126,"identity":"45e78d87-b107-408c-9a98-8c3ecb00c152","added_by":"auto","created_at":"2024-12-17 09:19:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":366279,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of hub genes. (A) Characterized gene co–expression network. (B) GO analysis of co–expressed genes. (C) Co–expressed gene KEGG analysis.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/ba5de0c781fcd2b8c9f10e4a.png"},{"id":71628558,"identity":"1e489914-d692-4adc-be36-56fc87a5329f","added_by":"auto","created_at":"2024-12-17 09:11:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":450122,"visible":true,"origin":"","legend":"\u003cp\u003eDistinction of immune infiltrations between psoriasis and control samples. (A) Box plot of the estimated proportion of immune infiltration in the psoriasis and Control group. (B) The heatmap showed the changes of the psoriasis and the Control group. (C) Correlation between HSD11B1 and the immune cell Neutrophil. (D) Correlation between the HSD11B1 and the immune cell Activated CD4 T cell. Asterisks represented p value (****p \u0026lt; 0.0001, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/6f447bb455ea66f665edec92.png"},{"id":71628589,"identity":"aad5a0e6-d6a4-450f-b9df-dc0799ced33a","added_by":"auto","created_at":"2024-12-17 09:11:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":559609,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between hub genes and the 50 HALLMARK signaling pathways. (A) Comparison of the 50 HALLMARK signaling pathways between the psoriasis group and controls. (B) Correlation between the hub genes and the 50 HALLMARK signaling pathways. Asterisks represented p value (****p \u0026lt; 0.0001, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/f9ca853ab8490873891ff916.png"},{"id":71628585,"identity":"7dc112d5-7669-49b8-88d8-e22621a3fa28","added_by":"auto","created_at":"2024-12-17 09:11:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":379812,"visible":true,"origin":"","legend":"\u003cp\u003eThe RBP-mRNA regulatory network orange color represents RBPs, and pink color represents mRNAs.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/8ab23aecd853012427efb878.png"},{"id":102631588,"identity":"5016c48e-8d0f-42e6-9c4b-1551a0d50954","added_by":"auto","created_at":"2026-02-13 19:39:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4249911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/812f699f-a4ea-4716-8d03-b998ed655876.pdf"},{"id":71628560,"identity":"155445a3-9582-48bf-8403-43c9e660063d","added_by":"auto","created_at":"2024-12-17 09:11:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":359675,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/de6eeccf64f01528d8cadfd7.docx"},{"id":71629127,"identity":"a4dd925d-2c0c-4634-b9f8-c49218b2278e","added_by":"auto","created_at":"2024-12-17 09:19:49","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1154652,"visible":true,"origin":"","legend":"","description":"","filename":"tables.zip","url":"https://assets-eu.researchsquare.com/files/rs-5326931/v1/32726f8f126a3d541d50eb88.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes and Immune Infiltration Characteristics in Psoriasis Patients ","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis is a chronic, immune-mediated inflammatory skin disorder that affects approximately 2%-3% of the global population\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Characterized by red, scaly plaques on the skin, this condition is not only a cosmetic concern but also impacts the quality of the patients\u0026rsquo; lives\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite the availability of treatments ranging from topical agents to systemic therapies, many patients experience inadequate responses or adverse effects, psoriasis remains incurable\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Research found that severe psoriasis are associated with an increased mortality risk of metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, there is an urgent need to identify novel therapeutic targets and elucidate the underlying mechanisms of psoriasis to develop more effective and safer treatments.\u003c/p\u003e \u003cp\u003eLipid metabolism as the central to psoriasis pathogenesis, influencing inflammatory cascades and keratinocyte proliferation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, abnormalities accumulate lipids, exacerbating skin inflammation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Immune cells, lipids, and inflammatory mediators perpetuate a cycle of damage. Modulating lipid metabolism to disrupt this cycle holds promise for effective, targeted psoriasis therapies, enhancing patient quality of life.\u003c/p\u003e \u003cp\u003eBy integrating multiple datasets and employing advanced bioinformatics methods, this study seeks to provide a more detailed and reliable understanding of the molecular mechanisms driving psoriasis. The findings from this research are expected to contribute to the development of more effective treatments, ultimately improving the quality of life for patients suffering from this debilitating condition.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData Sources and Preprocessing\u003c/p\u003e \u003cp\u003eAll the information utilized in this investigation is readily available to the public, primarily sourced from the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The psoriasis whole genome-wide expression profiles were retrospectively retrieved via the R package \u0026lsquo;GEOquery\u0026rsquo; from the GEO repository. GSE30999 comprises skin biopsy samples from 85 psoriasis patients and 85 healthy controls. GSE54456 encompasses skin biopsy samples from 92 psoriasis patients and 82 healthy controls. Batch effects resulting from non-biological technical biases were rectified using the ComBat method from the R package \u0026ldquo;sva\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Principal component analysis (PCA) was employed to assess the extent of correction. The current study adhered to the data access policies of each database.\u003c/p\u003e \u003cp\u003eA comprehensive total of 1564 genes associated with lipid metabolism were retrieved from the Msigdb database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gseamsigdb.org/gsea/msigdb/index.jsp)\u003c/span\u003e\u003cspan address=\"http://www.gseamsigdb.org/gsea/msigdb/index.jsp)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e8,9\u003c/sup\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(Table\u003c/span\u003e\u003cspan address=\"http://(Table\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e1\u003c/span\u003e\u003cspan address=\"http://1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferentially Expressed Genes Associated with psoriasis\u003c/p\u003e \u003cp\u003eDifferentially expressed genes (DEGs) between control (n\u0026thinsp;=\u0026thinsp;167) and psoriasis (n\u0026thinsp;=\u0026thinsp;177) samples were discerned utilizing the \u0026ldquo;limma (version 3.50.0)\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e package in R, applying the criteria of |log2Fold Change |\u0026gt; 0.5 and an adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which were incorporated in the subsequent research. Thereafter, the heatmap was produced using the R package \u0026ldquo;pheatmap\u0026rdquo; with Euclidean distance and complete linkage clustering methodology.\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003c/p\u003e \u003cp\u003eThe Gene Set Enrichment Analysis (GSEA)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e is a computational technique that assesses whether a priori defined groups of genes exhibit statistically significant, concordant variations between two biological conditions. GSEA was executed utilizing the R package \u0026ldquo;clusterProfiler (version 4.2.2)\u0026rdquo; on a ranked list of all genes according to their log2Fold Change values. Gene set permutations were conducted 1,000 times for each analysis. The study selected c2.cp.kegg.v7.5.1.symbols from the Molecular Signatures Database (MSigDB)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e as the reference gene collection. Gene sets with an adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significantly enriched.\u003c/p\u003e \u003cp\u003eGene Set Variation Analysis (GSVA)\u003c/p\u003e \u003cp\u003eGSVA (Gene Set Variation Analysis) is an unsupervised and non-parametric method for gene set enrichment that enables the utilization of gene expression profiles to evaluate associations between biological pathways and gene characteristics. To explore the disparities in biological functions between the control and psoriasis cohorts, gene set variation analysis (GSVA) was conducted using \u0026ldquo;c2.cp.kegg.v7.5.1.symbols\u0026rdquo; with the R package \u0026ldquo;GSVA (version 1.42.0).\u0026rdquo; The R package \u0026ldquo;pheatmap (version 1.0.12)\u0026rdquo; was employed to illustrate the findings. Furthermore, we retrieved 50 hallmark gene sets from the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to serve as reference gene sets. We utilized the ssGSEA function within the GSVA package to compute the GSVA score for each gene set across different samples. Subsequently, the Limma package was used to analyze the differences in GSVA scores of various gene sets between the control and psoriasis groups.\u003c/p\u003e \u003cp\u003eWeighted Gene Co‑expression Network Analysis (WGCNA) and Identification of Significant Modules\u003c/p\u003e \u003cp\u003eCo-expression networks were formulated utilizing the WGCNA algorithm implemented in the R WGCNA package (version 1.70-3)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The Pearson correlation coefficient was computed to evaluate the similarity of gene expression profiles, and subsequently, the correlation coefficients among genes were weighted by a power function to create a scale-free network. Utilizing the R package \u0026lsquo;PickSoftThreshold\u0026rsquo;, we established a weighted adjacency matrix by elevating the co-expression similarity to a power β\u0026thinsp;=\u0026thinsp;8. A gene module is defined as a cluster of densely interconnected genes regarding co-expression. WGCNA employs hierarchical clustering to discern gene modules, with colors representing distinct modules. The dynamic tree cut method was employed to identify various modules; during module selection, the adjacency matrix (a metric of topological similarity) was transformed into a topology overlap matrix (TOM), and modules were detected through cluster analysis. To examine associations of modules with lipid metabolism, the relationships of the module eigengene (ME, the first principal component of the module, reflecting the overall expression level of the module) to lipid metabolism were calculated via Pearson\u0026rsquo;s correlation analysis. Modules exhibiting significant associations with lipid metabolism were identified. The co-expression module structure was illustrated through heatmap plots of topological overlap within the gene network. Interrelations among modules were summarized by a hierarchical clustering dendrogram of the eigengenes and by a heatmap plot of the corresponding eigengene network. The lipid metabolism-related differentially expressed genes (lipid metabolism-related DEGs) were derived from the intersection of DEGs and genes from the lipid metabolism-related module.\u003c/p\u003e \u003cp\u003eGO and KEGG Pathway Enrichment Analysis\u003c/p\u003e \u003cp\u003eGene Ontology (GO)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e enrichment analysis encompasses Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) assessments. The Kyoto Encyclopedia of Genes and Genomes (KEGG)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e serves as a bioinformatics resource for investigating significantly modified metabolic pathways that are enriched within the gene list. The R package \u0026ldquo;clusterProfiler (version 4.2.2)\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was utilized to conduct GO and KEGG enrichment analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on the lipid metabolism-related differentially expressed genes (DEGs).\u003c/p\u003e \u003cp\u003eGeneMANIA\u003c/p\u003e \u003cp\u003eThe protein\u0026ndash;protein interaction (PPI) networks of the central genes were established using the GeneMANIA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org\u003c/span\u003e\u003cspan address=\"http://genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e16\u003c/sup\u003e, which can also forecast the connections among functionally analogous genes and central genes, encompassing protein-protein interactions, protein-DNA interactions, pathways, physiological and biochemical processes, co-expression, and co-localization.\u003c/p\u003e \u003cp\u003eThe Receiver Operating Characteristic (ROC) Curve\u003c/p\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curve, defined as a graphical representation of test sensitivity plotted as the y coordinate against its 1-specificity or false positive rate (FPR) as the x coordinate, serves as a robust method for assessing the efficacy of diagnostic tests. The most prevalent metric is the area-under-the-curve (AUC), derived from the receiver operating characteristic plot of sensitivity versus 1 \u0026ndash; specificity. We employed the R package \u0026ldquo;pROC\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e to generate ROC curves for determining the AUC of screening signature genes and appraising their diagnostic significance. This value is quantified on a continuum from 0.5 (indicative of a \u0026ldquo;coin flip\u0026rdquo;) to 1 (denoting perfect discrimination). Generally, an AUC of 0.5 signifies no predictive capability, 0.6 to 0.8 is deemed acceptable, 0.8 to 0.9 is classified as excellent, and exceeding 0.9 is regarded as exceptional.\u003c/p\u003e \u003cp\u003eImmune Infiltration Analysis\u003c/p\u003e \u003cp\u003eSingle-sample Gene Set Enrichment Analysis (ssGSEA)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, a refinement of Gene Set Enrichment Analysis (GSEA), computes distinct enrichment scores for every combination of a sample and gene set. Each ssGSEA enrichment score signifies the extent to which the genes in a specific gene set are collectively up- or down-regulated within a sample. Single-sample GSEA (ssGSEA) is an enhancement of the GSEA algorithm that, rather than calculating enrichment scores for groups of samples (e.g., control vs. disease) and sets of genes (e.g., pathways), delivers a score for each sample and gene set pairing.\u003c/p\u003e \u003cp\u003eBased on the 28 categories of immune cells obtained from the TISIDB (Tumor and Immune System Interactions Database) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/index.php\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e19\u003c/sup\u003e, which include the following immune cell types: Activated CD8 T cell, Central memory CD8 T cell, Effector memory CD8 T cell, Activated CD4 T cell, Central memory CD4 T cell, Effector memory CD4 T cell, T follicular helper cell, Gamma delta T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, Regulatory T cell, Activated B cell, Immature B cell, Memory B cell, Natural killer cell, CD56 bright natural killer cell, CD56 dim natural killer cell, Myeloid derived suppressor cell, Natural killer T cell, Activated dendritic cell, Plasmacytoid dendritic cell, Immature dendritic cell, Macrophage, Eosinophil, Mast cell, Monocyte, and Neutrophil, the relative enrichment score of each immunocyte was quantified from the gene expression profile of each sample. Variations in the infiltration levels of immune cells among samples in the psoriasis and control groups were depicted using the R package ggplot2 (version 3.3.6)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConstruction of the RBP\u0026ndash;mRNA network\u003c/p\u003e \u003cp\u003eStarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://starbase.sysu.edu.cn/tutorialAPI.php#RBPTarget\u003c/span\u003e\u003cspan address=\"https://starbase.sysu.edu.cn/tutorialAPI.php#RBPTarget\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a widely utilized open-source platform for examining ncRNA interactions through CLIP-seq, degradome-seq, and RNA\u0026ndash;RNA interactome data, was employed to explore the relationships between mRNA and RBP (RNA binding protein) expression. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, clusterNum\u0026thinsp;\u0026ge;\u0026thinsp;5, and clipExpNum\u0026thinsp;\u0026ge;\u0026thinsp;5 were established as the threshold criteria for identifying the significant mRNA-RBP pairs in psoriasis. Subsequently, the RBP-mRNA network was constructed using Cytoscape.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical evaluation was conducted utilizing R software v4.1.2. The relationship between two variables was determined through Spearman\u0026rsquo;s correlation analysis. Inter-group variations were assessed using the Wilcoxon test, whereas the Kruskal\u0026ndash;Wallis test was employed to compare differences among three or more groups. Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDEGs Identification\u003c/p\u003e \u003cp\u003eThrough the analysis of psoriasis samples in comparison to controls, a total of 3,839 differentially expressed genes (DEGs) were identified as statistically significant between the two cohorts (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |Log2 fold change| \u0026gt; 0.5). In psoriasis samples, 1,775 genes were found to be upregulated, while 2,064 genes were downregulated (Table\u0026nbsp;2). All DEGs were illustrated using a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Moreover, the top five upregulated genes (PLA2G4D, VNN3, TMPRSS11D, S100A12, SERPINB4) alongside the top five downregulated DEGs (BTC, KRT77, BCAR3, RORC, SNTB1) were presented in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). According to Wilcoxon tests, these top ten genes exhibited significant differences in expression levels between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGSEA\u003c/h3\u003e\n\u003cp\u003eTo further investigate the potential mechanisms underlying the differentially expressed genes (DEGs), we conducted a Gene Set Enrichment Analysis (GSEA). Utilizing the MSigDB Collection, we identified the most significantly enriched signaling pathways based on their normalized enrichment score (NES) (Table\u0026nbsp;3). GSEA revealed that the CYTOSOLIC DNA SENSING PATHWAY (NES\u0026thinsp;=\u0026thinsp;2.042, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014), NOD LIKE RECEPTOR SIGNALING PATHWAY (NES\u0026thinsp;=\u0026thinsp;2.036, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014), PRIMARY IMMUNODEFICIENCY (NES\u0026thinsp;=\u0026thinsp;2.036, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014), CARDIAC MUSCLE CONTRACTION (NES = -1.83, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014), HYPERTROPHIC CARDIOMYOPATHY (HCM) (NES = -1.921, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014), and DILATED CARDIOMYOPATHY (NES = -1.952, adj.p\u0026thinsp;=\u0026thinsp;0.019, FDR\u0026thinsp;=\u0026thinsp;0.014) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F) were significantly enriched in psoriasis.\u003c/p\u003e\n\u003ch3\u003eGSVA\u003c/h3\u003e\n\u003cp\u003eTo delve deeper into the functional annotation between psoriasis and control samples, we conducted GSVA analyses to assess the relative expression differences of the pathways in the two groups. GSVA analysis revealed a multitude of differentially expressed pathways, which were illustrated by the heatmap. In contrast to the control groups, the expression of pathways linked to KEGG_BASAL_CELL_CARCINOMA and KEGG_RIBOSOME was markedly diminished in psoriasis, while the expression of pathways associated with KEGG_DNA_REPLICATION and KEGG_HOMOLOGOUS_RECOMBINATION was significantly elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWeighted gene Co-expression Network Construction and Module Identification\u003c/p\u003e \u003cp\u003eWGCNA was utilized to explore gene sets associated with lipid metabolism. The scale independence and mean connectivity analysis revealed that when the weighted value equaled 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the average degree of connectivity approached 0, while scale independence exceeded 0.85. Thirteen co-expressed modules were identified, and uncorrelated genes were allocated to a gray module, which was disregarded in subsequent analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To examine the interrelationships among modules and ascertain their correlation, we computed the MEs. The eigengene network, represented through a dendrogram and a heatmap plot, is illustrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To comprehend the physiological relevance of the modules, we correlated the 13 MEs with lipid metabolism and sought the most significant associations. According to the heatmap of module-trait correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), genes clustered in the black module (n\u0026thinsp;=\u0026thinsp;151, Table\u0026nbsp;5) exhibited the strongest positive correlation with lipid metabolism (r\u0026thinsp;=\u0026thinsp;0.9216, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, we will primarily focus on the black module moving forward, as it may more accurately reflect lipid metabolism.\u003c/p\u003e \u003cp\u003eA cumulative total of 109 differentially expressed genes (DEGs) associated with lipid metabolism was derived from the overlap of DEGs and genes within the lipid metabolism-related module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Table\u0026nbsp;6), which were regarded as pivotal genes. Analysis through Wilcoxon tests indicated that these top 10 genes exhibited significant disparities in expression levels between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnrichment Analyses (GO/KEGG)\u003c/p\u003e \u003cp\u003eTo explore the biological roles of the lipid metabolism-associated DEGs, we conducted enrichment analyses for GO terms (Table\u0026nbsp;7) and KEGG pathways (Table\u0026nbsp;8). The GO findings revealed that these genes were significantly enriched in the fatty acid metabolic process (GO:0006631), long-chain fatty acid metabolic process (GO:0001676), fatty acid biosynthetic process (GO:0006633) (BP), peroxisome (GO:0005777), microbody (GO:0042579), peroxisomal membrane (GO:0005778), microbody membrane (GO:0031903) (CC), O-acyltransferase activity (GO:0008374), acyltransferase activity, transferring groups other than amino-acyl groups (GO:0016747), and acyltransferase activity (GO:0016746) (MF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Beyond the GO terms, the enriched KEGG pathways included Peroxisome (hsa04146), Fatty acid metabolism (hsa01212), and Glycerolipid metabolism (hsa00561) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eValidation of the Hub Genes\u003c/p\u003e \u003cp\u003eTo further substantiate the diagnostic significance of pivotal genes, we conducted receiver operating characteristic (ROC) analysis. AACS (AUC\u0026thinsp;=\u0026thinsp;0.941), HSD11B1 (AUC\u0026thinsp;=\u0026thinsp;0.939), GATA6 (AUC\u0026thinsp;=\u0026thinsp;0.916), PECR (AUC\u0026thinsp;=\u0026thinsp;0.9), FA2H (AUC\u0026thinsp;=\u0026thinsp;0.9), HACL1 (AUC\u0026thinsp;=\u0026thinsp;0.898), TMEM56 (AUC\u0026thinsp;=\u0026thinsp;0.89), ACOX2 (AUC\u0026thinsp;=\u0026thinsp;0.889), THRSP (AUC\u0026thinsp;=\u0026thinsp;0.886), DGAT2 (AUC\u0026thinsp;=\u0026thinsp;0.885), LPIN1 (AUC\u0026thinsp;=\u0026thinsp;0.884), DTX4 (AUC\u0026thinsp;=\u0026thinsp;0.882), CRAT (AUC\u0026thinsp;=\u0026thinsp;0.88), AGPAT1 (AUC\u0026thinsp;=\u0026thinsp;0.879), PEX11A (AUC\u0026thinsp;=\u0026thinsp;0.875), APOC1 (AUC\u0026thinsp;=\u0026thinsp;0.864), TMPRSS11E (AUC\u0026thinsp;=\u0026thinsp;0.863), INSIG1 (AUC\u0026thinsp;=\u0026thinsp;0.862), ACSL1 (AUC\u0026thinsp;=\u0026thinsp;0.861), FBP1 (AUC\u0026thinsp;=\u0026thinsp;0.855), MGST1 (AUC\u0026thinsp;=\u0026thinsp;0.854) were identified to possess comparable area under the ROC curve (AUC) values, regarded as hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-L, Supplementary Fig.\u0026nbsp;1). This indicates that the recognized hub genes exhibited a satisfactory discriminatory capacity as prospective biomarkers for psoriasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTrait Gene Interaction Analysis\u003c/p\u003e \u003cp\u003eWe utilized the GeneMANIA database to construct a PPI network for the signature genes, revealing 20 genes present within the PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To further explore the functions of the signature genes, GO and KEGG analyses were conducted on 41 genes, comprising 20 hub genes and 21 associated genes. The GO results indicate that these genes are significantly enriched in the fatty acid metabolic process (GO:0006631), triglyceride metabolic process (GO:0006641), acylglycerol metabolic process (GO:0006639), among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, Table\u0026nbsp;9). The primary enriched pathways, as determined by KEGG analysis, included the Peroxisome (hsa04146), Glycerolipid metabolism (hsa00561), Fatty acid metabolism (hsa01212), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Table\u0026nbsp;10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmune Cells Infiltration\u003c/p\u003e \u003cp\u003eThe infiltration of immune cells may play a crucial role in the pathogenesis of psoriasis. Consequently, we explored the relationships between psoriasis and control samples concerning infiltrated immune cells. Among 28 varieties of immune cells, the infiltration levels of 27 types were markedly different between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, Table\u0026nbsp;11). For 20 types of immune cells (Activated CD8 T cell, Effector memory CD8 T cell, Activated CD4 T cell, T follicular helper cell, Gamma delta T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, Regulatory T cell, Activated B cell, Immature B cell, Memory B cell, CD56dim natural killer cell, Myeloid-derived suppressor cell, Natural killer T cell, Activated dendritic cell, Macrophage, Eosinophil, Monocyte, Neutrophil), a significantly elevated infiltration level was noted in the psoriasis group compared to the control group. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, the overall infiltration levels of immune cells differed substantially between the psoriasis and control groups.\u003c/p\u003e \u003cp\u003eMoreover, the notable correlations between each hub gene and the respective immune cells were also identified. It is important to highlight that HSD11B1 was markedly linked to Neutrophil (R = -0.781, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC); HSD11B1 was significantly correlated with Activated CD4 T cell (R = -0.736, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignaling Pathways Involved in Signature Genes\u003c/p\u003e \u003cp\u003eThe disparities between psoriasis patients and controls across 50 HALLMARK signaling pathways were further examined utilizing GSVA. In psoriasis patients, 25 HALLMARK signaling pathways were notably up-regulated: HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_APOPTOSIS, HALLMARK_COMPLEMENT, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGETS, HALLMARK_G2M_CHECKPOINT, HALLMARK_GLYCOLYSIS, HALLMARK_IL2_STAT5_SIGNALING, HALLMARK_IL6_JAK_STAT3_SIGNALING, HALLMARK_INFLAMMATORY_RESPONSE, HALLMARK_INTERFERON_ALPHA_RESPONSE, HALLMARK_INTERFERON_GAMMA_RESPONSE, HALLMARK_MITOTIC_SPINDLE, HALLMARK_MTORC1_SIGNALING, HALLMARK_MYC_TARGETS_V1, HALLMARK_MYC_TARGETS_V2, HALLMARK_OXIDATIVE_PHOSPHORYLATION, HALLMARK_P53_PATHWAY, HALLMARK_PI3K_AKT_MTOR_SIGNALING, HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY, HALLMARK_SPERMATOGENESIS, HALLMARK_TNFA_SIGNALING_VIA_NFKB, HALLMARK_UNFOLDED_PROTEIN_RESPONSE, HALLMARK_UV_RESPONSE_UP, HALLMARK_XENOBIOTIC_METABOLISM. Twenty pathways were significantly down-regulated in psoriasis patients, including: HALLMARK_ADIPOGENESIS, HALLMARK_ANDROGEN_RESPONSE, HALLMARK_ANGIOGENESIS, HALLMARK_APICAL_JUNCTION, HALLMARK_BILE_ACID_METABOLISM, HALLMARK_CHOLESTEROL_HOMEOSTASIS, HALLMARK_COAGULATION, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_ESTROGEN_RESPONSE_EARLY, HALLMARK_FATTY_ACID_METABOLISM, HALLMARK_HEDGEHOG_SIGNALING, HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_DN, HALLMARK_MYOGENESIS, HALLMARK_NOTCH_SIGNALING, HALLMARK_PANCREAS_BETA_CELLS, HALLMARK_PEROXISOME, HALLMARK_TGF_BETA_SIGNALING, HALLMARK_UV_RESPONSE_DN, HALLMARK_WNT_BETA_CATENIN_SIGNALING (Fig.\u0026nbsp;8A, Table\u0026nbsp;12). We also assessed the correlations of the five most significant differentially expressed hub genes with the 50 HALLMARK signaling pathways (Fig.\u0026nbsp;8B).\u003c/p\u003e \u003cp\u003eFigure 8. Correlation between hub genes and the 50 HALLMARK signaling pathways. (A) Comparison of the 50 HALLMARK signaling pathways between the psoriasis group and controls. (B) Correlation between the hub genes and the 50 HALLMARK signaling pathways. Asterisks represented p value (****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eConstruction and Functional Annotation of the Crosstalk between the hub mRNAs and RBPs\u003c/p\u003e \u003cp\u003eAs RNA binding proteins (RBPs) associate with mRNA, we investigated 21 central mRNAs utilizing StarBase, resulting in the identification and acquisition of 16 mRNA/RBP pairs. Based on the connections between target genes presented in the online dataset, we established an RBP-mRNA network comprising 73 nodes, 57 RBPs, 16 mRNAs, and 345 edges. The specifics of the nodes and their interactions are detailed in Table\u0026nbsp;13, while the network is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePsoriasis is not only a skin disease but also a systemic condition with complex pathophysiology involving immune dysregulation and metabolic disturbances\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Previous studies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e have shown that lipid metabolism plays a critical role in the pathogenesis of psoriasis, influencing inflammation and keratinocyte proliferation. By analyzing the expression profiles of lipid metabolism-related genes, we aim to uncover novel insights that could lead to more effective diagnostic and therapeutic strategies for psoriasis.\u003c/p\u003e \u003cp\u003eThe DEGs identified in this study provided crucial insights into the molecular mechanisms of psoriasis. Notably, genes such as PLA2G4D, VNN3, TMPRSS11D, and S100A12 were among the top differentially expressed genes. Genes like S100A12 and SERPINB4 have been reported in earlier studies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, suggesting their consistent involvement in the disease pathology and enhancing the validity of our results\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Furthermore, the upregulated and downregulated genes appear to cluster within specific pathways, such as immune response and keratinocyte differentiation, which are known to be critical in psoriasis. This clustering not only reinforces the relevance of our findings but also opens avenues for identifying novel therapeutic targets. Future studies should focus on validating these DEGs in larger cohorts and exploring their potential as biomarkers or therapeutic targets.\u003c/p\u003e \u003cp\u003eAmong the co-expressed modules identified by WGCNA, the black module showed the strongest positive correlation with lipid metabolism. The DEGs related to lipid metabolism included in this module are significantly enriched in processes such as fatty acid metabolism and peroxisome function. such as AACS and HSD11B1and GATA6. Meanwhile, ROC curve analysis demonstrated that those hub genes had high diagnostic value with AUC values greater than 0.85. they could serve as potential biomarkers for the early diagnosis of psoriasis, improving diagnostic accuracy. The high diagnostic value of these genes suggests their significant roles in the disease's pathogenesis.\u003c/p\u003e \u003cp\u003eAACS is involved in the synthesis of acetoacetyl-CoA, a key intermediate in the synthesis of cholesterol and fatty acids, while HSD11B1 regulates glucocorticoid hormone action, which can influence inflammation and lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The dysregulation of these processes may contribute to the altered skin barrier function and systemic inflammation observed in psoriasis patients.\u003c/p\u003e \u003cp\u003eAACS (Acetoacetyl-CoA Synthetase) is a key enzyme involved in the synthesis of ketone bodies and cholesterol, playing a pivotal role in lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In the context of psoriasis, the upregulation of AACS may reflect an altered lipid metabolic state, which is consistent with the observed dysregulation of fatty acid metabolism in our study. The significant association of AACS with psoriasis suggests that it may contribute to the pathogenesis of the disease by affecting the balance of lipid homeostasis in the skin, potentially serving as a novel therapeutic target.\u003c/p\u003e \u003cp\u003eHSD11B1 (Hydroxysteroid 11-Beta Dehydrogenase 1) is involved in the conversion of inactive cortisone to its active form, cortisol, within cells. This enzyme has been implicated in the regulation of inflammatory responses, which are central to the pathophysiology of psoriasis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Moreover, targeting HSD11B1 could modulate local cortisol levels and thus inflammatory pathways, offering a promising avenue for therapeutic intervention\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA research\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e found GATA6(GATA Binding Protein 6)regulates the expression of HIF as well as VEGF. The expression of GATA6 was lower in the psoriatic dermal MSCs (Mesenchymal stem cells) than in the control group. This indicates that the inflammatory cytokines presented in the psoriatic microenvironment might involve in the pathogenesis of psoriasis through influencing the angiogenesis-related gene.\u003c/p\u003e \u003cp\u003eIn our study, the significant enrichment of immune-related pathways such as the cytosolic DNA sensing pathway and NOD-like receptor signaling pathway in psoriasis patients underscores the pivotal role of innate immunity in the pathogenesis of the disease. The cytosolic DNA sensing pathway is crucial for the recognition of pathogen and host-derived DNA in the cytoplasm, which in turn activates a host defense response including the production of type I interferons\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Dysregulation of this pathway has been implicated in various autoimmune and inflammatory diseases, suggesting its potential involvement in the aberrant immune response observed in psoriasis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Similarly, the NOD-like receptor signaling pathway is integral to the host's immune defense against a wide range of pathogens and is involved in the regulation of inflammation and apoptosis\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The enrichment of these pathways in our analysis indicates an ongoing immune response, possibly driven by the recognition of self-DNA and microbial components, leading to the chronic inflammation characteristic of psoriasis.\u003c/p\u003e \u003cp\u003eImmune infiltration analysis revealed a significant increase in the infiltration of 20 types of immune cells in the psoriatic lesions compared to the control group, supporting the hypothesis that psoriasis is an autoimmune skin disease caused by abnormal activation and infiltration of immune cells into the skin. Among these, T helper (Th) cells, particularly Th1 and Th17, have been implicated in the pathogenesis of psoriasis, as they secrete pro-inflammatory cytokines that contribute to the inflammatory milieu characteristic of the disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The observed upregulation of the NOD-like receptor signaling pathway in our GSEA analysis is consistent with the role of these innate immune receptors in recognizing microbial motifs and endogenous danger signals, leading to the production of inflammatory cytokines and chemokines that can exacerbate psoriatic inflammation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the significant enrichment of the cytosolic DNA sensing pathway suggests the involvement of antiviral defense mechanisms that may be aberrantly activated in psoriasis, contributing to the chronic inflammatory state\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The identification of these immune-related pathways and the differential infiltration of immune cells underscore the complex interplay between the innate and adaptive immune systems in psoriasis.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eDespite the comprehensive bioinformatics analysis conducted in this study, several limitations should be acknowledged. Firstly, the research was conducted purely through bioinformatics analysis without the integration of wet lab experiments, which could have provided additional validation for the computational predictions. Secondly, the sample size used in the analysis was relatively small, which may limit the generalizability of the results. Thirdly, there was a lack of clinical validation analysis to correlate the identified biomarkers with actual patient outcomes. Finally, the use of multiple datasets could introduce batch effects, even though efforts were made to correct for these using the ComBat method.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur comprehensive bioinformatics analysis has identified significant differentially expressed genes and enriched pathways associated with psoriasis, providing new insights into its molecular mechanisms. The integration of multiple datasets and advanced analytical methods has revealed potential therapeutic targets, particularly in lipid metabolism and immune response pathways. These findings not only enhanced our understanding of psoriasis pathogenesis but also pave the way for the development of more effective treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRY and XP performed the data analyses. PY, JX and XL interpreted the data. XC and AX reviewed the paper. XC, YH and LQ contributed to the revision stage. All authors approved the final manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GEO repository， including GSE30999 and GSE54456（https://www.ncbi.nlm.nih.gov/geo/）.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo, J. et al. Signaling pathways and targeted therapies for psoriasis. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-023-01655-6\u003c/span\u003e\u003cspan address=\"10.1038/s41392-023-01655-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhoreschi, K., Balato, A., Enerb\u0026auml;ck, C. \u0026amp; Sabat, R. 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This study aims to identify differentially expressed genes (DEGs) and their functional pathways associated with psoriasis to uncover new therapeutic targets. We leveraged GEO datasets for DEGs analysis in psoriasis. GSEA, GSVA, WGCNA, \u0026amp; ssGSEA probed lipid metabolism genes' roles in psoriasis processes \u0026amp; immune changes. An RBP-mRNA network illuminated post-transcriptional regulatory mechanisms. Our findings revealed 3,839 DEGs, with 1,775 upregulated and 2,064 downregulated genes in psoriatic samples compared to controls. GSEA highlighted significant enrichment of immune-related pathways such as the cytosolic DNA sensing pathway and NOD-like receptor signaling pathway. WGCNA identified a black module strongly positive correlated with lipid metabolism containing 151 genes (r\u0026thinsp;=\u0026thinsp;0.9216, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Enrichment analysis pointed to fatty acid metabolism and peroxisome pathways as critical in disease pathogenesis. Hub genes like AACS, HSD11B1 and GATA6 demonstrated high diagnostic potential with area under the ROC curve values exceeding 0.85. Immune infiltration analysis revealed significant differences in 27 types of immune cells between psoriasis and healthy control groups. Those findings provided a comprehensive molecular landscape of psoriasis, identifying potential new targets for therapeutic intervention and enhancing our understanding of the disease's underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes and Immune Infiltration Characteristics in Psoriasis Patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 09:11:44","doi":"10.21203/rs.3.rs-5326931/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":"6626398c-4ae8-4dae-80da-1aaf326177ec","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40492255,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":40492256,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-13T19:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 09:11:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5326931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5326931","identity":"rs-5326931","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}