Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes in Psoriasis Patients

Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes 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 Research Article Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes in Psoriasis Patients Rui Yue, Xuebiao Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999012/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, is closely associated with 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 Gene Expression Omnibus (GEO) datasets for DEGs analysis in psoriasis. Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), Weighted Gene Co-expression Network Analysis (WGCNA), and single-sample GSEA (ssGSEA) were used to investigate the roles of lipid metabolism genes in psoriasis progression and immune alterations. An RBP-mRNA network revealed post-transcriptional regulatory mechanisms. Our findings revealed 3,839 DEGs, including 1,775 upregulated and 2,064 downregulated genes in psoriatic samples compared to controls. Enrichment analysis showed that immune-related pathways such as cytoplasmic DNA sensing pathways and NOD-like receptor signaling pathways were significantly enriched. WGCNA identified modules highly correlated with lipid metabolism and screened out key genes such as AACS, HSD11B1 and GATA6. ROC curve analysis showed that these genes had a high discriminatory ability (AUC > 0.85) within the analyzed dataset, suggesting their potential as novel biomarkers. Immune infiltration analysis revealed significant differences in 27 types of immune cells between patients with psoriasis and the control group. This study provides new clues for the molecular mechanism of psoriasis and potential therapeutic targets. 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% to 3% of the global population [1, 2]. Characterized by red, scaly plaques, this condition is not only a cosmetic concern but also affects the patients’ quality of life [3, 4]. Therapeutics that specifically target IL-23, IL-17, and IL-17RA are approved for clinical use and show excellent efficacy[5]. Despite the presence of biological agents, many patients still experience relapses and adverse effects [6, 7]. Research found that severe psoriasis is associated with an increased risk of mortality related to metabolic syndrome [8]. 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 likely contributes to psoriasis pathogenesis by influencing inflammatory cascades and keratinocyte proliferation [9, 10]. Abnormalities in lipid metabolism leads to lipid accumulation, which exacerbates skin inflammation [11]. Immune cells, lipids, and inflammatory mediators perpetuate a cycle of damage. Modulating lipid metabolism to disrupt this cycle holds promise for developing targeted psoriasis therapies that improve patient quality of life. In this study, we leverage advanced bioinformatics techniques to investigate gene expression changes associated with lipid metabolism in psoriasis. We employ several bioinformatics methods, including differential expression analysis (DEGs), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA), to identify key regulatory genes and pathways involved in the disease. By focusing on lipid metabolism-related genes, we aim to elucidate their roles in the inflammatory processes characteristic of psoriasis and potentially uncover novel biomarkers that could enhance diagnostic and therapeutic strategies. The main objective of this study is to identify the lipid metabolism-related genes differential expressed in psoriasis compared to healthy controls. By integrating transcriptomic data with biological pathway analysis, we seek to provide a comprehensive understanding of how lipid metabolism intersects with the immune dysregulation observed in psoriasis. Our findings aim to contribute valuable insights into the molecular landscape of psoriasis and open new avenues for the development of targeted therapies that address the disease's underlying pathophysiology. This study integrates multiple datasets and uses advanced bioinformatics methods to provide a deeper and reliable understanding of the molecular mechanisms underlying psoriasis. We expect the findings from this research to contribute to developing more effective treatments, ultimately improving the quality of life for patients with this debilitating condition. Materials and Methods Data Sources and Preprocessing All data used in this study are publicly available, mainly from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). The psoriasis whole genome-wide expression profiles were retrieved using the R package ‘GEOquery’ (version 2.25.0) from the GEO repository. GSE30999 contains skin biopsy samples from 85 psoriasis patients and 85 healthy controls [12]. GSE54456 encompasses skin biopsy samples from 92 psoriasis patients and 82 healthy controls [13, 14]. Batch effects caused by non-biological technical biases were corrected using the ComBat method from the R package “sva” (version 2.25.0) [7]. Principal component analysis (PCA) was used to evaluate the effectiveness of batch effect 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 [8,9] ( http://www.gseamsigdb.org/gsea/msigdb/index.jsp ). Differentially Expressed Genes Associated with psoriasis Differentially expressed genes (DEGs) between control (n = 167) and psoriasis (n = 177) samples were identified using the “limma (version 3.50.0)” [10] package in R. The criteria applied were |log2 Fold Change| > 0.5 and an adjusted p-value < 0.05. These DEGs were used in the subsequent analyses. Next, the heatmap was generated using the R package “pheatmap” with Euclidean distance and complete linkage clustering. Gene Set Enrichment Analysis (GSEA) The Gene Set Enrichment Analysis (GSEA) [8] is a computational method. It evaluates whether predefined gene groups show statistically significant and consistent changes between two biological conditions. GSEA was performed using the R package “clusterProfiler (version 4.2.2)” on a ranked list of all genes based on their log2 Fold Change values. Gene set permutations were conducted one thousand times for each analysis. The study employed c2.cp.kegg v7.5.1 gene sets from the Molecular Signatures Database (MSigDB) [8,9,11] as the reference gene collection. Gene sets with an adjusted p-value < 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. It uses gene expression profiles to evaluate associations between biological pathways and gene characteristics. To explore the disparities in biological functions between the control and psoriasis groups, 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 “heatmap (version 1.0.12)” was used to generate heatmaps of the results. 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. After calculating the enrichment scores, the Limma package was used to analyze differences in gene set scores between the control and psoriasis groups. Weighted Gene Co‑expression Network Analysis (WGCNA) and Identification of Significant Modules We constructed co-expression networks using the WGCNA algorithm from the R WGCNA package (version 1.70-3) [12]. We computed the Pearson correlation coefficient to evaluate gene expression similarity, then weighted these coefficients 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 a cluster of densely co-expressed genes. WGCNA employs hierarchical clustering to discern gene modules, with colors representing distinct modules. We used the dynamic tree cut method to identify modules. During module selection, we transformed the adjacency matrix (measuring topological similarity) into a topology overlap matrix (TOM) and detected modules by 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. We identified modules significantly associated with lipid metabolism through Pearson’s correlation analysis of module eigengenes. 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 includes three categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) [15–17] serves as a bioinformatics resource to investigate significantly enriched metabolic pathways in the gene list. The R package “clusterProfiler (version 4.2.2)” [15] was used for GO and KEGG enrichment analysis (p < 0.05) on 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 predict connections among functionally analogous and central genes, including PPI, 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 is a graph that plots test sensitivity on the y-axis against 1-specificity, or false positive rate (FPR), on the x-axis. It serves as a robust method to assess the efficacy of diagnostic tests. The most prevalent metric is the area under the curve (AUC), calculated from the ROC curve plotting sensitivity against 1 – specificity. We used the R package “pROC” [17] to generate ROC curves and calculate the AUC for screening signature genes to evaluate their diagnostic value. This value ranges from 0.5, indicating random chance, to 1, representing perfect discrimination. Generally, an AUC of 0.5 indicates no predictive capability. Values between 0.6 and 0.8 are considered acceptable, 0.8 to 0.9 are classified as excellent, and values above 0.9 are regarded as exceptional. In this study, the ROC curve analysis was based on the same dataset, and the results were only used for internal trend assessment and have not yet been externally validated by independent cohorts. Immune Infiltration Analysis Single-sample Gene Set Enrichment Analysis (ssGSEA) [18] enhances the GSEA algorithm by calculating enrichment scores for each individual sample and gene set, instead of comparing groups of samples (e.g., control vs. disease) across gene sets. Based on the 28 immune cell categories from the TISIDB (Tumor and Immune System Interactions Database) ( http://cis.hku.hk/TISIDB/index.php ) [19], the relative enrichment score of each immune cells were quantified from the gene expression profile of each sample. These immune cell types include 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 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 ) is a widely used open-source platform for analyzing ncRNA interactions based on CLIP-seq, degradome-seq, and RNA–RNA interactome data. It was employed to investigate the relationships between mRNA and RNA binding protein (RBP) expression. P < 0.05, clusterNum ≥ 5, and clipExpNum ≥ 5 were set as the threshold criteria for identifying the significant RBP-mRNA pairs in psoriasis. Subsequently, we constructed the RBP-mRNA network with Cytoscape. Statistical Analysis Statistical evaluation was conducted using R software v4.1.2. The relationship between two variables was determined by Spearman’s correlation analysis. Inter-group variations were assessed by the Wilcoxon test, while the Kruskal–Wallis test was used to compare differences among three or more groups. Two-sided p-values < 0.05 were considered as statistically significant. Results DEGs Identification We analyzed psoriasis samples and compared them 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 upregulated, while 2,064 genes were downregulated. All DEGs were displayed using a volcano plot (Fig. 1A). Moreover, the five upregulated genes (PLA2G4D, VNN3, TMPRSS11D, S100A12, SERPINB4) with the lowest adjusted p-value and the five downregulated DEGs (BTC, KRT77, BCAR3, RORC, SNTB1) with the lowest adjusted p-value were presented in a heatmap (Fig. 1B). Wilcoxon tests showed that these ten genes exhibited significant differences in expression levels between the two groups (p < 0.05, Fig. 1C). Figure 1 DEGs 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 5 upregulated and 5 downregulated DEGs. (These genes are sorted by p-value rather than fold-change. Other genes may have a higher fold-change but are not ranked at the top in the p-value sorting.) (C) The variations of 10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. Asterisks represented p-value (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05). GSEA We conducted a Gene Set Enrichment Analysis (GSEA) to further explore the potential mechanisms behind the differentially expressed genes (DEGs). Using the MSigDB Collection, we identified the most significantly enriched signaling pathways based on their normalized enrichment score (NES) (Table 1). GSEA revealed that GSEA revealed that several pathways were significantly enriched in psoriasis. These include 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 = -1.830, 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) . Table.1 The most significantly enriched signaling pathways by GESA ID Enrichment Score KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION 0.505153187 KEGG_CHEMOKINE_SIGNALING_PATHWAY 0.504188007 KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY 0.479965564 KEGG_CELL_CYCLE 0.622741942 KEGG_JAK_STAT_SIGNALING_PATHWAY 0.466640767 KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY 0.506876755 KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.624929166 KEGG_PYRIMIDINE_METABOLISM 0.555806101 KEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.668714967 KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.696794046 KEGG_TYPE_I_DIABETES_MELLITUS 0.655403835 KEGG_AUTOIMMUNE_THYROID_DISEASE 0.664808877 KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY 0.718870162 KEGG_DNA_REPLICATION 0.712001676 KEGG_ALLOGRAFT_REJECTION 0.707869952 KEGG_GRAFT_VERSUS_HOST_DISEASE 0.734505121 KEGG_PROTEASOME 0.701287858 KEGG_PRIMARY_IMMUNODEFICIENCY 0.773424838 KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC -0.536734213 KEGG_RIBOSOME -0.545778532 KEGG_CARDIAC_MUSCLE_CONTRACTION -0.57804996 KEGG_ECM_RECEPTOR_INTERACTION -0.495807716 KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM -0.59200028 KEGG_DILATED_CARDIOMYOPATHY -0.593353714 KEGG_FOCAL_ADHESION -0.476133773 KEGG_P53_SIGNALING_PATHWAY 0.574618822 KEGG_TIGHT_JUNCTION -0.409853619 KEGG_CALCIUM_SIGNALING_PATHWAY -0.413433843 GSVA To better understand the functional differences between psoriasis and control samples, we performed GSVA analyses to compare pathway expression levels in the two groups. GSVA analysis revealed numerous differentially expressed pathways, as shown in the heatmap. Compared to controls, pathways related to KEGG_BASAL_CELL_CARCINOMA and KEGG_RIBOSOME showed marked downregulation in psoriasis, whereas KEGG_DNA_REPLICATION and KEGG_HOMOLOGOUS_RECOMBINATION were significantly upregulated (Fig. 2 G). 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 was 8 (Fig. 3A), the average degree of connectivity approached 0. Meanwhile, the 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. 3B). To examine the interrelationships among modules and ascertain their correlations, we computed the MEs. Figure 3C illustrates the eigengene network through a dendrogram and a heatmap plot. To assess the physiological relevance of the modules, we correlated the 13 MEs with lipid metabolism and identified the most significant associations. According to the heatmap of module-trait correlation (Fig. 3D), genes clustered in the black module (n = 151, Table 2) 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 likely reflects lipid metabolism more accurately. We identified a total of 109 differentially expressed genes (DEGs) linked to lipid metabolism by overlapping DEGs with genes from the lipid metabolism-related module (Fig. 3E). These were considered pivotal genes. Wilcoxon test analysis showed that these 10 genes had significant differences in expression between the two groups (p < 0.05, Fig. 3F). Figure 3 Construction 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 correspond 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 10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. Asterisks represented p-value (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05). Table.2 Genes clustered in the black module according to the heatmap of module-trait correlation PM20D1 BCKDHB HEATR4 FASN CUX2 SLC44A3 ACSL1 BCAT2 THRSP ACP6 PPM1K SYPL2 ACSBG1 ALDH1L1 IL20RA ULK4 GAL ABHD5 OLAH ADAMTS15 COMP ACADM HACL1 DHRS2 ELOVL3 ACOT4 PCCB PDE6A CRAT TMEM91 SLC25A1 PECR FABP7 HIST1H2AE ACSL5 NELL1 PDZK1 ACAA2 FITM2 MOGAT1 BPY2 TLCD1 DNAH17 SLC26A3 TMPRSS11E EGR2 MECR CDA HSD11B1 DTX4 CYP3A4 SOAT1 SGK2 ACAD8 CYB5A PLIN2 FADS2 CHI3L1 DDIT4 PNPLA3 CIDEA TMEM97 IVD PCTP KRT79 PNLIPRP3 ECHDC3 LDHD ACSM3 MOCS1 AACS CDKL2 HAO2 HIST1H1C LIPH UBIAD1 CYP4F2 ACO1 CAPN13 GPT FAR2 DUSP4 DHRS7B SLC25A34 SLCO4C1 ABCA13 HMGCS1 ZNF117 HSD3B1 DGAT2 TMPRSS3 REEP6 FA2H SLC46A3 MANEAL FAM46C CYP4F8 ELOVL5 SRD5A1 PEX11A AGR2 MVD SLC25A20 PXMP2 AWAT1 METTL7B ACSS2 PKIB PNLDC1 ACOX2 HLCS PMVK GLDC TRIM55 TTLL4 DIRAS3 MUC1 TMEM56 SORD ACAT2 APOC1 AGPAT1 DHRS11 PXMP4 ALOX15B PLA2G7 AGPAT3 GK5 RARRES1 INSIG1 DHCR7 CECR2 SLC27A2 MOGAT2 BRI3BP SLC25A35 TF DNAH8 SUOX GATA6 UPB1 FBP1 HIST1H2BC LPIN1 UBE2QL1 MGST1 DOPEY2 SLC25A18 HGD CLSTN3 PKLR Enrichment Analyses (GO/KEGG) We conducted enrichment analyses of GO terms and KEGG pathways to explore the biological roles of lipid metabolism-associated DEGs. The GO analysis revealed that these genes were significantly enriched in the fatty acid metabolic process (GO:0006631), long-chain fatty acid metabolic process (GO:0001676), and biosynthetic process (GO:0006633) (BP), peroxisome (GO:0005777), microbody (GO:0042579), peroxisome and microbody, along with their membranes (GO:0005777, GO:0042579, GO:0005778, 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-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). 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. The diagnostic value of the hub genes To further substantiate the diagnostic significance of pivotal genes, we conducted receiver operating characteristic (ROC) analysis. We identified 21 hub genes with comparable area under the ROC curve (AUC) values, including AACS (AUC = 0.941), HSD11B1 (AUC = 0.939), and GATA6 (AUC = 0.916) among others (Fig. 5 A-L, Supplementary Fig. 1). Therefore, these results indicate that the recognized hub genes exhibit satisfactory discriminatory capacity as prospective biomarkers for psoriasis. Trait Gene Interaction Analysis We used the GeneMANIA database to construct a PPI network for the signature genes and identified 20 genes present within it (Fig. 6 A). To further explore the functions of the signature genes, GO and KEGG analyses were conducted on 41 genes, including 20 hub genes and 21 associated genes. The GO results indicate that these genes are significantly enriched in several processes, including the fatty acid metabolic process (GO:0006631), triglyceride metabolic process (GO:0006641), acylglycerol metabolic process (GO:0006639) (Fig. 6 B). The primary enriched pathways identified by KEGG analysis included Peroxisome (hsa04146), Glycerolipid metabolism (hsa00561), Fatty acid metabolism (hsa01212), and others (Fig. 6 C). Immune Cells Infiltration Immune cell infiltration may play a crucial role in the psoriasis pathogenesis. Therefore, we compared immune cell infiltration between psoriasis patients and control samples to better understand this relationship. Among 28 types of immune cells, the infiltration levels of 27 types were markedly different between the two groups (p < 0.05) (Fig. 7 A). Twenty types of immune cells showed significantly elevated infiltration levels in the psoriasis group compared to controls. These included Activated CD8 T cells, Effector memory CD8 T cells, Activated CD4 T cells, T follicular helper cells, Gamma delta T cells, Type 1, 17, and 2 T helper cells, Regulatory T cells, Activated B cells, Immature B cells, Memory B cells, CD56dim natural killer cells, Myeloid-derived suppressor cells, Natural killer T cells, Activated dendritic cells, Macrophages, Eosinophils, Monocytes, and Neutrophils. As illustrated in Fig. 7 B, the total immune cell infiltration profile differed significantly between the psoriasis and control groups. Signaling Pathways Involved in Signature Genes We further examined the disparities between psoriasis patients and controls across fifty HALLMARK signaling pathways using GSVA. In psoriasis patients, twenty-five HALLMARK signaling pathways were notably upregulated, including ALLOGRAFT_REJECTION, APOPTOSIS, COMPLEMENT, DNA_REPAIR, and others. Conversely, twenty pathways were significantly downregulated in psoriasis patients. These included ADIPOGENESIS, ANDROGEN_RESPONSE, ANGIOGENESIS, and others. The results are shown in Fig. 8A. We also assessed correlations between the five most significantly differentially expressed hub genes and the 50 HALLMARK signaling pathways, as illustrated in 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 RNA binding proteins (RBPs) associate with mRNA. We investigated 21 central mRNAs using StarBase and identified 16 mRNA/RBP pairs. Using the online dataset of target gene connections, we established an RBP-mRNA network comprising seventy-three nodes, fifty-seven RBPs, sixteen mRNAs, and three hundred forty-five edges. The details of the nodes and their interactions are illustrated in Fig. 9 . Discussion Psoriasis is a systemic condition, not just a skin disease. It involves complex pathophysiology, including immune dysregulation and metabolic disturbances [ 16 ]. Studies [ 17, 18 ] have demonstrated that lipid metabolism plays an important role in the pathogenesis of psoriasis by influencing inflammation and keratinocyte proliferation. By analyzing the expression profiles of lipid metabolism-related genes, we aim to uncover novel insights to improve diagnosis and treatment of psoriasis. The original research on the GSE54456 dataset was conducted by Li et al [14]. This paper systematically compared the expression characteristics of RNA-seq and Affymetrix chip platforms in psoriasis skin tissues. They pointed out that there are certain differences between the two techniques in terms of gene detection sensitivity, dynamic range, and the identification results of some differentially expressed genes. Li et al. also sorted out the technical key points and data integration strategies of cross-platform analysis, providing an important reference for the data processing and analysis of this study. Compared with Li et al. and other related literature, this study further improved the analysis process in terms of data integration methods, differential gene screening, and functional annotation. For instance, by integrating batch correction, multi-dimensional functional enrichment and immune infiltration analysis, not only were some known key genes verified, but also new candidate genes related to lipid metabolism were discovered, supplementing and expanding the understanding of the molecular mechanism of psoriasis. The DEGs identified in this study offer important insights into the molecular mechanisms of psoriasis. Notably, the differentially expressed genes included PLA2G4D, VNN3, TMPRSS11D, and S100A12. These genes, including PLA2G4D, VNN3, TMPRSS11D, S100A12, and SERPINB4, have been previously reported as differentially expressed in psoriasis in classic studies such as Suárez-Fariñas et al [12], supporting the robustness of our findings. Furthermore, the upregulated and downregulated genes cluster within specific pathways, including immune response and keratinocyte differentiation, both 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 verify these DEGs in larger cohorts. Additionally, their potential as biomarkers or therapeutic targets should be explored. Of 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. Notably, genes like AACS, HSD11B1, and GATA6 show downregulated expression in the skin lesions of patients with psoriasis. Meanwhile, ROC curve analysis demonstrated that those hub genes had high diagnostic value, with AUC 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 suggested their significant roles in the disease's pathogenesis. Though the lipid metabolism-mediated regulatory links proposed herein are transcriptome-derived hypotheses that require experimental validation, and their evidentiary rigor has not yet matched that of well-established immune pathways (e.g., IL-23/IL-17 axis) with proven clinical therapeutic efficacy. AACS (Acetoacetyl-CoA Synthetase) is a key enzyme in the ketone body utilization pathway. It can catalyze the conversion of ketone bodies (such as acetoacetic acid) to acetoacetyl-COA, thereby introducing them into the synthesis pathways of fatty acids and cholesterol. The decrease in AACS expression can partially maintain ketone body levels, thereby exerting a neuroprotective effect, which may represent a form of self-regulation. What’s more, AACS plays a mediating role in fatty acid metabolism and indirectly regulate the integrity of the skin barrier [19]. Knocking down AACS reduces lipid synthesis in cells, particularly cholesterol production, which directly weakens the skin's barrier function. AACS mainly indirectly inhibits inflammation by maintaining barrier function. ROC analysis in our study showed that AACS had a high AUC (0.941) in the early diagnosis of psoriasis, indicating its potential as a biomarker in clinical diagnosis. HSD11B1 (Hydroxysteroid 11-Beta Dehydrogenase 1) converts inactive cortisone into its active form, cortisol, inside cells. This enzyme has been implicated in regulating inflammatory responses by affecting steroid metabolism. Some study [20] found that both the activity and expression levels of HSD11B1 change with age and relate to skin function. The locally generated cortisol plays a role in maintaining homeostasis, but its excessive activation (such as in aging or chronic ultraviolet exposure) contributes to skin atrophy and a decline in barrier function. The immunosuppressive effects of cortisol may help reduce the excessive immune response and relieve the symptoms of psoriasis. Study [21] found that HSD11B1 transcripts were significantly decreased in the psoriasis patients’ lesions compared to control skin samples, which was similar to our findings. They found that the regulation of the steroidogenic pathway was disrupted in the lesional tissue of psoriasis patients, which may be either a cause or a consequence of epidermal barrier disruption. Further studies on its role in psoriasis, particularly its mechanisms in skin and immune cells, will help clarify how steroid metabolism influences the immune response in psoriasis. A study [22] found that that GATA6, which inhibits angiogenesis, was expressed at lower levels in psoriatic dermal mesenchymal stem cells (MSCs) than in the control group. This finding indicates that psoriasis tends to exhibit a functional state promoting angiogenesis. Oulès’s study [23] found that GATA6 is expressed in the infundibulum of hair follicles, the junctional zone and the upper part of sebaceous glands in normal skin, but it is significantly downregulated in skin lesions of acne patients. GATA6 prevents hyperkeratosis in the follicular infundibulum by inhibiting excessive proliferation and differentiation. For example, GATA6 upregulates the immunosuppressive molecule PD-L1 and the anti-inflammatory cytokine IL-10, demonstrating its potential anti-inflammatory role. In addition, Laudisi’s study [24] indicates that GATA6 plays a crucial role in maintaining intestinal epithelial barrier function and homeostasis. A decline in GATA6 expression consequently promotes intestinal barrier dysfunction, induces dysbiosis, and enhances local immune activation, thereby exacerbating intestinal inflammation. In our study, we observed significant enrichment of immune-related pathways in psoriasis patients. These pathways include the cytosolic DNA sensing pathway and the NOD-like receptor signaling pathway. This finding underscores the pivotal role of innate immunity in the pathogenesis of the disease. The cytosolic DNA sensing pathway is crucial for recognizing pathogen-derived and host DNA in the cytoplasm, which subsequently triggers a host defense response including the production of type I interferons [25–27]. Dysregulation of this pathway has been implicated in various autoimmune and inflammatory diseases, indicating its role in the aberrant immune response observed in psoriasis [25]. In the pathogenesis of psoriasis, the type I interferon pathway is not a core inflammatory pathway, which may be the main reason for the limited efficacy of anti-IFN drugs. Harden’s study [26] found of all 10 patients treated 4 times with 10 mg/kg of Humanized anti–IFN-γ (HuZAF), only 1 patient (10%) achieved a significant clinical response. Although it can be safe and tolerable in patients with psoriasis, had minimal efficacy in the treatment of psoriasis. Similarly, the NOD-like receptor signaling pathway plays an important role in defending against diverse pathogens and regulating inflammation and apoptosis [27]. The enrichment of these pathways suggests an ongoing immune response, likely triggered by recognition of self-DNA and microbial components, which contributes to the chronic inflammation characteristic of psoriasis. Recent single-cell/single-core sequencing studies [28–30] on psoriasis have precisely identified specific cell groups such as CD4 + TRM cells, pathogenic Th17 subsets, and SFRP2 + fibroblasts, as well as their interaction networks, revealing the heterogeneous mechanisms of local inflammation. Based on the lipid metabolism-immune infiltration association analysis of bulk transcriptome, this study further clarified the regulatory associations between core genes such as AACS and HSD11B1 and neutrophils and activated CD4 + T cells, providing a supplementary metabolic-level explanation for the functions of cell subpopulations discovered at the single-cell level. The two corroborate each other from the perspectives of high-resolution cell maps and the overall molecular regulatory network, improving the pathological picture of the immune-metabolic interaction in psoriasis. Immune infiltration analysis revealed a significant increase of 20 types of immune cells infiltrating psoriatic lesions compared to the control group. This finding supports 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 disease’s characteristic inflammatory microenvironment [1, 28 ]. 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]. Recent studies [29,9] found a significant role of lipid metabolism in the differentiation and function of Th17 cells, and elucidated distinctive molecular pathways that drive the activation of RORγt by cellular lipid metabolism. We also found significant correlation between the hub gene and the specific immune cells. For example, HSD11B1 showed strong associations with neutrophils and activated CD4 T cells. However, no similar findings have been reported in psoriasis so far, and further confirmation is required. Previous transcriptomic studies [12, 13] on psoriasis focused on Th1/Th17 activation, keratinocyte abnormalities, and systemic inflammation—e.g., Suárez-Fariñas et al.[7] identified inflammation-related genes, while Correa da Rosa et al.8 explored immune-epithelial pathways for treatment prediction. Our study differs by targeting lipid metabolism as a key pathogenic node: via WGCNA, GSEA, and immune infiltration analysis, we verified known immune pathways and identified understudied lipid metabolism-related genes (AACS, HSD11B1, GATA6) and pathways (fatty acid metabolism, peroxisome function), revealing lipid metabolism’s role in linking metabolic disturbances and immune infiltration. Furthermore, this study systematically integrated the RBP-mRNA network at the post-transcriptional regulatory level for the first time, supplementing the regulatory links that were less explored in previous studies. Compared with existing studies, our results complement the understanding of the interaction between metabolism and immunity. They provide potential therapeutic targets, including AACS, HSD11B1, and GATA6. Additionally, this study offers a new perspective on the molecular mechanisms underlying psoriasis. In addition, the cytosolic DNA sensing pathway is significantly enriched, suggesting the involvement of antiviral defense mechanisms. These mechanisms may be aberrantly activated in psoriasis, contributing to the chronic inflammatory state [25]. The identification of these pathways and the differential infiltration of immune cells highlight the complex interplay between the innate and adaptive immune systems in psoriasis. Limitations This study uses transcriptome data for exploratory analysis but has certain limitations. Firstly, the ssGSEA method has limited resolution when distinguishing immune cell subpopulations with partially overlapping functions, and the results are more suitable as overall trend cues. Secondly, the screening of differentially expressed genes mainly focuses on statistical significance, and ROC analysis results only reflect the discriminatory ability within the current analysis dataset. Therefore, these results should be interpreted with caution. Thirdly, the GO/KEGG pathway sets represent fixed annotations and cannot fully capture the dynamic regulatory processes in diseases. Finally, the type I interferon pathway was enriched in the analysis. This study has adopted methods such as ComBat for strict batch correction and reduced the influence of platform differences by unifying the screening threshold. Due to the lack of external validation with independent datasets, the results of ROC analysis need to be interpreted with caution. Future studies will consider including independent cohorts for further validation. In the future, it will be necessary to further verify the key conclusions in combination with independent external cohorts or experimental methods to enhance the robustness and generalization value of the research. Moreover, there will be a plan to deeply analyze the immune microenvironment of psoriasis in combination with single cell sequencing technology. Conclusion Our comprehensive bioinformatics analysis identified differentially expressed genes and enriched pathways related to psoriasis. This provides new insights into its molecular mechanisms. By integrating multiple datasets with advanced analytical methods, we revealed potential therapeutic targets, especially in lipid metabolism and immune response pathways. These findings not only enhance our understanding of psoriasis pathogenesis but also pave the way for the development of more effective treatment strategies. Declarations Competing interests The authors have no relevant financial interests to disclose. Funding This work was supported by The President's Fund Project of Nanfang Hospital, Southern Medical University, China (2024B055). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rui Yue. All authors commented on previous versions of the manuscript. All authors read and approved the final 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/](https:/www.ncbi.nlm.nih.gov/geo) ）. Data Availability Statement 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, Zhang H, Lin W, Lu L, Su J, Chen X. Signaling pathways and targeted therapies for psoriasis. Signal Transduct Target Ther 2023, 8 (1): 437. Parisi R, Iskandar IYK, Kontopantelis E, Augustin M, Griffiths CEM, Ashcroft DM. National, regional, and worldwide epidemiology of psoriasis: systematic analysis and modelling study. Bmj 2020, 369 : m1590. Greb JE, Goldminz AM, Elder JT, Lebwohl MG, Gladman DD, Wu JJ, et al. Psoriasis. Nat Rev Dis Primers 2016, 2 : 16082. Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker J. Psoriasis. Lancet 2021, 397 (10281): 1301–1315. Ghoreschi K, Balato A, Enerbäck C, Sabat R. Therapeutics targeting the IL-23 and IL-17 pathway in psoriasis. Lancet 2021, 397 (10275): 754–766. Bray JK, Cline A, Feldman SR. Assessing perceived adverse effects of biologic medications for patients with psoriasis. J Am Acad Dermatol 2020, 82 (3): 766–768. Sun Y, Lou F, Cai X, Wang Z, Yang X, Sun L, et al. CCR7(+) dendritic cells expressing both IL-23A and IL-12B potentially contribute to psoriasis relapse. Nat Commun 2025, 16 (1): 7581. Wu JJ, Kavanaugh A, Lebwohl MG, Gniadecki R, Merola JF. Psoriasis and metabolic syndrome: implications for the management and treatment of psoriasis. J Eur Acad Dermatol Venereol 2022, 36 (6): 797–806. Kanno T, Nakajima T, Miyako K, Endo Y. Lipid metabolism in Th17 cell function. Pharmacol Ther 2023, 245 : 108411. Wroński A, Wójcik P. Impact of ROS-Dependent Lipid Metabolism on Psoriasis Pathophysiology. Int J Mol Sci 2022, 23 (20). Zhang X, Li X, Wang Y, Chen Y, Hu Y, Guo C, et al. Abnormal lipid metabolism in epidermal Langerhans cells mediates psoriasis-like dermatitis. JCI Insight 2022, 7 (13). Suárez-Fariñas M, Li K, Fuentes-Duculan J, Hayden K, Brodmerkel C, Krueger JG. Expanding the psoriasis disease profile: interrogation of the skin and serum of patients with moderate-to-severe psoriasis. J Invest Dermatol 2012, 132 (11): 2552–2564. Correa da Rosa J, Kim J, Tian S, Tomalin LE, Krueger JG, Suárez-Fariñas M. Shrinking the Psoriasis Assessment Gap: Early Gene-Expression Profiling Accurately Predicts Response to Long-Term Treatment. J Invest Dermatol 2017, 137 (2): 305–312. Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A, et al. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol 2014, 134 (7): 1828–1838. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res 2023, 51 (D1): D587–d592. Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. Jama 2020, 323 (19): 1945–1960. Cui L, Wu Y, Chen Z, Li B, Cai J, Chang Z, et al. N(6)-methyladenosine modification-tuned lipid metabolism controls skin immune homeostasis via regulating neutrophil chemotaxis. Sci Adv 2024, 10 (40): eadp5332. Seiringer P, Hillig C, Schäbitz A, Jargosch M, Pilz AC, Eyerich S, et al. Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis. Front Immunol 2024, 15 : 1334844. Bergstrom JD. The lipogenic enzyme acetoacetyl-CoA synthetase and ketone body utilization for denovo lipid synthesis, a review. J Lipid Res 2023, 64 (8): 100407. Tiganescu A, Walker EA, Hardy RS, Mayes AE, Stewart PM. Localization, age- and site-dependent expression, and regulation of 11β-hydroxysteroid dehydrogenase type 1 in skin. J Invest Dermatol 2011, 131 (1): 30–36. Başar Kılıç Ş, Taheri S, Mehmetbeyoğlu Duman E, Öksüm Solak E, Yılmaz Şükranlı Z, Rassoulzadegan M, et al. Psoriatic skin transcript phenotype: androgen/estrogen and cortisone/cortisol imbalance with increasing DNA damage response. Mol Biol Rep 2024, 51 (1): 933. Hou R, Yan H, Niu X, Chang W, An P, Wang C, et al. Gene expression profile of dermal mesenchymal stem cells from patients with psoriasis. J Eur Acad Dermatol Venereol 2014, 28 (12): 1782–1791. Oulès B, Philippeos C, Segal J, Tihy M, Vietri Rudan M, Cujba AM, et al. Contribution of GATA6 to homeostasis of the human upper pilosebaceous unit and acne pathogenesis. Nat Commun 2020, 11 (1): 5067. Laudisi F, Stolfi C, Bevivino G, Maresca C, Franzè E, Troncone E, et al. GATA6 Deficiency Leads to Epithelial Barrier Dysfunction and Enhances Susceptibility to Gut Inflammation. J Crohns Colitis 2022, 16 (2): 301–311. Pan Y, You Y, Sun L, Sui Q, Liu L, Yuan H, et al. The STING antagonist H-151 ameliorates psoriasis via suppression of STING/NF-κB-mediated inflammation. Br J Pharmacol 2021, 178 (24): 4907–4922. Harden JL, Johnson-Huang LM, Chamian MF, Lee E, Pearce T, Leonardi CL, et al. Humanized anti-IFN-γ (HuZAF) in the treatment of psoriasis. J Allergy Clin Immunol 2015, 135 (2): 553–556. Griebel T, Maekawa T, Parker JE. NOD-like receptor cooperativity in effector-triggered immunity. Trends Immunol 2014, 35 (11): 562–570. Kumar R, Theiss AL, Venuprasad K. RORγt protein modifications and IL-17-mediated inflammation. Trends Immunol 2021, 42 (11): 1037–1050. Kanno T, Miyako K, Endo Y. Lipid metabolism: a central modulator of RORγt-mediated Th17 cell differentiation. Int Immunol 2024, 36 (10): 487–496. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx 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. 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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-8999012","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601071951,"identity":"5055b3ca-3440-4203-9298-c4f45aca461c","order_by":0,"name":"Rui Yue","email":"","orcid":"","institution":"Nanfang Hospital Zengcheng Campus, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yue","suffix":""},{"id":601071952,"identity":"ad80b04f-4851-4db5-8a05-8d723d663738","order_by":1,"name":"Xuebiao Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBCDBDYGBsYHCRU2pGlhNnhwJo0ELUDMJvmw7RBhpebsvYdf87bZ5PGxHz5WkcB2gIG/vTsBrxbLnnNpljPb0orZeNLSbiTw3GGQOHN2A14tBjdyzAw+th1ObGPIMbuRIPGMwUAil4CW+2/MDBLb/ie28b8xK0gwOEyElhs8xg8+th1IbJPIMWNISCBCi2VPjhnjjHPJxWwSz5IlEg6k8RD0izn7GePPPGV2efL9yQc//vxnI8ff3kvAYcDokEAW4MGrHKqF+QNBVaNgFIyCUTCyAQDIzEopWiFSOAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanfang Hospital Zengcheng Campus, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xuebiao","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2026-03-01 03:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999012/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999012/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104179537,"identity":"3db2ce64-129b-419e-bc87-d3941a44b7f4","added_by":"auto","created_at":"2026-03-08 17:05:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193589,"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 5 upregulated and 5 downregulated DEGs. (These genes are sorted by p-value rather than fold-change. Other genes may have a higher fold-change but are not ranked at the top in the p-value sorting.) (C) The variations of 10-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":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/4e5e8e3f6ac8f2dfcdd3f6b2.jpg"},{"id":104404567,"identity":"e70566f3-4c8f-4f49-bf64-f5af72159d9c","added_by":"auto","created_at":"2026-03-11 12:20:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328540,"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":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/8602b5b5c31a0c3c722fd15e.jpg"},{"id":104179539,"identity":"97e506d7-49e4-4c06-804f-4c14eb9cb9db","added_by":"auto","created_at":"2026-03-08 17:05:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161718,"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 correspond 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 10-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":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/8170fad92a5c99e1203f7041.jpg"},{"id":104179541,"identity":"cd7066f4-2893-4728-ae7c-ec2ca8868a1f","added_by":"auto","created_at":"2026-03-08 17:05:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211956,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment based on Lipid metabolism -related DEGs. (A) GO pathways\u003c/p\u003e\n\u003cp\u003eresults. (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":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/aec453354a8348271c2356b2.jpg"},{"id":104179544,"identity":"e45b8239-0853-4d5e-aa83-841b52a4c73a","added_by":"auto","created_at":"2026-03-08 17:05:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":170618,"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":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/9a3db3825fa2d8cf178fcc2f.jpg"},{"id":104404189,"identity":"c1c13bc5-7014-4be7-a1cf-b2de61116695","added_by":"auto","created_at":"2026-03-11 12:19:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250664,"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":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/e0d55299f638dd26bb74cd44.jpg"},{"id":104404054,"identity":"64c3dab3-c700-4bec-95c2-00ee0340d915","added_by":"auto","created_at":"2026-03-11 12:19:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":321185,"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":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/783119fd13778f6af194910f.jpg"},{"id":104404621,"identity":"d07c8267-6a27-4cbf-8caa-a55983866294","added_by":"auto","created_at":"2026-03-11 12:20:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":401246,"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":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/870f3e92b55c17a3d495799b.jpg"},{"id":104405039,"identity":"199a6f94-c5f4-47f4-8fb9-0281b7fc8f3c","added_by":"auto","created_at":"2026-03-11 12:21:40","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":246126,"visible":true,"origin":"","legend":"\u003cp\u003eThe RBP-mRNA regulatory network orange color represents RBPs, and pink color represents mRNAs.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/2e4f299e7691e8bb47a1f180.jpg"},{"id":105705768,"identity":"694e02ec-485d-4241-83f1-fa23e680c247","added_by":"auto","created_at":"2026-03-30 06:58:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2960374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/682763b3-a0c4-4646-8137-0c60205f6387.pdf"},{"id":104179543,"identity":"ec0a93c3-4b6d-435d-bd5e-ba9c6c1bc936","added_by":"auto","created_at":"2026-03-08 17:05:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":359711,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8999012/v1/5f423a88bac7320197687b3a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes in Psoriasis Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis is a chronic, immune-mediated inflammatory skin disorder that affects approximately 2% to 3% of the global population [1, 2]. Characterized by red, scaly plaques, this condition is not only a cosmetic concern but also affects the patients\u0026rsquo; quality of life [3, 4]. Therapeutics that specifically target IL-23, IL-17, and IL-17RA are approved for clinical use and show excellent efficacy[5]. Despite the presence of biological agents, many patients still experience relapses and adverse effects [6, 7]. Research found that severe psoriasis is associated with an increased risk of mortality related to metabolic syndrome [8]. 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 likely contributes to psoriasis pathogenesis by influencing inflammatory cascades and keratinocyte proliferation [9, 10]. Abnormalities in lipid metabolism leads to lipid accumulation, which exacerbates skin inflammation [11]. Immune cells, lipids, and inflammatory mediators perpetuate a cycle of damage. Modulating lipid metabolism to disrupt this cycle holds promise for developing targeted psoriasis therapies that improve patient quality of life.\u003c/p\u003e \u003cp\u003eIn this study, we leverage advanced bioinformatics techniques to investigate gene expression changes associated with lipid metabolism in psoriasis. We employ several bioinformatics methods, including differential expression analysis (DEGs), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA), to identify key regulatory genes and pathways involved in the disease. By focusing on lipid metabolism-related genes, we aim to elucidate their roles in the inflammatory processes characteristic of psoriasis and potentially uncover novel biomarkers that could enhance diagnostic and therapeutic strategies. The main objective of this study is to identify the lipid metabolism-related genes differential expressed in psoriasis compared to healthy controls. By integrating transcriptomic data with biological pathway analysis, we seek to provide a comprehensive understanding of how lipid metabolism intersects with the immune dysregulation observed in psoriasis. Our findings aim to contribute valuable insights into the molecular landscape of psoriasis and open new avenues for the development of targeted therapies that address the disease's underlying pathophysiology.\u003c/p\u003e \u003cp\u003eThis study integrates multiple datasets and uses advanced bioinformatics methods to provide a deeper and reliable understanding of the molecular mechanisms underlying psoriasis. We expect the findings from this research to contribute to developing more effective treatments, ultimately improving the quality of life for patients with this debilitating condition.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData Sources and Preprocessing\u003c/p\u003e \u003cp\u003eAll data used in this study are publicly available, mainly 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 retrieved using the R package \u0026lsquo;GEOquery\u0026rsquo; (version 2.25.0) from the GEO repository. GSE30999 contains skin biopsy samples from 85 psoriasis patients and 85 healthy controls [12]. GSE54456 encompasses skin biopsy samples from 92 psoriasis patients and 82 healthy controls [13, 14]. Batch effects caused by non-biological technical biases were corrected using the ComBat method from the R package \u0026ldquo;sva\u0026rdquo; (version 2.25.0) [7]. Principal component analysis (PCA) was used to evaluate the effectiveness of batch effect 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 [8,9] ( \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).\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 identified using the \u0026ldquo;limma (version 3.50.0)\u0026rdquo; [10] package in R. The criteria applied were |log2 Fold Change| \u0026gt; 0.5 and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. These DEGs were used in the subsequent analyses. Next, the heatmap was generated using the R package \u0026ldquo;pheatmap\u0026rdquo; with Euclidean distance and complete linkage clustering.\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003c/p\u003e \u003cp\u003eThe Gene Set Enrichment Analysis (GSEA) [8] is a computational method. It evaluates whether predefined gene groups show statistically significant and consistent changes between two biological conditions. GSEA was performed using the R package \u0026ldquo;clusterProfiler (version 4.2.2)\u0026rdquo; on a ranked list of all genes based on their log2 Fold Change values. Gene set permutations were conducted one thousand times for each analysis. The study employed c2.cp.kegg v7.5.1 gene sets from the Molecular Signatures Database (MSigDB) [8,9,11] as the reference gene collection. Gene sets with an adjusted p-value\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. It uses gene expression profiles to evaluate associations between biological pathways and gene characteristics. To explore the disparities in biological functions between the control and psoriasis groups, 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;heatmap (version 1.0.12)\u0026rdquo; was used to generate heatmaps of the results. 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. After calculating the enrichment scores, the Limma package was used to analyze differences in gene set scores 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\u003eWe constructed co-expression networks using the WGCNA algorithm from the R WGCNA package (version 1.70-3) [12]. We computed the Pearson correlation coefficient to evaluate gene expression similarity, then weighted these coefficients 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 a cluster of densely co-expressed genes. WGCNA employs hierarchical clustering to discern gene modules, with colors representing distinct modules. We used the dynamic tree cut method to identify modules. During module selection, we transformed the adjacency matrix (measuring topological similarity) into a topology overlap matrix (TOM) and detected modules by 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. We identified modules significantly associated with lipid metabolism through Pearson\u0026rsquo;s correlation analysis of module eigengenes. 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) [13] enrichment analysis includes three categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) [15\u0026ndash;17] serves as a bioinformatics resource to investigate significantly enriched metabolic pathways in the gene list. The R package \u0026ldquo;clusterProfiler (version 4.2.2)\u0026rdquo; [15] was used for GO and KEGG enrichment analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on 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) [16] which can also predict connections among functionally analogous and central genes, including PPI, 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 is a graph that plots test sensitivity on the y-axis against 1-specificity, or false positive rate (FPR), on the x-axis. It serves as a robust method to assess the efficacy of diagnostic tests. The most prevalent metric is the area under the curve (AUC), calculated from the ROC curve plotting sensitivity against 1 \u0026ndash; specificity. We used the R package \u0026ldquo;pROC\u0026rdquo; [17] to generate ROC curves and calculate the AUC for screening signature genes to evaluate their diagnostic value. This value ranges from 0.5, indicating random chance, to 1, representing perfect discrimination. Generally, an AUC of 0.5 indicates no predictive capability. Values between 0.6 and 0.8 are considered acceptable, 0.8 to 0.9 are classified as excellent, and values above 0.9 are regarded as exceptional. In this study, the ROC curve analysis was based on the same dataset, and the results were only used for internal trend assessment and have not yet been externally validated by independent cohorts.\u003c/p\u003e \u003cp\u003eImmune Infiltration Analysis\u003c/p\u003e \u003cp\u003eSingle-sample Gene Set Enrichment Analysis (ssGSEA) [18] enhances the GSEA algorithm by calculating enrichment scores for each individual sample and gene set, instead of comparing groups of samples (e.g., control vs. disease) across gene sets.\u003c/p\u003e \u003cp\u003eBased on the 28 immune cell categories 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) [19], the relative enrichment score of each immune cells were quantified from the gene expression profile of each sample. These immune cell types include 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 immune cells among samples in the psoriasis and control groups were depicted using the R package ggplot2 (version 3.3.6) [20].\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) is a widely used open-source platform for analyzing ncRNA interactions based on CLIP-seq, degradome-seq, and RNA\u0026ndash;RNA interactome data. It was employed to investigate the relationships between mRNA and RNA binding protein (RBP) expression. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, clusterNum\u0026thinsp;\u0026ge;\u0026thinsp;5, and clipExpNum\u0026thinsp;\u0026ge;\u0026thinsp;5 were set as the threshold criteria for identifying the significant RBP-mRNA pairs in psoriasis. Subsequently, we constructed the RBP-mRNA network with Cytoscape.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical evaluation was conducted using R software v4.1.2. The relationship between two variables was determined by Spearman\u0026rsquo;s correlation analysis. Inter-group variations were assessed by the Wilcoxon test, while the Kruskal\u0026ndash;Wallis test was used to compare differences among three or more groups. Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDEGs Identification\u003c/p\u003e \u003cp\u003eWe analyzed psoriasis samples and compared them 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 upregulated, while 2,064 genes were downregulated. All DEGs were displayed using a volcano plot (Fig.\u0026nbsp;1A). Moreover, the five upregulated genes (PLA2G4D, VNN3, TMPRSS11D, S100A12, SERPINB4) with the lowest adjusted p-value and the five downregulated DEGs (BTC, KRT77, BCAR3, RORC, SNTB1) with the lowest adjusted p-value were presented in a heatmap (Fig.\u0026nbsp;1B). Wilcoxon tests showed that these ten genes exhibited significant differences in expression levels between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;1C).\u003cdiv description=\"图表, 箱线图描述已自动生成\" class=\"Drawing\" id=\"1174261067\" name=\"图片 1174261067\"\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1 DEGs 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 5 upregulated and 5 downregulated DEGs. (These genes are sorted by p-value rather than fold-change. Other genes may have a higher fold-change but are not ranked at the top in the p-value sorting.) (C) The variations of 10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. 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\n\u003ch3\u003eGSEA\u003c/h3\u003e\n\u003cp\u003eWe conducted a Gene Set Enrichment Analysis (GSEA) to further explore the potential mechanisms behind the differentially expressed genes (DEGs). Using the MSigDB Collection, we identified the most significantly enriched signaling pathways based on their normalized enrichment score (NES) (Table\u0026nbsp;1). GSEA revealed that GSEA revealed that several pathways were significantly enriched in psoriasis. These include 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 = -1.830, 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=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F) .\u003c/p\u003e \u003cp\u003eTable.1 The most significantly enriched signaling pathways by GESA\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnrichment Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.505153187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CHEMOKINE_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.504188007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.479965564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CELL_CYCLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.622741942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_JAK_STAT_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.466640767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.506876755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.624929166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_PYRIMIDINE_METABOLISM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.555806101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668714967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.696794046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_TYPE_I_DIABETES_MELLITUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655403835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_AUTOIMMUNE_THYROID_DISEASE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.664808877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CYTOSOLIC_DNA_SENSING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.718870162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_DNA_REPLICATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.712001676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_ALLOGRAFT_REJECTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707869952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_GRAFT_VERSUS_HOST_DISEASE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.734505121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_PROTEASOME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.701287858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_PRIMARY_IMMUNODEFICIENCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.773424838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.536734213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_RIBOSOME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.545778532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CARDIAC_MUSCLE_CONTRACTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.57804996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_ECM_RECEPTOR_INTERACTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.495807716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.59200028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_DILATED_CARDIOMYOPATHY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.593353714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_FOCAL_ADHESION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.476133773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_P53_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.574618822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_TIGHT_JUNCTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.409853619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_CALCIUM_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.413433843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eGSVA\u003c/h3\u003e\n\u003cp\u003eTo better understand the functional differences between psoriasis and control samples, we performed GSVA analyses to compare pathway expression levels in the two groups. GSVA analysis revealed numerous differentially expressed pathways, as shown in the heatmap. Compared to controls, pathways related to KEGG_BASAL_CELL_CARCINOMA and KEGG_RIBOSOME showed marked downregulation in psoriasis, whereas KEGG_DNA_REPLICATION and KEGG_HOMOLOGOUS_RECOMBINATION were significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\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 was 8 (Fig.\u0026nbsp;3A), the average degree of connectivity approached 0. Meanwhile, the 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;3B). To examine the interrelationships among modules and ascertain their correlations, we computed the MEs. Figure\u0026nbsp;3C illustrates the eigengene network through a dendrogram and a heatmap plot. To assess the physiological relevance of the modules, we correlated the 13 MEs with lipid metabolism and identified the most significant associations. According to the heatmap of module-trait correlation (Fig.\u0026nbsp;3D), genes clustered in the black module (n\u0026thinsp;=\u0026thinsp;151, Table\u0026nbsp;2) 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 likely reflects lipid metabolism more accurately.\u003c/p\u003e \u003cp\u003eWe identified a total of 109 differentially expressed genes (DEGs) linked to lipid metabolism by overlapping DEGs with genes from the lipid metabolism-related module (Fig.\u0026nbsp;3E). These were considered pivotal genes. Wilcoxon test analysis showed that these 10 genes had significant differences in expression between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;3F).\u003cdiv description=\"\" class=\"Drawing\" id=\"3\" name=\"图片 3\"\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;3 Construction of WGCNA co\u0026ndash;expression network. (A) Soft threshold β\u0026thinsp;=\u0026thinsp;8 and scale\u0026ndash;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 correspond 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 10-gene expression levels between psoriasis and control groups were revealed by Wilcoxon tests. 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\u003eTable.2 Genes clustered in the black module according to the heatmap of module-trait correlation\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM20D1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCKDHB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHEATR4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFASN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCUX2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLC44A3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACSL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBCAT2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHRSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPM1K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSYPL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACSBG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eALDH1L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL20RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eULK4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABHD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADAMTS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACADM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHACL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDHRS2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELOVL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACOT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDE6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCRAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTMEM91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLC25A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePECR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFABP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIST1H2AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACSL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNELL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDZK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACAA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFITM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMOGAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLCD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNAH17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLC26A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTMPRSS11E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEGR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMECR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCDA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSD11B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCYP3A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSGK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACAD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCYB5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePLIN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFADS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHI3L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDDIT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNPLA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCIDEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTMEM97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePCTP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePNLIPRP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eECHDC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLDHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACSM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMOCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAACS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCDKL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIST1H1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLIPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUBIAD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCYP4F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCAPN13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDUSP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHRS7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLC25A34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLCO4C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABCA13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHMGCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZNF117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSD3B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDGAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTMPRSS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREEP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFA2H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLC46A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMANEAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFAM46C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP4F8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELOVL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRD5A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEX11A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAGR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLC25A20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePXMP2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAWAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMETTL7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACSS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePKIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePNLDC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACOX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHLCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePMVK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRIM55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTLL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIRAS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMUC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTMEM56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSORD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACAT2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGPAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHRS11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePXMP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eALOX15B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePLA2G7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAGPAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGK5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRARRES1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINSIG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHCR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCECR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLC27A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMOGAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBRI3BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSLC25A35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNAH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGATA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUPB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIST1H2BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLPIN1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBE2QL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOPEY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLC25A18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCLSTN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePKLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eEnrichment Analyses (GO/KEGG)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe conducted enrichment analyses of GO terms and KEGG pathways to explore the biological roles of lipid metabolism-associated DEGs. The GO analysis revealed that these genes were significantly enriched in the fatty acid metabolic process (GO:0006631), long-chain fatty acid metabolic process (GO:0001676), and biosynthetic process (GO:0006633) (BP), peroxisome (GO:0005777), microbody (GO:0042579), peroxisome and microbody, along with their membranes (GO:0005777, GO:0042579, GO:0005778, 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=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). Beyond the GO terms, the enriched KEGG pathways included Peroxisome (hsa04146), Fatty acid metabolism (hsa01212), and Glycerolipid metabolism (hsa00561) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eresults. (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 \u003cp\u003eThe diagnostic value of the hub genes\u003c/p\u003e \u003cp\u003eTo further substantiate the diagnostic significance of pivotal genes, we conducted receiver operating characteristic (ROC) analysis. We identified 21 hub genes with comparable area under the ROC curve (AUC) values, including AACS (AUC\u0026thinsp;=\u0026thinsp;0.941), HSD11B1 (AUC\u0026thinsp;=\u0026thinsp;0.939), and GATA6 (AUC\u0026thinsp;=\u0026thinsp;0.916) among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-L, Supplementary Fig.\u0026nbsp;1). Therefore, these results indicate that the recognized hub genes exhibit 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 used the GeneMANIA database to construct a PPI network for the signature genes and identified 20 genes present within it (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To further explore the functions of the signature genes, GO and KEGG analyses were conducted on 41 genes, including 20 hub genes and 21 associated genes. The GO results indicate that these genes are significantly enriched in several processes, including the fatty acid metabolic process (GO:0006631), triglyceride metabolic process (GO:0006641), acylglycerol metabolic process (GO:0006639) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The primary enriched pathways identified by KEGG analysis included Peroxisome (hsa04146), Glycerolipid metabolism (hsa00561), Fatty acid metabolism (hsa01212), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmune Cells Infiltration\u003c/p\u003e \u003cp\u003eImmune cell infiltration may play a crucial role in the psoriasis pathogenesis. Therefore, we compared immune cell infiltration between psoriasis patients and control samples to better understand this relationship. Among 28 types 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=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Twenty types of immune cells showed significantly elevated infiltration levels in the psoriasis group compared to controls. These included Activated CD8 T cells, Effector memory CD8 T cells, Activated CD4 T cells, T follicular helper cells, Gamma delta T cells, Type 1, 17, and 2 T helper cells, Regulatory T cells, Activated B cells, Immature B cells, Memory B cells, CD56dim natural killer cells, Myeloid-derived suppressor cells, Natural killer T cells, Activated dendritic cells, Macrophages, Eosinophils, Monocytes, and Neutrophils. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, the total immune cell infiltration profile differed significantly between the psoriasis and control groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignaling Pathways Involved in Signature Genes\u003c/p\u003e \u003cp\u003eWe further examined the disparities between psoriasis patients and controls across fifty HALLMARK signaling pathways using GSVA. In psoriasis patients, twenty-five HALLMARK signaling pathways were notably upregulated, including ALLOGRAFT_REJECTION, APOPTOSIS, COMPLEMENT, DNA_REPAIR, and others. Conversely, twenty pathways were significantly downregulated in psoriasis patients. These included ADIPOGENESIS, ANDROGEN_RESPONSE, ANGIOGENESIS, and others. The results are shown in Fig.\u0026nbsp;8A. We also assessed correlations between the five most significantly differentially expressed hub genes and the 50 HALLMARK signaling pathways, as illustrated in Fig.\u0026nbsp;8B.\u003cdiv description=\"\" class=\"Drawing\" id=\"8\" name=\"图片 8\"\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;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\u003eRNA binding proteins (RBPs) associate with mRNA. We investigated 21 central mRNAs using StarBase and identified 16 mRNA/RBP pairs. Using the online dataset of target gene connections, we established an RBP-mRNA network comprising seventy-three nodes, fifty-seven RBPs, sixteen mRNAs, and three hundred forty-five edges. The details of the nodes and their interactions are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePsoriasis is a systemic condition, not just a skin disease. It involves complex pathophysiology, including immune dysregulation and metabolic disturbances [\u003csup\u003e16\u003c/sup\u003e]. Studies [\u003csup\u003e17, 18\u003c/sup\u003e] have demonstrated that lipid metabolism plays an important role in the pathogenesis of psoriasis by influencing inflammation and keratinocyte proliferation. By analyzing the expression profiles of lipid metabolism-related genes, we aim to uncover novel insights to improve diagnosis and treatment of psoriasis.\u003c/p\u003e \u003cp\u003eThe original research on the GSE54456 dataset was conducted by Li et al [14]. This paper systematically compared the expression characteristics of RNA-seq and Affymetrix chip platforms in psoriasis skin tissues. They pointed out that there are certain differences between the two techniques in terms of gene detection sensitivity, dynamic range, and the identification results of some differentially expressed genes. Li et al. also sorted out the technical key points and data integration strategies of cross-platform analysis, providing an important reference for the data processing and analysis of this study. Compared with Li et al. and other related literature, this study further improved the analysis process in terms of data integration methods, differential gene screening, and functional annotation. For instance, by integrating batch correction, multi-dimensional functional enrichment and immune infiltration analysis, not only were some known key genes verified, but also new candidate genes related to lipid metabolism were discovered, supplementing and expanding the understanding of the molecular mechanism of psoriasis.\u003c/p\u003e \u003cp\u003eThe DEGs identified in this study offer important insights into the molecular mechanisms of psoriasis. Notably, the differentially expressed genes included PLA2G4D, VNN3, TMPRSS11D, and S100A12. These genes, including PLA2G4D, VNN3, TMPRSS11D, S100A12, and SERPINB4, have been previously reported as differentially expressed in psoriasis in classic studies such as Su\u0026aacute;rez-Fari\u0026ntilde;as et al [12], supporting the robustness of our findings.\u003c/p\u003e \u003cp\u003eFurthermore, the upregulated and downregulated genes cluster within specific pathways, including immune response and keratinocyte differentiation, both 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 verify these DEGs in larger cohorts. Additionally, their potential as biomarkers or therapeutic targets should be explored.\u003c/p\u003e \u003cp\u003eOf 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. Notably, genes like AACS, HSD11B1, and GATA6 show downregulated expression in the skin lesions of patients with psoriasis. Meanwhile, ROC curve analysis demonstrated that those hub genes had high diagnostic value, with AUC 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 suggested their significant roles in the disease's pathogenesis. Though the lipid metabolism-mediated regulatory links proposed herein are transcriptome-derived hypotheses that require experimental validation, and their evidentiary rigor has not yet matched that of well-established immune pathways (e.g., IL-23/IL-17 axis) with proven clinical therapeutic efficacy.\u003c/p\u003e \u003cp\u003eAACS (Acetoacetyl-CoA Synthetase) is a key enzyme in the ketone body utilization pathway. It can catalyze the conversion of ketone bodies (such as acetoacetic acid) to acetoacetyl-COA, thereby introducing them into the synthesis pathways of fatty acids and cholesterol. The decrease in AACS expression can partially maintain ketone body levels, thereby exerting a neuroprotective effect, which may represent a form of self-regulation. What\u0026rsquo;s more, AACS plays a mediating role in fatty acid metabolism and indirectly regulate the integrity of the skin barrier [19]. Knocking down AACS reduces lipid synthesis in cells, particularly cholesterol production, which directly weakens the skin's barrier function. AACS mainly indirectly inhibits inflammation by maintaining barrier function. ROC analysis in our study showed that AACS had a high AUC (0.941) in the early diagnosis of psoriasis, indicating its potential as a biomarker in clinical diagnosis.\u003c/p\u003e \u003cp\u003eHSD11B1 (Hydroxysteroid 11-Beta Dehydrogenase 1) converts inactive cortisone into its active form, cortisol, inside cells. This enzyme has been implicated in regulating inflammatory responses by affecting steroid metabolism. Some study [20] found that both the activity and expression levels of HSD11B1 change with age and relate to skin function. The locally generated cortisol plays a role in maintaining homeostasis, but its excessive activation (such as in aging or chronic ultraviolet exposure) contributes to skin atrophy and a decline in barrier function. The immunosuppressive effects of cortisol may help reduce the excessive immune response and relieve the symptoms of psoriasis. Study [21] found that HSD11B1 transcripts were significantly decreased in the psoriasis patients\u0026rsquo; lesions compared to control skin samples, which was similar to our findings. They found that the regulation of the steroidogenic pathway was disrupted in the lesional tissue of psoriasis patients, which may be either a cause or a consequence of epidermal barrier disruption. Further studies on its role in psoriasis, particularly its mechanisms in skin and immune cells, will help clarify how steroid metabolism influences the immune response in psoriasis.\u003c/p\u003e \u003cp\u003eA study [22] found that that GATA6, which inhibits angiogenesis, was expressed at lower levels in psoriatic dermal mesenchymal stem cells (MSCs) than in the control group. This finding indicates that psoriasis tends to exhibit a functional state promoting angiogenesis. Oul\u0026egrave;s\u0026rsquo;s study [23] found that GATA6 is expressed in the infundibulum of hair follicles, the junctional zone and the upper part of sebaceous glands in normal skin, but it is significantly downregulated in skin lesions of acne patients. GATA6 prevents hyperkeratosis in the follicular infundibulum by inhibiting excessive proliferation and differentiation. For example, GATA6 upregulates the immunosuppressive molecule PD-L1 and the anti-inflammatory cytokine IL-10, demonstrating its potential anti-inflammatory role. In addition, Laudisi\u0026rsquo;s study [24] indicates that GATA6 plays a crucial role in maintaining intestinal epithelial barrier function and homeostasis. A decline in GATA6 expression consequently promotes intestinal barrier dysfunction, induces dysbiosis, and enhances local immune activation, thereby exacerbating intestinal inflammation.\u003c/p\u003e \u003cp\u003eIn our study, we observed significant enrichment of immune-related pathways in psoriasis patients. These pathways include the cytosolic DNA sensing pathway and the NOD-like receptor signaling pathway. This finding underscores the pivotal role of innate immunity in the pathogenesis of the disease. The cytosolic DNA sensing pathway is crucial for recognizing pathogen-derived and host DNA in the cytoplasm, which subsequently triggers a host defense response including the production of type I interferons [25\u0026ndash;27]. Dysregulation of this pathway has been implicated in various autoimmune and inflammatory diseases, indicating its role in the aberrant immune response observed in psoriasis [25]. In the pathogenesis of psoriasis, the type I interferon pathway is not a core inflammatory pathway, which may be the main reason for the limited efficacy of anti-IFN drugs. Harden\u0026rsquo;s study [26] found of all 10 patients treated 4 times with 10 mg/kg of Humanized anti\u0026ndash;IFN-γ (HuZAF), only 1 patient (10%) achieved a significant clinical response. Although it can be safe and tolerable in patients with psoriasis, had minimal efficacy in the treatment of psoriasis. Similarly, the NOD-like receptor signaling pathway plays an important role in defending against diverse pathogens and regulating inflammation and apoptosis [27]. The enrichment of these pathways suggests an ongoing immune response, likely triggered by recognition of self-DNA and microbial components, which contributes to the chronic inflammation characteristic of psoriasis.\u003c/p\u003e \u003cp\u003eRecent single-cell/single-core sequencing studies [28\u0026ndash;30] on psoriasis have precisely identified specific cell groups such as CD4\u003csup\u003e+\u003c/sup\u003e TRM cells, pathogenic Th17 subsets, and SFRP2 \u003csup\u003e+\u003c/sup\u003e fibroblasts, as well as their interaction networks, revealing the heterogeneous mechanisms of local inflammation. Based on the lipid metabolism-immune infiltration association analysis of bulk transcriptome, this study further clarified the regulatory associations between core genes such as AACS and HSD11B1 and neutrophils and activated CD4\u003csup\u003e+\u003c/sup\u003e T cells, providing a supplementary metabolic-level explanation for the functions of cell subpopulations discovered at the single-cell level. The two corroborate each other from the perspectives of high-resolution cell maps and the overall molecular regulatory network, improving the pathological picture of the immune-metabolic interaction in psoriasis.\u003c/p\u003e \u003cp\u003eImmune infiltration analysis revealed a significant increase of 20 types of immune cells infiltrating psoriatic lesions compared to the control group. This finding supports 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 disease\u0026rsquo;s characteristic inflammatory microenvironment [1,\u003csup\u003e28\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 [2]. Recent studies [29,9] found a significant role of lipid metabolism in the differentiation and function of Th17 cells, and elucidated distinctive molecular pathways that drive the activation of RORγt by cellular lipid metabolism.\u003c/p\u003e \u003cp\u003eWe also found significant correlation between the hub gene and the specific immune cells. For example, HSD11B1 showed strong associations with neutrophils and activated CD4 T cells. However, no similar findings have been reported in psoriasis so far, and further confirmation is required.\u003c/p\u003e \u003cp\u003ePrevious transcriptomic studies [12, 13] on psoriasis focused on Th1/Th17 activation, keratinocyte abnormalities, and systemic inflammation\u0026mdash;e.g., Su\u0026aacute;rez-Fari\u0026ntilde;as et al.[7] identified inflammation-related genes, while Correa da Rosa et al.8 explored immune-epithelial pathways for treatment prediction. Our study differs by targeting lipid metabolism as a key pathogenic node: via WGCNA, GSEA, and immune infiltration analysis, we verified known immune pathways and identified understudied lipid metabolism-related genes (AACS, HSD11B1, GATA6) and pathways (fatty acid metabolism, peroxisome function), revealing lipid metabolism\u0026rsquo;s role in linking metabolic disturbances and immune infiltration.\u003c/p\u003e \u003cp\u003eFurthermore, this study systematically integrated the RBP-mRNA network at the post-transcriptional regulatory level for the first time, supplementing the regulatory links that were less explored in previous studies. Compared with existing studies, our results complement the understanding of the interaction between metabolism and immunity. They provide potential therapeutic targets, including AACS, HSD11B1, and GATA6. Additionally, this study offers a new perspective on the molecular mechanisms underlying psoriasis.\u003c/p\u003e \u003cp\u003eIn addition, the cytosolic DNA sensing pathway is significantly enriched, suggesting the involvement of antiviral defense mechanisms. These mechanisms may be aberrantly activated in psoriasis, contributing to the chronic inflammatory state [25]. The identification of these pathways and the differential infiltration of immune cells highlight the complex interplay between the innate and adaptive immune systems in psoriasis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study uses transcriptome data for exploratory analysis but has certain limitations. Firstly, the ssGSEA method has limited resolution when distinguishing immune cell subpopulations with partially overlapping functions, and the results are more suitable as overall trend cues. Secondly, the screening of differentially expressed genes mainly focuses on statistical significance, and ROC analysis results only reflect the discriminatory ability within the current analysis dataset. Therefore, these results should be interpreted with caution. Thirdly, the GO/KEGG pathway sets represent fixed annotations and cannot fully capture the dynamic regulatory processes in diseases. Finally, the type I interferon pathway was enriched in the analysis. This study has adopted methods such as ComBat for strict batch correction and reduced the influence of platform differences by unifying the screening threshold. Due to the lack of external validation with independent datasets, the results of ROC analysis need to be interpreted with caution. Future studies will consider including independent cohorts for further validation. In the future, it will be necessary to further verify the key conclusions in combination with independent external cohorts or experimental methods to enhance the robustness and generalization value of the research. Moreover, there will be a plan to deeply analyze the immune microenvironment of psoriasis in combination with single cell sequencing technology.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur comprehensive bioinformatics analysis identified differentially expressed genes and enriched pathways related to psoriasis. This provides new insights into its molecular mechanisms. By integrating multiple datasets with advanced analytical methods, we revealed potential therapeutic targets, especially in lipid metabolism and immune response pathways. These findings not only enhance 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 have no relevant financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by The President's Fund Project of Nanfang Hospital, Southern Medical University, China (2024B055).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rui Yue. All authors commented on previous versions of the manuscript. All authors read and approved the final 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/](https:/www.ncbi.nlm.nih.gov/geo) ）.\u003c/p\u003e\n\u003ch3\u003eData Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GEO repository, including GSE30999 and GSE54456(\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).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGuo J, Zhang H, Lin W, Lu L, Su J, Chen X. 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Lipid metabolism: a central modulator of RORγt-mediated Th17 cell differentiation. \u003cem\u003eInt Immunol\u003c/em\u003e 2024, \u003cb\u003e36\u003c/b\u003e(10): 487–496.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Psoriasis, Transcriptomics, Lipid Metabolism, Immune Infiltration, RNA-Binding Proteins, Bioinformatic","lastPublishedDoi":"10.21203/rs.3.rs-8999012/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999012/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsoriasis, a chronic and recurring disease, is closely associated with 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 Gene Expression Omnibus (GEO) datasets for DEGs analysis in psoriasis. Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), Weighted Gene Co-expression Network Analysis (WGCNA), and single-sample GSEA (ssGSEA) were used to investigate the roles of lipid metabolism genes in psoriasis progression and immune alterations. An RBP-mRNA network revealed post-transcriptional regulatory mechanisms. Our findings revealed 3,839 DEGs, including 1,775 upregulated and 2,064 downregulated genes in psoriatic samples compared to controls. Enrichment analysis showed that immune-related pathways such as cytoplasmic DNA sensing pathways and NOD-like receptor signaling pathways were significantly enriched. WGCNA identified modules highly correlated with lipid metabolism and screened out key genes such as AACS, HSD11B1 and GATA6. ROC curve analysis showed that these genes had a high discriminatory ability (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.85) within the analyzed dataset, suggesting their potential as novel biomarkers. Immune infiltration analysis revealed significant differences in 27 types of immune cells between patients with psoriasis and the control group. This study provides new clues for the molecular mechanism of psoriasis and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Analysis of Differentially Expressed Lipid Metabolism-Related Genes in Psoriasis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:05:34","doi":"10.21203/rs.3.rs-8999012/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":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T06:57:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:05:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8999012","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8999012","identity":"rs-8999012","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}