Biomarkers For Psoriasis Research Articles

Unveiling the genetic link and pathogenesis between psoriasis and IgA nephropathy based on Mendelian randomization and transcriptome data analyses.

It has been reported that many people with psoriasis have been diagnosed with secondary IgA nephropathy (IgAN). However, the mechanisms behind the association between psoriasis and IgAN have not been well clarified. The connection between psoriasis and IgAN deserves deeper exploration. Mendelian randomization (MR) analysis would be employed to explore the link of causality between IgAN and psoriasis, psoriasis vulgaris, other and unspecified psoriasis, guttate psoriasis, and arthropathic psoriasis. Transcriptomic analyses were carried out against the Gene Expression Omnibus databases. We identified crosstalk genes through the analysis of Differentially expressed genes and weight gene co-expression network analysis. Functional annotations were enriched for these crosstalk genes. Subsequently, we established a protein-protein interaction network, and candidate genes would be discovered through the utilization of the MCODE and CytoHubba plug-in applications. Lastly, the predictive efficacy of these genes was examined via creating receiver operating characteristic curves. The MR analysis suggested that psoriasis vulgaris patients were at a higher risk for IgAN. [OR = 1.040, 95%CI (1.005,1.076), p = 0.026 < 0.05]. Additionally, arthropathic psoriasis may augment the incidence of IgAN [OR = 1.081, 95%CI (1.040-1.124), p < 0.01] in the European population. Through the analysis of DEGs and WGCNA, we identified 12 significant genes (NETO2, RRM2, SLAMF7, GBP1, KIF20A, CCL4, MMP1, IL1β, NDC80, CXCL9, C15orf48, GSTA3), which may be potential crosstalk genes between the two diseases. Then, the functional annotation results indicated that the crosstalk genes seemed primarily involved in immune and inflammatory responses. By establishing the PPI network, we further discovered that CXCL9, IL1β, CCL4, and MMP1 play a vital part in psoriasis and IgAN, and all have good diagnostic values. Our MR analysis provided evidence that genetic vulnerability to IgAN may be associated with an elevated risk of psoriasis vulgaris and arthropathic psoriasis respectively among Europeans. Doctors should be aware of these associations when patients with psoriasis present with renal dysfunction, especially those with psoriasis vulgaris and arthropathic psoriasis. Chronic inflammation, drug effects, and immunity may contribute to the generation and development of both diseases. IL1β, CXCL9, CCL4, and MMP1 may be core biomarkers for psoriasis and IgAN.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Platelet Indices – Are They Really Biomarkers in Psoriasis? A Cross-sectional Study

Background: Psoriasis is a chronic inflammatory disorder with multiple systemic manifestations in common with other inflammatory disorders. Platelet activation has recently been proposed a major pathogenic factor in psoriasis. Mean platelet volume (MPV) and platelet distribution width (PDW) are considered platelet activation markers. Although there are various inflammatory markers reported to be abnormal in psoriasis, a simple cost-effective and clinically useful marker that could predict the severity of the disease and be used to know about its association with systemic abnormalities and response to treatment is still under study. Aim: The aim of our study was to look at the association of platelet indices with psoriasis and its severity. Methods: This was a single-center cross-sectional study in which 50 patients with psoriasis and 50 apparently healthy age and sex-matched controls who fulfilled the inclusion and exclusion criteria were enrolled after obtaining informed consent. A detailed history was elicited, and a clinical examination was done including anthropometric measurements and calculation of psoriasis area and severity index (PASI). Investigations including hemoglobin, red blood cell (RBC) count, packed cell volume (PCV), mean corpuscular volume, mean corpuscular hemoglobin (MCH), MCH concentration, white blood cell (WBC) and platelet count, plateletcrit, MPV, PDW, and red cell distribution width (RDW) were done. Results: Out of 50 psoriasis patients recruited, 40 (80%) had moderate-to-severe psoriasis, whereas 10 had mild psoriasis (10%) based on PASI scores. In majority of cases, the hemoglobin, PCV, RBC, and WBC counts were within the normal range and were comparable with that of controls. There was no statistically significant difference in the mean values of platelet count, MPV, PDW, plateletcrit, and RDW between cases and controls. There was no significant association between platelet indices and severity of psoriasis. Conclusion: There was no significant association between platelet indices including RDW and the presence or absence of psoriasis in our study. Furthermore, among patients with psoriasis, there was no significant association between platelet indices and severity of the disease.

Multi-omic analysis revealed the immunological patterns and diagnostic value of exhausted T cell-derived PTTG1 in patients with psoriasis

BackgroundPsoriasis, characterized by chronic inflammation, is a persistent skin condition that is notoriously challenging to manage and prone to relapse. Despite significant advancements in its treatment, many adverse reactions still occur. Therefore, exploring the mechanisms behind the occurrence and development of psoriasis is extremely important. MethodsThe weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results. ResultsOur results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1. ConclusionsThis study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.

Systematic analysis of serum peptidase inhibitor 3 in psoriasis diagnosis and treatment.

Psoriasis is a chronic inflammatory skin disease. To date, there are no serum biomarkers for psoriasis that have been validated to diagnose or treat psoriasis. Peptidase inhibitor 3 (PI3) levels in serum were measured using chemiluminescence immunoassay (CLIA) in two independent cohorts including healthy controls (HC) and patients diagnosed with chronic urticaria (CU), chronic eczema (CE), systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), psoriatic arthritis (PsA), or psoriasis vulgaris (PV). Receiver operating characteristic (ROC) curve analysis determined the diagnostic performance of PI3 in patients with psoriasis. The correlation between PI3 levels and the Psoriasis Area Severity Index (PASI) score was analyzed using the Spearman correlation method. Additionally, the study evaluated PI3 expression and treatment response of PV patients 12weeks before and after topical treatment with calcipotriol betamethasone and calcipotriol ointment (T#1) or topical therapy plus PSORI-CM01 granules (T#2). In cohort #1, PI3 levels effectively discriminate PV patients from HC and CU patients, with AUCs of 0.909 and 0.840, respectively. In cohort #2, AUCs for detecting PV patients among HC, CU, CE, SLE, and RA patients were 0.940, 0.926, 0.802, 0.989, and 0.951, respectively. For PsA patients, AUCs were 0.989, 0.986, 0.910, 1.000, and 0.984 compared to HC, CU, CE, SLE, and RA patients, respectively. In both cohorts, PI3 levels correlated significantly with PASI scores in PV patients (cohort #1, r = 0.433; cohort #2, r = 0.634) and PsA patients (cohort #2, r = 0.718). Moreover, univariate logistic regression analyses revealed that PV patients with higher PI3 expression had a significantly higher risk of treatment resistance, with an odds ratio of 3.45 [95% confidence interval (CI) 1.54, 7.74, p = 0.003]. Finally, PI3 levels decreased nearly 35-fold more in the responder than in the non-responder group before and after treatment. Serological PI3 is a reliable biomarker for PV diagnosis and may have the potential to predict and monitor the progression of PV before and after treatment. Key Points • This study validated PI3's diagnostic performance in two independent psoriasis cohorts using CLIA. • PI3 expression is significantly correlated with the psoriasis severity and with patients who benefited from the treatments. • Serological PI3 is a reliable biomarker for psoriasis diagnosis and may have the potential to monitor the psoriasis progression with and without treatments.

Investigation and Confirmation of PYCARD as a Potential Biomarker for the Management of Psoriasis Disease.

Psoriasis is not yet completely curable, and its etiology and pathogenesis are unclear. Necroptosis, also known as programmed necrosis, is a regulated mode of necrotic cell death. The interaction between inflammatory diseases and necrotic apoptosis has recently attracted significant attention. We explored the molecular mechanisms of necrotic apoptosis-related genes in psoriasis using bioinformatics methods to identify potential biomarkers for psoriasis. In this study, we screened psoriasis differentially expressed genes from the datasets GSE13355 and GSE14905 and took intersections with necrotic apoptosis-related genes for the next analysis. We used multiple machine learning algorithms to screen key genes and perform enrichment analysis. In addition, we performed an immune infiltration analysis. Transcription factors were predicted by the R package "RcisTarget". We also observed the cellular clustering of key genes in different cell types at the single-cell sequencing level. We used real-time fluorescence-based quantitative-polymerase chain reaction, Western blot, and immunohistochemistry to analyze gene expression in clinical samples. We constructed an imiquimod-induced psoriasis-like dermatitis model in mice for further validation. Seven key genes were screened as follows: AIM2, CARD6, HPSE, MYD88, PYCARD, RAI14, and TNFSF10. Enrichment analysis showed that the key genes were mainly involved in inflammatory pathways. Immune infiltration analysis showed significantly higher levels of CD8 T cells, CD4 initial T cells, and CD4 memory-activated T cells in the disease group's samples than in the normal patients' samples. The key gene expression in single cells analyzed showed that PYCARD was significantly expressed in keratinocytes. PYCARD was selected for gene expression analysis; the results showed that its expression was significantly elevated in the skin lesion tissues of patients with psoriasis. We also verified that PYCARD might play a vital role in the development of psoriasis skin lesions using animal experiments. PYCARD plays a vital role in psoriasis development and is a potential biomarker for psoriasis.

Artificial intelligence in psychodermatology: A brief report of applications and impact in clinical practice.

This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection between mental health and skin disorders, stands to benefit from AI's advanced data analysis and pattern recognition capabilities. A literature search was conducted on PubMed and Google Scholar, spanning from 2004 to 2024, following PRISMA guidelines. Studies included demonstrated AI's effectiveness in predicting treatment outcomes for body dysmorphic disorder, identifying biomarkers in psoriasis and anxiety disorders, and refining therapeutic strategies. The review identified several studies highlighting AI's role in improving treatment outcomes and diagnostic accuracy in psychodermatology. AI was effective in predicting outcomes for body dysmorphic disorder and identifying biomarkers related to psoriasis and anxiety disorders. However, challenges such as limited dermatologist knowledge, integration difficulties, and ethical concerns regarding patient privacy were noted. AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care. Nonetheless, realizing this potential requires large-scale clinical validation, enhanced dataset diversity, and robust ethical frameworks. Future research should focus on these areas, with interdisciplinary collaboration essential for overcoming current challenges and optimizing patient care in psychodermatology.

Metabolic profiling of psoriasis vulgaris and palmoplantar pustulosis.

Psoriasis is a chronic inflammatory skin disorder with various subtypes, including psoriasis vulgaris (PV) and palmoplantar pustulosis (PPP). Metabolomics studies have provided insights into psoriasis pathogenesis. However, research on metabolomic alterations in PV and PPP patients is limited. We aimed to explore and compare the metabolic profiles of patients with PV and PPP to those of healthy volunteers (HVs). A single-centre retrospective cohort was constructed, comprising Korean patients with psoriasis and HVs matched by age and sex. Clinical information including demographics, disease severity, and comorbidities were collected. Plasma samples were subjected to targeted metabolic analysis using an Absolute IDQ®p180 kit, which quantified 188 metabolites, including amino acids and carnitines. Statistical significance was assessed using an independent t-test and chi-square test, with p-values adjusted by the Benjamini-Hochberg procedure. Pathway analyses were employed to gain a comprehensive understanding of the metabolite profile. This study included 93 patients (73 PV and 20 PPP) and an equal number of HVs. PV patients showed increased levels of sarcosine, serotonin, propionylcarnitine, proline, aspartic acid, tyrosine, taurine, spermine and ornithine, but exhibited a decreased level of acetylcarnitine than matched HVs. Notably, sarcosine levels were significantly elevated in PPP patients. Furthermore, the sarcosine/glycine ratio was significantly higher in both PV and PPP patients than in HVs. Pathway analysis showed significant increases in metabolites involved in amino acid metabolism and the urea cycle in PV patients. In conclusion, this study demonstrated distinct metabolic profiles in PV and PPP patients compared to HVs, suggesting sarcosine as a potential biomarker for psoriasis.

Integrative analysis of gene and microRNA expression profiles reveals candidate biomarkers and regulatory networks in psoriasis.

Psoriasis (PS) is a chronic inflammatory skin disease with a long course and tendency to recur, the pathogenesis of which is not fully understood. This article aims to identify the key differentially expressed genes (DEGs) and microRNA (miRNAs) of PS, construct the core miRNA-mRNA regulatory network, and investigate the underlying molecular mechanism through integrated bioinformatics approaches. Two gene expression profile datasets and 2 miRNA expression profile datasets were downloaded from the gene expression omnibus (GEO) database and analyzed by GEO2R. Intersection DEGs and intersection differentially expressed miRNAs (DEMs) were each screened. The Metascape database and R software were used to perform enrichment analysis of intersecting DEGs and study their functions. Target genes of DEMs were predicted from the online database miRNet. The protein-protein interaction files of the overlapping target genes were obtained from string and the miRNA-mRNA network was constructed by Cytoscape software. In addition, the online web tool CIBERSORT was used to analyze the immune infiltration of dataset GSE166388, and the relative abundance of 22 immune cells in the diseased and normal control tissues was calculated and assessed. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the relative expression of the screened miRNAs and mRNAs to assess the applicability of DEMs and DEGs as biomarkers in PS. A total of 205 mating DEGs and 6 mating DEMs were screened. 103 dysregulated crossover genes from 205 crossover DEGs and 7878 miRNA target genes were identified. The miRNA-mRNA regulatory network was constructed and the top 10 elements were obtained from CytoHubba, including hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2. QRT-PCR revealed significant differences in miRNA and gene expression between inflammatory and normal states. In this study, the miRNA-mRNA core regulator pairs hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2 may be involved in the course of PS. This study provides new insights to discover new potential targets and biomarkers to further investigate the molecular mechanism of PS.

Biomarkers in Psoriasis and Psoriatic Arthritis: Where Are We Now?

At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2023 annual conference and trainee symposium, the status of psoriatic disease (PsD) biomarkers was discussed in a workshop. The significant heterogeneity of PsD causes disease management to be very challenging, but biomarkers can prove helpful in disease diagnosis, stratification, and precision medicine. Although a few potential biomarkers have been discovered, none have been fully validated. Recent studies have used omic technologies that show promise but need further verification and validation. Many challenges remain, but the anticipated results of studies being conducted by recently established large consortia may lead to the identification of clinically actionable biomarkers.

UPP1 levels are closely associated with the pathogenesis and progression of psoriasis: a potential biomarker for psoriasis.

UPP1 levels are closely associated with the pathogenesis and progression of psoriasis: a potential biomarker for psoriasis.

Identification of angiogenesis-related genes and molecular subtypes for psoriasis based on random forest algorithm.

Psoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-related genes (ARGs) in psoriasis have not been systematically elucidated. Therefore, we aim to identify potential biomarkers and subtypes using two algorithmsr. Transcriptome sequencing data of patients with psoriasis were obtained, in which differentially expressed genes were assessed by principal component analysis. A diagnostic model was developed using random forest algorithm and validated by receiver operating characteristic (ROC) curves. Subsequently, we performed consensus clustering to calculate angiogenesis-associated molecular subtypes of psoriasis. Additionally, a correlation analysis was conducted between ARGs and immune cell infiltration. Finally, validation of potential ARG genes was performed by quantitative real-time PCR (qRT-PCR). We identified 29 differentially expressed ARGs, including 13 increased and 16 decreased. Ten ARGs, CXCL8, ANG, EGF, HTATIP2, ANGPTL4, TNFSF12, RHOB, PML, FOXO4, and EMCN were subsequently sifted by the diagnostic model based on a random forest algorithm. Analysis of the ROC curve (area under the curve [AUC] = 1.0) indicated high diagnostic performance in internal validation. The correlation analysis suggested that CXCL8 has a high positive correlation with neutrophil (R =0.8, P < 0.0001) and interleukins pathway (R = 0.79, P < 0.0001). Furthermore, two ARG-mediated subtypes were obtained, indicating potential heterogeneity. Finally, the qRT-PCR demonstrated that the mRNA expression levels of CXCL8 and ANGPTL4 were elevated in psoriasis patients, with a reduced expression of EMCN observed. The current paper indicated potential ARG-related biomarkers of psoriasis, including CXCL8, ANGPTL4, and EMCN, with two molecular subtypes.

Single-cell analysis and machine learning identify psoriasis-associated CD8+ T cells serve as biomarker for psoriasis.

Psoriasis is a chronic inflammatory skin disease, the etiology of which has not been fully elucidated, in which CD8+ T cells play an important role in the pathogenesis of psoriasis. However, there is a lack of in-depth studies on the molecular characterization of different CD8+ T cell subtypes and their role in the pathogenesis of psoriasis. This study aims to further expound the pathogenesy of psoriasis at the single-cell level and to explore new ideas for clinical diagnosis and new therapeutic targets. Our study identified a unique subpopulation of CD8+ T cells highly infiltrated in psoriasis lesions. Subsequently, we analyzed the hub genes of the psoriasis-specific CD8+ T cell subpopulation using hdWGCNA and constructed a machine-learning prediction model, which demonstrated good efficacy. The model interpretation showed the influence of each independent variable in the model decision. Finally, we deployed the machine learning model to an online website to facilitate its clinical transformation.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC). The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR. In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models. We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

Biomarkers in Psoriasis: The Future of Personalised Treatment.

Psoriasis is a chronic and complex immune-mediated papulosquamous disease affecting almost 2% of the world population. The interaction between a genetically predisposed individual and environmental triggers leads to a vicious cycle involving autoreactive T cells, dendritic cells, keratinocytes and dermal cells. Up to 40% of the psoriasis cases develop disabling psoriatic arthritis and an equal number of patients also tend to develop metabolic syndrome as well as cardiovascular comorbidities; hence, this is no more considered to be a disease limited to skin only. Being a systemic disease, there is an urgent need to develop potential biomarkers for the assessment of disease severity, prediction of outcome of the therapeutic intervention and association with various systemic comorbidities. Diverse genetic markers not only function as predictors of diseases pathogenesis, but also help to predict development of psoriasis and psoriatic arthritis. Personalised medicine is customising the therapeutic needs of a psoriasis patient and improving the outcome as per the hints we receive from the various biomarkers. This review deals with the list of potential biomarkers proposed to be useful in psoriasis, though there is limited data validating their routine use in clinical practice and the progress so far made in the field of precision medicine for psoriasis.

Identifying key inflammatory genes in psoriasis via weighted gene co-expression network analysis: Potential targets for therapy.

Psoriasis is a globally prevalent chronic inflammatory skin disease. This study aimed to scrutinize the hub genes related to inflammation and potential molecular mechanisms in psoriasis. Utilizing mRNA expression profiles from public datasets GSE13355, GSE78097, and GSE14905, we set up a comprehensive analysis. Initially, we selected differentially expressed genes (DEGs) from psoriasis and control samples in GSE13355, followed by calculating inflammatory indices using genomic set variation analysis (GSVA). Weighted gene co-expression network analysis (WGCNA) was then applied to link significant modules with the inflammatory index. This process helped us identify differentially expressed inflammation-related genes (DE-IRGs). A protein-protein interaction (PPI) network was established, with the molecular complex detection (MCODE) plug-in pinpointing six chemokine genes (CCR7, CCL2, CCL19, CXCL8, CXCL1, and CXCL2) as central hub genes. These genes demonstrated pronounced immunohistochemical staining in psoriatic tissues compared to normal skin. Notably, the CCR7 gene exhibited the highest potential for m6A modification sites. Furthermore, we constructed transcription factor-microRNA-mRNA networks, identifying 139 microRNAs and 52 transcription factors associated with the hub genes. For the LASSO logistic regression model, the area under the curve (AUC) in the training set was 1, and in the two validation cohorts GSE78097 and GSE14905 were 1 and 0.872, respectively. In conclusion, our study highlights six chemokine genes (CCR7, CCL2, CCL19, CXCL8, CXCL1, and CXCL2) as potential biomarkers in psoriasis, providing insights into the immune and inflammatory responses as pivotal instances in disease pathogenesis. These findings pave the way for exploring new therapeutic targets, particularly focusing on chemokine-associated pathways in psoriasis treatment.

Single-cell sequencing analysis and multiple machine-learning models revealed the cellular crosstalk of dendritic cells and identified FABP5 and KLRB1 as novel biomarkers for psoriasis.

Psoriasis is an immune-mediated disorder influenced by environmental factors on a genetic basis. Despite advancements, challenges persist, including the diminishing efficacy of biologics and small-molecule targeted agents, alongside managing recurrence and psoriasis-related comorbidities. Unraveling the underlying pathogenesis and identifying valuable biomarkers remain pivotal for diagnosing and treating psoriasis. We employed a series of bioinformatics (including single-cell sequencing data analysis and machine learning techniques) and statistical methods to integrate and analyze multi-level data. We observed the cellular changes in psoriatic skin tissues, screened the key genes Fatty acid binding protein 5 (FABP5) and The killer cell lectin-like receptor B1 (KLRB1), evaluated the efficacy of six widely prescribed drugs on psoriasis treatment in modulating the dendritic cell-associated pathway, and assessed their overall efficacy. Finally, RT-qPCR, immunohistochemistry, and immunofluorescence assays were used to validate. The regulatory influence of dendritic cells (DCs) on T cells through the CD70/CD27 signaling pathway may emerge as a significant facet of the inflammatory response in psoriasis. Notably, FABP5 and KLRB1 exhibited up-regulation and co-localization in psoriatic skin tissues and M5-induced HaCaT cells, serving as potential biomarkers influencing psoriasis development. Our study analyzed the impact of DC-T cell crosstalk in psoriasis, elucidated the characterization of two biomarkers, FABP5 and KLRB1, in psoriasis, and highlighted the promise and value of tofacitinib in psoriasis therapy targeting DCs.

A Snapshot of Biomarkers in Psoriasis.

A persistent long-standing, inflammatory skin condition that is brought on by a variety of factors is psoriasis. It is distinguished by itchy, scaly, reddish plaques, particularly on areas of the body that are frequently chafed, including the extensor sites of the limbs. Recent developments in molecular-targeted therapy that use biologics or small-molecule inhibitors can effectively cure even the worst psoriatic indications. The outstanding clinical outcomes of treatment help to clarify the disease's detrimental consequences on quality of life. Biomarkers that identify deep remission are essential for developing uniform treatment plans. Blood protein markers such as AMPs that are consistently quantifiable can be very helpful in routine clinical practice. The metabolic pathways involve biomarkers that can not only help diagnose psoriasis in a clinical setting but also indicate its severity based on the levels present in the body. Machine learning and AI have made a diagnosis of the expression of genes as biomarkers more accessible. In this article, biomarkers, as well as their key role in psoriasis, are discussed.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the “limma” package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein–protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Rutaecarpine ameliorates imiquimod-induced psoriasis-like dermatitis in mice associated with alterations in the gut microbiota.

Psoriasis is accepted as a chronic, inflammatory, immune-mediated skin disease triggered by complex environmental and genetic factors. For a long time, disease recurrence, drug rejection, and high treatment costs have remained enormous challenges and burdens to patients and clinicians. Natural products with effective immunomodulatory and anti-inflammatory activities from medicinal plants have the potential to combat psoriasis and complications. Herein, an imiquimod (IMQ)-induced psoriasis-like dermatitis model is established in mice. The model mice are treated with 1% rutaecarpine (RUT) (external use) or the oral administration of RUT at different concentrations. Furthermore, high-throughput 16S rRNA gene sequencing is applied to analyze the changes in the diversity and composition of the gut microbiota. Based on the observation of mouse dorsal skin changes, RUT can protect against inflammation to improve psoriasis-like skin damage in mice. Additionally, RUT could suppress the expression levels of proinflammatory cytokines (IL-23, IL-17A, IL-22, IL-6, and IFN-α) within skin tissue samples. Concerning gut microbiota, we find obvious variations within the composition of gut microflora between IMQ-induced psoriasis mice and RUT-treated psoriasis mice. RUT effectively mediates the recovery of gut microbiota in mice induced by IMQ application. Psoriasis is linked to the production of several inflammatory cytokines and gut microbiome alterations. This research shows that RUT might restore gut microbiota homeostasis, reduce inflammatory cytokine production, and ameliorate psoriasis symptoms. In conclusion, the gut microbiota might be a therapeutic target or biomarker for psoriasis that aids in clinical diagnosis and therapy.