Background: 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.Methods: 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.Results: 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.Conclusion: 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.

Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by itching, redness, and dryness, significantly impacting the quality of life of affected individuals. With a rising prevalence across diverse demographics, understanding AD is crucial due to its systemic nature and association with comorbidities such as asthma and allergic rhinitis, as well as its psychosocial implications. The pathophysiology of AD involves a complex interplay of genetic predispositions and environmental triggers, leading to dysbiosis and increased susceptibility to superinfection. Clinically, AD manifests variably across age groups, with distinct presentations in pediatric and adult populations. Diagnosis is primarily based on clinical assessment criteria, supplemented by differential diagnoses and, when necessary, skin tests for allergies. Current management strategies encompass topical therapies, including moisturizers, corticosteroids, and calcineurin inhibitors, alongside systemic treatments such as antihistamines, immunosuppressants, and biologics. Lifestyle modifications, including trigger avoidance and effective skin care routines, are essential components of comprehensive care. Emerging novel therapies targeting specific biomarkers are currently under investigation in clinical trials, offering promising avenues for more effective management. However, challenges remain in optimizing treatment protocols and addressing the multifaceted nature of AD. In conclusion, this review highlights the need for continued research and awareness regarding atopic dermatitis. A multidisciplinary approach to management is essential to enhance patient outcomes and address the complexities of this prevalent and impactful condition.

Background: Psoriasis, a chronic inflammatory disorder with an unknown cause, significantly impacts the physical and psychological well-being of patients. However, current biomarkers related to psoriasis lack clinical specificity, sensitivity, and predictive ability. Methods: In this study, we collected skin lesion tissues from 20 psoriasis patients and 20 normal skin samples. Additionally, we obtained four datasets from the GEO database, which included human psoriasis and healthy specimens. We utilized SVM-RFE analysis and the LASSO regression model to identify potential biomarkers. Furthermore, we examined the composition of immune cell types in psoriasis and their correlation with specific genes. Results: Our investigation revealed 57 differentially expressed genes (DEGs), and we identified significantly enriched pathways through KEGG pathway analysis. The results of machine learning and WGCNA suggested that LCE3D and SPRR1B could potentially be used as marker genes for diagnosing psoriasis. RT-PCR and immunohistochemical detection confirmed the abnormally high expression of the SPRR1B gene in psoriasis. Analysis of immune cell infiltration revealed a strong positive correlation between SPRR1B and Macrophages M0 and T cells follicular helper, while showing the strongest negative correlation with resting Mast cells. In addition, we found that silencing SPRR1B in IFN-γ-treated HaCat cells could significantly reduce the increase in IL-17, IL-22, KRT6, and KRT16 caused by IFN-γ. Conclusion: These findings suggest that SPRR1B may have a significant role in the pathogenesis of psoriasis and could be employed as a novel immunomarker for its development.

Melanoma is an aggressive skin cancer that has gained attention worldwide. Growing evidence has highlighted that the tumor microenvironment (TME) is an important feature of carcinogenesis and contributes to therapeutic efficacy in melanoma. However, additional advances in melanoma immuno-oncology are necessary to achieve a comprehensive knowledge of the immune infiltrate population and to identify accurate and readily measurable biomarkers. In this study, we analyzed gene expression of 468 melanoma cases from the TCGA database, which led to the identification of three melanoma clusters (representedby low, median and high infiltration) that display unique immune features. We found that the microenvironment clusters had substantial prognostic efficacy. The median cluster was characterized by an inability to draw immune cells, highlighting possible immune escape mechanisms, and lower CXCL9 and CXCL10 expression, which was correlated to poor prognosis. Deep molecular characterization of immune cells, cytolytic-activity and tumor-inflammatory status revealed diversity of the local immune infiltration landscape in the melanoma clusters. Differentially expressed genes related to TME were extracted from each infiltration cluster. Functional annotations revealed that these genes were mainly related to immune system activation and the processes of immunoreaction. The top ten hub genes in immune infiltration-related protein-protein interaction (PPI) networks were selected for further prognostic investigation. Further validation showed that five of ten hub genes were good prognostic biomarkers for melanoma in two independent groups from the Gene Expression Omnibus database. In brief, these data highlight that systemic characterization of melanoma could uncover tumor infiltrate characteristics, which can help select the most adequate treatment and identify consistent and important indicators of the local immune tumor microenvironment in melanoma patients.

Background: We already know that abnormal immune cell infiltration is related to the pathogenesis of psoriasis (PSO), but the role of immune cell infiltration in the pathogenesis and treatment of psoriasis is unknown.The purpose of this study was to determine the diagnostic and predictive value of immune-related genes in psoriasis.Methods: Six gene expression datasets were downloaded from GEO database to analyze gene expression in psoriasis tissues. Single-sample gene set enrichment analysis (ssGSEA) was used to assess immune cell infiltration in psoriatic tissue. Immune-related genes were screened by overlapping immune-related genes with differentially expressed genes (DEGs). Protein interaction (PPI) networks are used to identify key DEGs. We analyzed the diagnostic value of key genes in psoriasis and the predictive value of key genes in three types of biological therapy. Immunohistochemistry (IHC) was used to detect gene expression in psoriasis tissues.Results: There were significant differences in immune cell infiltration between psoriatic tissue and normal skin tissue. PRC1, GATA3, IL1RN and CCL20 are the key genes of four subclusters in PPI network. PRC1, GATA3, IL1RN and CCL20 have diagnostic value in psoriasis, among which PRC1 and CCL20 have better diagnostic value than GATA3 and IL1RN. After treatment with effective biologic agents, the expression of PRC1, GATA3, IL1RN and CCL20 decreased in skin tissues of patients. GATA3 and IL1RN did not change significantly in patients with non-response to Ustekinumab, and the expression of GATA3 did not change significantly in patients with non-response to Etanercept, that showed good predictive value. Immunohistochemical analysis confirmed that the expression of PRC1, GATA3, IL1RN and CCL20 in PSO tissue was significantly different from that in normal skin tissue, and immune-related genes PRC1, GATA3, IL1RN and CCL20 could be used as auxiliary diagnostic indicators of psoriasis. GATA3 and IL1RN may serve as promising predictors of tumor necrosis factor inhibitors and IL-12/23 inhibitors therapy in patients.Conclusion: This study reveals the significance of immune genes in the pathogenesis and treatment of psoriasis. These key genes may provide new ideas for future treatments.