Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Rice Research Institute, Guangdong Academy of Agricultural Sciences / Guangdong Key Laboratory of New Technology in Rice Breeding / Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China.
Skin Res Technol. 2024 Aug;30(8):e13889. doi: 10.1111/srt.13889.
Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention.
Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI).
A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores.
High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.
银屑病是一种免疫介导的皮肤疾病，与免疫调节密切相关。本研究旨在进一步了解银屑病的发病机制，揭示潜在的治疗靶点，为其诊断、治疗和预防提供新的线索。
从基因表达综合数据库（GEO）中获取健康人群和银屑病患者皮肤组织的表达谱数据。分别对差异表达基因（DEGs）进行基因本体论（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）分析。使用机器学习算法获得与银屑病密切相关的特征基因。使用受试者工作特征（ROC）曲线评估特征基因对银屑病的诊断价值。使用细胞类型鉴定估计相对转录本子集（CIBERSORT）算法计算免疫细胞浸润的比例。相关性分析用于描述基因表达与免疫细胞、银屑病面积和严重程度指数（PASI）之间的关系。
在银屑病组中鉴定出 254 个 DEGs，包括 185 个上调基因和 69 个下调基因。GO 主要富集在细胞因子介导的信号通路、病毒反应和细胞因子活性。KEGG 主要集中在细胞因子-细胞因子受体相互作用和 IL-17 信号通路。GSEA 主要在趋化因子信号通路和细胞因子-细胞因子受体相互作用。机器学习算法筛选出九个特征基因 C10orf99、GDA、FCHSD1、C12orf56、S100A7、INA、CHRNA9、IFI44 和 CXCL9。在验证集中，这九个基因在银屑病组中的表达增加，AUC 值均>0.9，与训练集一致。免疫浸润结果显示，银屑病组中巨噬细胞、T 细胞和中性粒细胞的比例增加。特征基因与 T 细胞和巨噬细胞呈不同程度的正相关或负相关。这九个特征基因在中重度银屑病组中高表达，并与 PASI 评分呈正相关。
九个特征基因 C10orf99、GDA、FCHSD1、C12orf56、S100A7、INA、CHRNA9、IFI44 和 CXCL9 的高水平表达是银屑病的危险因素，其差异表达与免疫系统活性和 PASI 评分的调节有关，影响不同免疫细胞的比例，促进银屑病的发生和发展。
