Darvish Zeinab, Ghanbari Saeed, Afshar Saeid, Tapak Leili, Amini Payam
Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Email:
Cell J. 2023 Jun 28;25(6):418-426. doi: 10.22074/cellj.2023.1982769.1191.
Psoriasis, an immune-mediated disorder, is a multifactorial disease with unidentified cause(s). This study aimed to discover possible biomarkers of this papulosquamous skin disease.
The gene chip GSE55201, resulted from an experimental study, including 44 Psoriasis patients and 30 healthy controls was downloaded from GEO and weighted gene co-expression network analysis was utilized to identify hub genes. Key modules were determined using the module eigenvalues. We used biological functions (BFs), cellular components, and molecular functions in the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis in the gene metabolic pathway were used for enrichment analysis.
Adjacency matrix was built by using power adjacency function and the power to turn the correlation to adjacency matrix was four with a topology fit index of 0.92. Using the weighted gene co-expression network analysis, 11 modules were identified. The green-yellow module eigenvalues were significantly associated with Psoriasis (Pearson correlation=0.53, P<0.001). Candidate hub genes were determined by their higher connectivity and relationship with module eigenvalue. The genes including and were recorded as the hub genes.
We can conclude that and have an important role in the immune response regulation and they could be considered as a potential diagnostic biomarker and therapeutic target for Psoriasis.
银屑病是一种免疫介导的疾病，是病因不明的多因素疾病。本研究旨在发现这种丘疹鳞屑性皮肤病可能的生物标志物。
从基因表达综合数据库（GEO）下载了一项实验研究产生的基因芯片GSE55201，该研究包括44例银屑病患者和30例健康对照，采用加权基因共表达网络分析来识别枢纽基因。使用模块特征值确定关键模块。我们在基因本体（GO）分析中使用生物学功能（BFs）、细胞成分和分子功能，并在基因代谢途径中使用京都基因与基因组百科全书富集分析进行富集分析。
通过使用幂邻接函数构建邻接矩阵，将相关性转换为邻接矩阵的幂为4，拓扑拟合指数为0.92。使用加权基因共表达网络分析，识别出11个模块。绿黄色模块特征值与银屑病显著相关（皮尔逊相关系数=0.53，P<0.001）。通过较高的连通性及其与模块特征值的关系确定候选枢纽基因。包括[具体基因1]和[具体基因2]在内的基因被记录为枢纽基因。
我们可以得出结论，[具体基因1]和[具体基因2]在免疫反应调节中起重要作用，它们可被视为银屑病潜在的诊断生物标志物和治疗靶点。
