Abstract
OBJECTIVE: This study aimed to analyze gene expression data from psoriasis and control samples, focusing on identifying exosome and cell senescence genes, integrating datasets, and validating batch effect removal using principal component analysis (PCA). METHODS: We analyzed gene expression profiles from Gene Expression Omnibus (GEO) to identify significant differences between healthy and diseased tissues. It evaluated immune cell proportion variations and used weighted gene co-expression network analysis (WGCNA) to find key modules. Protein-protein interaction (PPI) networks were constructed to explore gene interactions, followed by enrichment analysis for biological functions and pathways. To validate findings, feature genes were confirmed using additional GEO datasets and real-time fluorescence quantitative PCR (RT-qPCR). RESULTS: This study integrated GSE30999 and GSE13355 datasets, identifying 274 exosome-related and cell senescence genes. After standardizing and normalizing the data, PCA confirmed effective batch effect removal. Differentially expressed genes (DEGs) were analyzed for immune-related functions, and PPI networks were constructed. The results, visualized with heatmaps, revealed significant differences in the expression of exosome-related DEGs between psoriasis and control samples. These findings provide insights into potential novel targets for psoriasis therapy. CONCLUSIONS: Sixteen exosome-related differentially expressed genes (ERDEGs), including CD274 and SERPINB3, are likely to play a significant role in the development of psoriasis.