Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.

backgroundPsoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors. Genetic predisposition plays a pivotal role, with the IL-17/IL-23 immune axis recognized as a central pathogenic pathway. Ongoing research, however, continues to uncover additional critical drivers, cytokines, intracellular signaling networks, and potential therapeutic targets.

methodsSingle-cell RNA sequencing (scRNA-seq) datasets comprising both psoriatic and healthy samples were obtained from the Gene Expression Omnibus (GEO). Cell-type proportions were estimated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and weighted gene co-expression network analysis (WGCNA) was applied to explore correlations between cell types and gene signatures. Machine learning algorithms were subsequently employed to identify four psoriasis-associated key genes: DEFB4A, GJB2, SERPINB3, and SERPINB13. Their expression was validated in bulk RNA-seq datasets. Using scRNA-seq data, we further investigated the lesional regulatory roles of these genes and their associated pathway alterations, and we proposed targeted therapeutic strategies.

resultsA series of algorithms identified 271 hub genes significantly associated with psoriasis lesions and basal cells. Machine learning analysis refined this set to four key genes in psoriasis: DEFB4A, GJB2, SERPINB3, and SERPINB13.

conclusionsThese four psoriasis-associated driver genes were upregulated in lesional skin. We also screened small-molecule compounds targeting these genes, offering potential therapeutic strategies.