Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: .

BACKGROUND

Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research.

METHODS

We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 ( and other immune cells. Finally, drug docking was performed on , and the expression levels of in psoriasis were verified through clinical and in vivo experiments, and further in vivo experiments established the efficacy of tetrandrine in the treatment of psoriasis.

RESULTS

T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. emerged as a characteristic gene for psoriasis and was found to be downregulated in psoriasis and potentially negatively associated with T cells, influencing psoriasis fatty acid metabolism, as inferred from enrichment and immunoinfiltration analyses. In the cellular communication network, cells expressing exhibited close interactions with other immune cells through multiple signaling channels. Furthermore, drug sensitivity showed that tetrandrine stably binds to , could be a potential therapeutic agent for psoriasis.

CONCLUSION

emerges as a specific candidate gene potentially regulating the fatty acid metabolism of patients with psoriasis. In addition, tetrandrine shows promise as a potential treatment for the condition.