Single-Cell RNA Sequencing Revealing Dysregulated Perturbations of Tregs in Psoriasis and Construction of a Treg-Related Diagnostic Model via a 101- Combination Machine Learning Computational Framework

This study provides a comprehensive analysis of the cellular heterogeneity of the epidermal immune microenvironment in psoriasis through single-cell transcriptomics, offering valuable insights into metabolic reprogramming, developmental pathways, cell-cell interactions, and the functional properties of Tregs in psoriasis. Additionally, a high-accuracy diagnostic model for psoriasis was developed using machine learning techniques. These findings offer a single-cell molecular perspective on immune microenvironment regulation in psoriasis and contribute to the identification of diagnostic biomarkers as well as the refinement of clinical diagnostic strategies.