Comprehensive Summary
This article explores how transcriptional dysregulation of skin barrier genes contributes to the pathogenesis of atopic dermatitis and psoriasis, while also highlighting the role of artificial intelligence in advancing targeted therapies. Conducted as a retrospective, multi-center review, the study analyzes clinical trial data and molecular datasets spanning over a decade. It details how cytokine signaling – particularly Th2 in AD and Th17 in psoriasis – suppresses the expression of key skin barrier genes such as FLG, LOR, IVL, and CLDN1, leading to increased transepidermal water loss and skin inflammation. The findings confirm reduced expression of these barrier genes in diseased skin and show that both natural compounds (e.g., tapinarof) and therapeutic agents (e.g., dupilumab) can restore gene expression and protein expression. Furthermore, AI-assisted methods are shown to enhance dermatological precision by enabling patient stratification based on barrier gene profiles. Using machine learning, the study identifies three keratinocyte programs – KC1, KC2, and KC17 – that predict treatment responses.
Outcomes and Implications
The difficulties of translating molecular insights into clinical outcomes and handling variability in gene expressions exist, limiting the advancement of models. However, with the use of AI-assisted approaches, precision dermatology will begin to integrate more machine learning models and single-cell transcriptomics. The cooperation of gene-targeted strategies and digital biomarker discovery expands the clinical applicability and benefits in the real world. Moreover, the integration of AI-assisted methods leads to the possibility of use in predicting treatment outcomes and stratifying AD subtypes, resulting in a holistic therapeutic model that combines immune regulation, technological advancement, and patient quality of life.