Integrative Transcriptomic and Genetic Analyses Identify CDC20 as a Potential Biomarker Associated with Cell Cycle...

To identify psoriasis-associated candidate genes and regulatory features via integrated transcriptomic, immune and genetic analyses, and validate them in an imiquimod-induced psoriasis-like murine model.

Public GEO transcriptomic datasets were integrated after batch-effect correction. We performed differential expression, WGCNA, machine-learning-based feature selection and diagnostic modelling, CIBERSORT immune infiltration estimation, transcription-factor regulation, drug enrichment, colocalization and SMR analyses. In-vivo histological and Western blot validation was conducted in mice.

We obtained 767 candidate genes enriched in cell-cycle and immune-inflammatory pathways. The Lasso-XGBoost model (AUC = 0.923) yielded 11 candidate genes that well separate psoriasis patients from healthy controls. Drug enrichment identified Lucanthone. SMR revealed consistent expression-genetic directionality for LYN and IL1RN. Notably, the 11 machine-learning-derived signature genes mainly mark psoriasis disease activity and differ from the SMR-identified genetically supported causal genes LYN and IL1RN. Increased CDC20, CCNB1 and CDK1 protein levels were confirmed in mouse lesional skin.

This study suggests that CDC20 may serve as a potential biomarker for cell cycle and immune-inflammatory processes in psoriasis, providing a basis for further mechanistic and translational studies.