Bioinformatics identification and experimental validation of biomarkers associated with T cells in psoriasis

This study conducted a comprehensive analysis to recognize T cell-associated biomarkers in psoriasis and to elucidate their underlying molecular mechanisms. Biomarkers were selected and validated using machine learning algorithms and expression levels, followed by assessment of diagnostic value through receiver operating characteristic (ROC) analysis. In addition, a nomogram for PSO was constructed, and enrichment analysis analyzed relevant gene-enriched pathways, while immune cell infiltration analysis compared differential immune cell profiles between PSO and normal samples, highlighting correlations between biomarkers and immune cells. Finally, reverse transcription quantitative PCR (RT-qPCR) was employed to verify expression levels of the biomarkers.Among these, 3 biomarkers (AKR1B10, C10orf99, and CKS2) demonstrated high sensitivity and specificity in ROC curve, and the reliability of the developed nomogram diagnostic model was observed. Importantly, RT-qPCR confirmed higher expression of AKR1B10, C10orf99, and CKS2 in PSO patients.