Abstract
The NLRP3 inflammasome is closely associated with inflammatory diseases, including psoriasis. Objective diagnostic biomarkers and alternative therapies for psoriasis remain limited. We aimed to identify reliable biomarkers for the diagnosis of psoriasis and investigate potential therapy strategies. Machine learning methods were performed in over 1000 skin samples from public transcriptome database to identify NLRP3 inflammasome-related biomarkers. Multivariate Cox regression analysis was used to establish the biomarker-based diagnostic model. TNF-induced HaCaT cell model was used to evaluate biomarker-related inflammatory changes. Biomarker-targeting drugs was predicted with NetworkAnalyst database and validated in imiquimod (IMQ)-induced mouse model. Elevated level of four NLRP3 inflammasome-related biomarkers, including NLRP3, ASC, TXNIP and CASP-1, were identified from the public psoriasis transcriptome samples and validated in our local psoriasis skin biopsies. The biomarker-based diagnostic model was developed from training dataset and validation dataset, which both showed significant diagnostic value for psoriasis. Knocking down one of these genes in vitro showed reduced inflammatory factors, reduced cell apoptosis and improved cell viability. Furthermore, Predictive biomarker-targeting therapeutics, including resveratrol and JQ-1, demonstrated effective alleviation of psoriasis severity and reduced inflammation in IMQ-induced psoriasis mice. Combinational evaluation of NLRP3, ASC, TXNIP and CASP-1 may constitute a novel diagnostic approach for psoriasis. Targeting these proteins provide more options for psoriasis therapy.