Abstract
PURPOSE: In present, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy and skin biopsy, and unlike other immune diseases, psoriasis lacks specific indicators in the blood. Therefore, we are required to search novel biomarkers for the diagnosis of psoriasis. METHODS: In this study, we analyzed the composition and the differences of intestinal fungal communities composition between psoriasis patients and healthy individuals in order to find the intestinal fungal communities associated with the diagnosis of psoriasis. We built a machine learning model and identified potential microbial markers for the diagnosis of psoriasis. RESULTS: The results of AUROC (area under ROC) showed that Aspergillus puulaauensis (AUROC = 0.779), Kazachstania africana (AUROC = 0.750) and Torulaspora delbrueckii (AUROC = 0.745) had high predictive ability (AUROC > 0.7) for predicting psoriasis, While Fusarium keratoplasticum (AUROC = 0.670) was relatively lower (AUROC < 0.7). CONCLUSION: The strategy based on the prediction of intestinal fungal communities provides a new idea for the diagnosis of psoriasis and is expected to become an auxiliary diagnostic method for psoriasis.