Network Pharmacology and Animal Experimental Validation on the Therapeutic Mechanisms of Prunus cerasifera Extract in Psoriasis

Huiqin Wang,1 Yuan Ding,1 Zhenrui Wang,1 Yiqi Wang,1 Yanwen Liu,2 Yaqin Ma,1 Weidong Wu1 1Department of Dermatology and Venereology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Research Center for Dermatology and Venereology, Xinjiang Key Laboratory of Dermatology Research, Urumqi, Xinjiang Uygur Autonomous Region, 830000, People’s Republic of China; 2Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, 830000, People’s Republic of ChinaCorrespondence: Weidong Wu, Department of Dermatology and Venereology, People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Research Center for Dermatology and Venereology, Xinjiang Key Laboratory of Dermatology Research, No. 91, Tianchi Road, Tianshan District, Urumqi, Xinjiang Uygur Autonomous Region, 830000, People’s Republic of China, Email xiaobenshu2003@sina.comBackground: Psoriasis is a chronic immune-mediated inflammatory skin disorder characterized by excessive keratinocyte proliferation and persistent inflammation. Its multifactorial pathogenesis involves complex inflammatory mediators and signaling pathways. Current treatments remain limited by high recurrence and adverse effects, highlighting the need for safe and effective natural bioactive compounds for psoriasis prevention and management. In addition, cyclin B1 (CCNB1) has been reported as a hub gene associated with psoriasis; however, its mechanism of action remains unclear.Objective: To elucidate the potential therapeutic mechanisms of a bioactive extract derived from Prunus cerasifera fruit powder (PCE10) by integrating network pharmacology and animal experimental validation.Methods: A psoriasis-like mouse model was established using imiquimod (IMQ) in mice as the study subjects. The anti-psoriatic effect of PCE10 was assessed using the Psoriasis Area and Severity Index (PASI), histopathological examination, and inflammatory cytokine assays. Network pharmacology combined with machine learning algorithms was employed to identify psoriasis-associated target genes potentially regulated by PCE10. Molecular docking were subsequently performed to predict the binding interactions between candidate bioactive compounds and key molecular targets.Results: Network pharmacology identified 67 psoriasis-related targets, with 23 hub genes detected in the protein-protein interaction network. Enrichment analyses indicated involvement in inflammatory regulation and TNF, NF-κB, and p53 pathways. Machine learning identified CCNB1 as a key psoriasis-associated gene. Molecular docking indicated interactions between p53 and the bioactive compounds of PCE10. In vivo, PCE10 alleviated IMQ-induced psoriasis-like dermatitis and reduced epidermal hyperplasia, decreasing serum levels of IL-23, IL-17A, and TNF-α. Mechanistically, PCE10 suppressed keratinocyte proliferation by modulating the p53/CCNB1 axis.Conclusion: PCE10 may exert anti-psoriatic effects through modulation of p53/CCNB1 regulatory axis. Nevertheless, this study is mainly based on an IMQ-induced mouse model and is not supported by validation in human psoriatic skin tissues or clinical specimens. Additional translational studies are required to establish its clinical applicability.Keywords: psoriasis, imiquimod, Prunus cerasifera, network pharmacology, machine learning