Abstract：
The pharmacological mechanism of Phellodendri Chinensis cortex (PCC) against diseases, especially bladder cancer (BC), has never been reported systematically. This study was designed to explore potential mechanism of PCC in treatment of BC. First, we used network pharmacology to discover the potential mechanism of Phellodendri Chinensis cortex and phellodendrine against bladder cancer. Then, we used bioinformatics analysis to verify the correlation between gene expression analysis, survival analysis and common targets. Finally, molecular docking was used to calculate the binding energies of phellodendrine and common targets.A total of 264 targets for PCC were predicted, and 391 BC-related targets were obtained from 4 databases. There were 54 potential targets, 315 biological processes, and 120 signaling pathways involved for PCC against BC. The CDKN2A expression increased and the ESR1, JUN, IL6, AR, and PTGS2 levels decreased in BC according to Gene Expression Profiling Interactive Analysis version 2. The high expression of JUN, MYC, EGFR, and EGF and low expression of VEGFA and PPARG were associated with short overall survival (OS). The high expression of AKT1, EGFR, and EGF and low expression of IL1β were associated with poor disease-free survival (DFS). The search of the intersection of phellodendrine and BC targets yielded 11 common targets, 50 biological processes, and 13 signaling pathways involved. High AURKA and FASN and low ESR1, JUN, ABCB1, and PTGS1 were expressed in BC. The high expression of FASN, ABCC1, PTGS1, JUN, and PIK3CA was associated with short OS, the high expression of PIK3CA and ABCC1 was associated with poor DFS prognosis. Phellodendrine showed a better binding affinity for PTGS2 protein with a docking score of -7.183 and a MM-GBSA result of -46.47 kcal/mol. This study revealed potential mechanism of PCC and phellodendrine against BC through network pharmacology and bioinformatics.
摘要：
黄柏(PCC)抗病的药理机制,尤其是膀胱癌（BC），从未系统地报道过。本研究旨在探讨PCC治疗BC的潜在机制。首先,运用网络药理学研究黄柏和黄柏碱抗膀胱癌的潜在作用机制。然后，我们使用生物信息学分析来验证基因表达分析之间的相关性,生存分析和共同目标。最后,分子对接用于计算黄柏碱与常见靶标的结合能。共预测了PCC的264个目标，从4个数据库中获得391个BC相关目标。有54个潜在目标，315个生物过程，和120条信号通路涉及PCC抗BC。CDKN2A表达增加，ESR1，JUN，IL6、AR、根据基因表达谱交互式分析版本2，BC中的PTGS2水平降低。JUN的高表达，MYC,EGFR,EGF和VEGFA和PPARG的低表达与短总生存期（OS）相关。AKT1、EGFR、EGF和IL1β的低表达与低无病生存率（DFS）相关。对黄柏和BC目标交叉点的搜索产生了11个共同目标，50个生物过程，和13个信号通路参与。高AURKA和FASN和低ESR1，JUN，ABCB1和PTGS1在BC中表达。FASN的高表达,ABCC1,PTGS1,JUN,PIK3CA与短操作系统有关，PIK3CA和ABCC1的高表达与不良DFS预后相关.黄柏碱对PTGS2蛋白显示出更好的结合亲和力，对接评分为-7.183，MM-GBSA结果为-46.47kcal/mol。本研究通过网络药理学和生物信息学揭示了PCC和黄柏碱抗BC的潜在机制。
