文献类型：期刊文献

英文题名：Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods

作者：Wang, Jiye[1];Luo, Lin[2];Ding, Qiong[2];Wu, Zengrui[1];Peng, Yayuan[1];Li, Jie[1];Wang, Xiaoqin[2,3];Li, Weihua[1];Liu, Guixia[1];Zhang, Bo[2,3];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai, Peoples R China;[2]Shihezi Univ, Sch Pharm, Key Lab Xinjiang Phytomed Resources, Minist Educ, Shihezi, Peoples R China;[3]Chengdu Univ, Sch Pharm, Sichuan Ind Inst Antibiot,Sichuan Educ Dept, Key Lab Med & Edible Plants Resources Dev, Chengdu, Peoples R China

年份：2021

卷号：12

外文期刊名：FRONTIERS IN PHARMACOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000701284300001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2019YFA0904800), the National Natural Science Foundation of China (Grants 81872800 and 82173746), the 2nd round funds for talents of Xinjiang Production and Construction Corps, the key technology program (Grant 2020AA005), the Young and middle-aged Leading talents funds (Grant 2018CB019).

语种：英文

外文关键词：kaempferide; machine learning; melanogenesis; multi-target strategy; network analysis; vitiligo

摘要：Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd, for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.