Prediction of Psoriasis and Potential Treatment of Traditional Chinese Medicine Based on Reverse Network Pharmacology Analysis
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摘要:
目的 应用逆向网络药理学及分子对接技术研究银屑病发病机制。 方法 从genecards和NCBI数据库获取银屑病相关基因，取交集基因，构建PPI网络，确定核心差异基因，进行GO和KEGG富集分析，构建靶点信号通路网络图。利用TCMSP和symmap平台反向采集潜在治疗中药及相关活性成分，构建预测药物-活性成分网络图，筛选核心靶点和核心成分进行分子对接验证。 结果 获262个银屑病基因，GO、KEGG分析得多条生物条目及通路。结合PPI网络，确定7个关键基因。逆向收集20种中药和264种成分，构建网络图。分子对接验证7个基因和前5种成分。 结论 本研究通过分子对接验证白花蛇舌草、虎杖等中药及成分可调控IL1B、CXCL8等靶点，为中药组分治疗银屑病提供参考。 Abstract:Objective To study the pathogenesis of psoriasisand and to provide new research ideas and directions for the prevention and treatment of psoriasis. Methods Psoriasis-related genes were obtained from genecards and NCBI databases, and intersection genes were obtained. After PPI network constructed, core differential genes were determined. Based on GO and KEGG enrichment analysis, a target gene-signaling pathway network diagram was constructed. The TCMSP and symmap platforms were used to reversely collect potential therapeutic Chinese medicines and related active ingredients, construct a predicted drug-active ingredient network diagram, and screen core targets and core ingredients for molecular docking verification. Results 262 psoriasis genes were obtained, and multiple biological entries and pathways were analyzed by GO and KEGG. Combined with the PPI network, 7 key genes were identified. 20 kinds of traditional Chinese medicine and 264 kinds of ingredients were collected through reverse engineering to construct a network diagram. Molecular docking verified 7 genes and the first 5 components. Conclusion This study discovered potential drugs for the treatment of psoriasis, and verified, through molecular docking, that traditional Chinese medicines and ingredients such as Hedyotis diffusa and Polygonum cuspidatum can regulate IL1B, CXCL8 and other targets, providing a reference for traditional Chinese medicine components in the treatment of psoriasis. -
Key words:
- Psoriasis /
- Reverse network pharmacology /
- Molecular docking
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图 1 差异基因的韦恩图和火山图
A:韦恩图 ； B:火山图。
Figure 1. Venn diagram and volcano diagram of differential genes
图 2 PPI蛋白互作网络图可视化
注：节点越大，颜色越深，说明该基因与疾病相关性越高。
Figure 2. PPI network diagram visualization
图 3 差异基因GO富集分析柱状图和气泡图
A：差异基因GO富集分析柱状图；B：差异基因GO富集分析气泡图
Figure 3. Histogram and bubble chart of differential gene by GO enrichment analysis
图 4 差异基因KEGG通路分析气泡图和柱状图
A：差异基因KEGG通路分析气泡图； B: 差异基因KEGG通路分析柱状图，颜色越红代表富集程度越高，柱状图越长/气泡越大代表涉及靶点基因数量越多。
Figure 4. Bubble chart and histogram of differential gene by KEGG pathway analysis
图 5 预测药物-活性成分网络图和靶点基因-信号通路网络图
A: 靶点基因-信号通路网络图; B: 预测药物-活性成分网络图形状越大代表富集在该通路上的基因越多，中药所含该成分越高。
Figure 5. Target gene-signaling pathway network diagram and predicted drug-active ingredient network diagram
图 7 中药性味归经雷达图
A：四气雷达图；B：五味雷达图；C：归经雷达图
Figure 7. Radar diagram of the distribution of properties and flavors of traditional Chinese medicine
图 8 分子对接结果部分可视化图
Figure 8. Partial visualization of molecular docking results
A: IL1B- stigmasterol; B:CXCL8- stigmasterol; C: CCL2- kaempferol; D: CXCL8- sitosterol; E: CXCL8- beta-sitosterol; F:CCL2- beta-sitosterol。
表 1 潜在治疗药物分布
Table 1. Distribution of potential therapeutic drugs
基因 个数 药物 IL1B、IFNG、STAT3、STAT1、CXCL8、CXCL1、CCL2 7 高良姜、枸骨叶、虎杖、艾叶、白果、白花蛇舌草、穿心莲、鹅不食草、葛花、广枣、黄芪、前胡、麻黄、山豆根、山楂叶、鼠曲草、乌梅、仙鹤草、苎麻根 
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表 2 部分活性成分信息
Table 2. Partial active ingredient information
MOL ID 名称 度值 OB（%） DL MOL000098 槲皮素（quercetin） 18 46.43 0.28 MOL000358 β-谷甾醇（beta-sitosterol） 14 36.91 0.75 MOL000422 山柰酚（kaempferol） 12 41.88 0.24 MOL000449 豆甾醇（stigmasterol） 9 43.83 0.76 MOL000359 谷甾醇（sitosterol） 7 36.91 0.75 MOL000354 异鼠李素（isorhamnetin） 6 49.6 0.31 MOL000392 芒丙花素（formononetin） 5 69.67 0.21 MOL000492 绿茶多酚（（+）-catechin） 5 54.83 0.24
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表 3 核心靶点与核心活性成分分子对接结合能（kcal/mol）
Table 3. Molecular docking binding energy of core targets and core active ingredients （kcal/mol）
基因 成分 IL1B IFNG STAT1 STAT3 CXCL8 CXCL1 CCL2 quercetin −5.08 −4.01 −3.97 −4.03 −4.31 −5.12 −5.36 beta-sitosterol −5.57 −3.82 −4.95 −4.88 −5.44 −4.71 −6.93 kaempferol −5.61 −3.63 −4.06 −3.82 −4.44 −3.32 −6.54 stigmasterol −5.85 −4.07 −5.03 −4.20 −6.41 −4.28 −6.64 sitosterol −5.68 −4.32 −4.72 −3.85 −6.52 −3.14 −6.29
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