基于计算机文本挖掘的多形性胶质母细胞瘤放化疗后呕吐基因功能富集及药物治疗分析

Enrichment of Vomiting Gene Function and Drug Therapy of Glioblastoma Multiforme after Radiotherapy and Chemotherapy Based on Computer Text Mining

摘要: 目的：使用计算机软件等工具和公开的数据库挖掘和分析多形性胶质母细胞瘤放化疗过程中诱发的呕吐和胃粘膜相关的基因集和信号通路，预测可能治疗呕吐的潜在有效药物。方法：通过文本挖掘网站pubmed2ensembl确定与呕吐和胃粘膜相关的基因，并将得到的基因集用Venny 2.1.0取交集，DAVID网站对交集基因集进行基因功能富集分析和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes, KEGG)通路分析。使用STRING-db和Cytoscape 3.2.1进行蛋白质相互作用网络分析和模块分析。GEPIA进一步确定模块分析的结果。采用DGIdb分析药物–基因相互作用的结果。结果：筛选出969个“止吐”和“胃粘膜”的交集基因集，经过对交集基因集的生物学功能进行富集分析和KEGG通路分析、蛋白质之间相互作用分析和模块分析，筛选出相关的52个基因。最终与正常组织相比箱式图有统计学意义并且存在生存率差异的为SAA1基因。针对SAA1基因的药物有3种，分别为ALBUMIN HUMAN、NAPROXEN、ANAKINRA。结论：使用计算机文本挖掘等生物信息学相关的软件和网站工具可以进一步探索呕吐的发病机制，并且，可以预测可能有效治疗的药物，提供改善呕吐的可能性。

Abstract: Objective: To use computer software and other tools and public databases to mine and analyze the vomiting induced during radiotherapy and chemotherapy of glioblastoma multiforme and the gastric mucosa-related gene sets and signal pathways, and predict potential effective drugs for the treatment of vomiting. Method: The genes related to vomiting and gastric mucosa were determined through the text mining website pubmed2ensembl, and the obtained gene set was crossed with Venny 2.1.0. The DAVID website performed gene function enrichment analysis on the intersection gene set and the Kyoto Encyclopedia of Genes and Genomes (Kyoto Encyclopedia of Genes and Genomes, KEGG) pathway analysis. Use STRING-db and Cytoscape 3.2.1 for protein interaction network analysis and module analysis. GEPIA further determines the results of the module analysis. DGIdb was used to analyze the results of drug-gene interactions. Result: Screen out 969 intersection gene sets of “antiemetic” and “gastric mucosa”. After enrichment analysis and KEGG pathway analysis of the biological functions of the intersection gene set, interaction analysis between proteins and module analysis, 52 related genes were screened out. In the end, compared with normal tissues, the box plot is statistically significant and the survival rate difference is the SAA1 gene. There are 3 kinds of drugs for SAA1 gene, namely ALBUMIN HUMAN, NAPROXEN, ANAKINRA. Conclusion: The use of computer text mining and other bioinformatics-related software and website tools can further explore the pathogenesis of vomiting, and it can predict the possible effective treatment of drugs and provide the possibility of improving vomiting.