垂体瘤的基因芯片数据生物信息学分析

Bioinformatics Analysis of Gene Chip Data for Pituitary Tumors

目的应用生物信息学技术筛选参与垂体瘤发生发展过程中的关键基因及通路,为垂体瘤治疗提供新靶点。方法基于基因表达微阵列芯片数据集GSE51618及GSE26966,使用在线分析工具GEO2R筛选差异表达基因,利用GeneOntology(GO)和Kyoto Encyclopedia of Genes and Genomes(KEGG)对差异基因的功能进行富集分析,通过STRING及Cytoscape构建蛋白-蛋白相互作用网络并筛选出关键基因及预测关键转录因子。结果共筛选到符合条件的差异表达基因354个,其中共同上调基因296个,共同下调基因58个。GO富集分析显示其主要参与细胞表面受体信号通路、细胞增殖等生物学过程。KEGG富集通路分析发现其富集于PI3K-Akt信号通路、催乳素信号通路、MAPK信号通路、多巴胺能神经突触、细胞周期调控等信号通路上。共筛选出RIPK4、POMC、ESR1、EGR1、GNB3、FOS、TF7个可能为诱导垂体瘤发生发展的关键基因,并预测出SP1、LHX3等调控差异基因的关键转录因子。结论通过生物信息学对差异基因和关键基因进行分析,有助于揭示垂体瘤发生发展的重要分子机制,为其治疗提供新靶点。

Objective To apply bioinformatics technology to screen key genes and pathways involved in the occurrence and development of pituitary tumors,and to provide new targets for the treatment of pituitary tumors.Methods Based on the gene expression microarray chip datasets GSE51618 and GSE26966,the online analysis tool GEO2R was used to screen differentially expressed genes.GeneOntology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)were used to enrich the function of the differential genes.Through STRING and Cytoscape constructs a protein-protein interaction network and screens out key genes and predicts key transcription factors.Results In this study,a total of 354 eligible differentially expressed genes were screened,of which 296 genes were up-regulated and 58 genes were down-regulated.GO enrichment analysis showed that it was mainly involved in biological processes such as cell surface receptor signaling pathways and cell proliferation.Analysis of the KEGG enrichment pathway found that it was significantly enriched in signal pathways such as the PI3K-Akt signaling pathway,prolactin signaling pathway,MAPK signaling pathway,dopaminergic synapses,and cell cycle regulation.A total of RIPK4,POMC,ESR1,EGR1,GNB3,FOS,and TF7 were screened as key genes for inducing the development of pituitary tumors,and key transcription factors such as SP1 and LHX3 that regulate differential genes were predicted.Conclusion The analysis of differential genes and key genes through bioinformatics can help reveal the important molecular mechanism of pituitary tumor development and provide new targets for its treatment.