宫颈癌差异表达基因筛选及功能分析

目的:筛选参与宫颈癌发生、发展的关键基因,为临床诊疗提供新的靶点。方法:在NCBI-GEO数据库中筛选多组宫颈癌基因表达检测数据集,利用GEO2R分析工具筛选各组数据集的差异表达基因;应用R分析筛选不同数据集之间共有的差异表达基因;利用DAVID在线分析对差异表达基因进行功能聚类和通路分析;利用STRING分析差异表达基因编码蛋白之间的相互作用关系。结果:共选择6组表达数据集,筛选得到59个差异表达基因(宫颈癌组织vs正常组织),表达差异至少达2倍,其中包含50个表达上调基因及9个表达下调基因。这些差异表达基因参与细胞周期、DNA复制、细胞分裂等生物进程。蛋白互作分析表明,这些差异表达基因多数存在相互作用。结论:利用生物信息学方法对不同来源的基因检测数据进行整合分析,有助于更准确的筛选对宫颈癌发生、发展过程具有重要作用的关键基因,本文筛选的宫颈癌差异基因为进一步研究宫颈癌发生、发展的分子机制及临床诊疗提供思路。

Screening of Differential Expressed Genes in Cervical Cancer and Functional Analysis

ABSTRACT Objective: To screen the key genes which contribute to cervical cancer progression, and provide novel potential target for clinical diagnosis and therapy. Methods: GEO datasets of cervical cancer were obtained from NCBI-GEO database. GEO2R was used to screen the genes with increased and decreased expression level in cervical cancer tissues. R was used to identify the common genes between different GEO datasets. DAVID was used to analyze gene ontology and pathway for these genes. STRING was used to analyze the interaction of proteins which is encoding by these genes. Results: 59 common genes were indentified from 6 GEO datasets, which contained 50 genes with increased expression level and 9 genes with decreased expression level in cervical cancer tissues. These genes were found to be involved in cell cycle, DNA replication, cell division processes. Protein - protein interaction reveals the network between these genes. Conclusion: Bioinformatics analysis can be used to effective screen cancer related genes. These candidate genes in our data would be helpful in disclosing the molecular mechanism of cervical cancers, and also, helpful for clinical diagnosis and treatment.