头颈部鳞状细胞癌差异表达基因的生物信息学分析

Bioinformatics analysis of DEGs in HNSCC

目的:采用生物信息学技术挖掘头颈部鳞状细胞癌(HNSCC)差异表达基因(DEGs)和信号通路,为HNSCC的治疗寻找新的治疗靶点。方法:从基因表达综合(GEO)数据库下载基因芯片数据集GSE6631,利用GEO2R筛选DEGs。采用注释、可视化和整合发现数据库(DAVID)对DEGs进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路分析;通过蛋白质相互作用的String数据库和Cytoscape软件构建DEGs对应的蛋白质相互作用网络,并从网络中筛选出核心基因。利用基因表达谱分析(GEPIA)网络服务器在线分析关键基因(Hub基因),制作箱线图比较核心基因在HNSCC肿瘤和正常组织的表达量及错误发现率(FDR),验证具有显著表达的核心基因。结果:共筛选出150个表达差异明显的基因,其中包括85个上调基因,65个下调基因。以P<0.05为筛选条件,上调基因富集到52个生物学过程及7条富集通路;下调基因富集到37个生物学过程及3条富集通路。所有DEGs共有49个生物学过程呈显著性富集(t=-0.033,P<0.05,FDR<0.05),6条富集通路呈显著性(FDR<0.05)。共计63个DEGs及322条互作关系的蛋白质相互作用网络,并选出degree值排名前10的DEGs作为核心表达基因,其在HNSCC组织中均为高表达。结论:采用生物信息学方法能有效分析HNSCC与正常组织DEGs,筛选出的10个DEGs,均为HNSCC发病过程中具有显著意义的生物标记物。

Objective:To explore the differential expression genes(DEGs)and signaling pathways of head and neck squamous cell carcinoma(HNSCC)by bioinformatics technology so as to look for new therapeutic target for the treatment of HNSCC.Methods:The microarray data set GSE6631 was downloaded from the gene expression omnibus(GEO)database,and DEGs were screened by GEO2R.Annotated,visualized and integrated discovery database(David)was used for the enrichment analysis of gene ontology(GO)and the pathway analysis of Kyoto Encyclopedia of genes and genomes(KEGG)on DEGs.And the string database and Cytoscape software of protein interaction were used to construct interaction network of proteins which corresponded with DEGs,and then the core genes were selected from the network.The key genes(hub genes)were analyzed online by web server of gene expression profiling interactive analysis(GEPIA),and box plot was made to compare the expression quantity and false discovery rate(FDR)of core genes in HNSCC and normal tissues,and to verify the core genes with significant expression.Results:A total of 150 genes with significantly differential expression were screened,including 85 up-regulated genes and 65 down regulated genes.That P value was less than 0.05 was used as the screening condition,the up-regulated genes were enriched to 52 biological processes and 7 enrichment pathways,while the down regulated genes were enriched to 37 biological processes and 3 enrichment pathways.Of all the differential genes,49 biological processes were significantly enriched(t=-0.033,P<0.05,FDR<0.05),and 6 enrichment pathways were significantly enriched(FDR<0.05).A total of 63 DEGs and 322 protein-protein interaction networks with the relationship of interaction were identified,and the top of 10 DEGs of degree value were selected as the core expression genes,which were highly expressed in HNSCC tissues.Conclusion:The adopted Bioinformatics method can effectively analyze the DEGs of HNSCC and normal tissues,and the screened 10 DEGs are significant biomarkers in the pathogenetic process of HNSCC.