基于TCGA数据库分析甲状腺癌基因表达谱

为分析甲状腺癌基因表达谱,筛选疾病相关的基因标志物。基于肿瘤基因组图谱(TCGA)数据库中的甲状腺癌基因表达数据,运用R/Bioconductor统计平台进行数据处理与统计学分析。分别应用edgeR算法和limma算法选取肿瘤组织与对照组间倍数改变 > 2,P< 0.05的基因作为差异基因；进一步运用Medcalc统计软件进行受试者工作特征曲线(ROC)分析,鉴定出有诊断标志物潜在应用价值的基因标志物。通过两种运算方法筛选出甲状腺癌组织中存在着1 945个差异基因(上调基因1 033个,下调基因912个)；根据差异倍数进一步鉴定出11个基因在肿瘤组织中表达上调,且对鉴别肿瘤组与对照组有较好的应用价值。本研究分析了TCGA中的甲状腺癌表达谱数据,鉴定出了与疾病诊断显著相关的差异表达基因,能够为探索疾病发生发展机制及寻找新型分子标志物提供依据。

Analysis of thyroid cancer gene expression profile based on TCGA database

To explore the gene expression profile and screen disease-related biomarkers of thyroid cancer (THCA), the R/Bioconductor statistical platform was used for data processing and statistical analysis based on the THCA gene expression data in the TCGA database. Genes with fold change (FC) >2, P<0.05 between tumor and control tissues were selected as differentially expressed genes (DEGs) based on both edgeR package and limma package in R/Bioconductor. Then,the Medcalc statistical software was used for receiver operating characteristic (ROC) curve analysis to identify genetic biomarkers with potential application value as diagnostic markers. By combining the results from the two algorithms, a total of 1 945 DEGswere obtained in the THCA tumors (1 033 up-regulated genes and 912 down-regulated genes). Further, 11 DEGs were identified up-regulated in the tumor tissues,which showed good application values for distinguishing the tumor group from the control group. This study analyzed the THCA expression profile data in TCGA and identified DEGs that are significantly related to disease diagnosis, which can provide basis for exploring the mechanism and novel molecular markers of THCA.