基于癌基因组图谱挖掘胃癌预后相关基因

Identification of genes associated with gastric cancer prognosis from TCGA datasets

目的利用癌基因组图谱数据库中的大量胃癌基因组数据,在高频突变或肿瘤组织高表达的基因中挖掘与临床分化和预后相关的基因。方法从癌基因组图谱数据集中下载胃癌基因组的突变数据,基因表达数据以及病例样本的临床信息资料。筛选并统计出现有害突变频率较高的基因,并比较这些基因在不同分化程度病例中的突变次数。同时利用DESeq2对基因表达数据进行差异表达分析,对于在肿瘤组织中高表达的基因做生存分析,使用Log-rank检验并做FDR校正,筛选出mRNA表达量与胃癌预后相关联的基因。结果突变分析得到PIK3CA和APC的有害突变频率在不同分化程度的胃癌存在差异(Fisher's exact test:P<0.05)。差异表达分析得到2 040个在肿瘤组织中上调的基因,2 357个在肿瘤组织中下调的基因。生存分析得到8个对胃癌预后有影响的基因,1个为保护因素,7个危险因素。结论本研究通过挖掘癌基因组图谱中的胃癌数据集,得到了ANGPT2、MMP10和WISP3等与胃癌分化及预后相关的基因,为接下来的研究提供了线索和依据,也为临床治疗提示了新的预后指标。

Objective To identify genes associated with prognosis or differentiated type in gastric cancer from fre- quently mutated genes or highly-expressed genes, using large-scale genomic data from The Cancer Genome Atlas. Methods The somatic mutation data, RNAseqV2 data and clinical information were downloaded from the TCGA website. The frequency of deleterious somatic mutations for each gene was counted to select the frequently mutated genes. DESeq2 was used to analyze the gene expression data. Then survival analysis was performed on genes highly- expressed in the tumor tissue. Kaplan-Meier curves were generated by R-survival package, and significance was e- valuated by log-rank test. Results The frequency for pathogenic mutations in PIK3CA and APC was significantly discordant between different grades of gastric cancer. 2 040 genes were up-regulated in tumor tissue, while 2 357 genes were down-regulated. Among the up-regulated genes, 7 genes were associated with poor prognosis of gastric cancer and one was associated with better prognosis. Conclusions Genes associated with differentiation types or prognosis in gastric cancer are identified. The result may clue us future research on potential prognostic markers in clinical treatment.