联合多种高通量表达谱数据分析筛选胃癌相关基因

目的进一步寻找潜在的胃癌与正常组织间有诊断与治疗价值的基因标志。方法分别应用肿瘤基因组解剖工程中表达序列标签和基因表达序列分析表达谱数据库筛查胃癌和正常组织差异表达基因，用虚拟电子杂交方法对候选基因进行进一步筛选，并同斯坦福微阵列数据库中胃组织基因表达谱芯片数据比较。结果应用NCBI在线数字差异显示工具、cDNA数字基因表达分析工具和SAGE数字基因表达分析工具分别筛选出165、286和181个基因，经虚拟Nonhern分析共获得45个差异表达基因，将其与芯片数据比较分析，其中12个基因表达一致，对其中与胃癌关系尚未见明确报道的基因进行RT-PCR验证，ANXA1、MSMB、ANXA10和PSCA4个基因与数据筛选结果一致。结论利用表达谱数据库资源能快速高效地筛选、确定胃癌相关基因．对这些基因进一步分析可为胃癌的临床诊治提供分子标志。

Identification of gastric cancer-related genes by multiple high throughput analysis and data mining

Objective To investigate gastric cancer-related genes by combined multiple high throughput analysis and data mining,and to further identify gene markers that may be useful in the diagnosis and treatement of gantric cancer.Methods Data of expressed sequence tags(EST)and serial analysis of gene expression(SAGE)in Cancer Genome Anatomy Project(CGAP)were employed to analyze differential gene expression between normal and cancerous gastric epithelium,the obtained genes were further analyzed by virtual Northern blotting and compared with microarray data from Stanford Microarray Database(SMD).Results NCBI digital differential display(DDD),cDNA digital gene expression displayer(DGED)and SAGE DGED produced 165,286 and 181 differential expression genes.All these genes were analyzed by virtual Northern blotting and 45 genes were obtained.Comparing with microarray data,candidate genes were reduced to 12.Further RT-PCR analyses validated 4 genes,including ANXA1,MSMB,ANXA10 and PSCA,were differentially expressed in normal and cancerous gastric tissues.Conclusions Combined multiple high throughput analysis and data mining is an effective strategy for identification of gastric cancer-related genes.Further analyses of these genes from data mining will provide biomarkers for the diagnosis and treatement of gastric cancer.