卵巢癌相关长链非编码RNA的生物信息学挖掘

Bioinformatics Mining of Relevant Long Non-Coding RNA in Ovarian Cancer

探讨卵巢癌中差异表达的长链非编码RNA,从NCBI基因芯片数据库GEO下载卵巢癌基因芯片数据GSE14407,用SAM软件输出差异表达的基因,对其进行重注释并筛选出其中的长链非编码RNA,用Gene Cluster和Tree View软件验证SAM软件分析结果。针对该芯片数据,筛出133个在卵巢癌中表达有差异的长链非编码RNA,其中112个上调,21个下调,且这些长链非编码RNA的表达倍数均>2或<0.5,差异有统计学意义(q值<0.05)。用生物信息学方法挖掘普通基因芯片中卵巢癌相关长链非编码RNA是十分有效的方法,可为卵巢癌相关长链非编码RNA的探索提供新途径。

To investigate the differential expression of long non-coding RNA in ovarian cancer, the microarray data of ovarian cancer GSE 14407 was downloaded from Gene Expression Omnibus （GEO）, the public data platf- orm of NCBI. The data were analyzed by SAM software, which generated some differentially-expressed gene probes. Then we reannotated the probes and used the Gene Cluster and TreeView software to further validate the analysis of SAM. Based on the data from this microchip, 133 differentially-expressed long non-coding RNA in ovarian cancer tissue were screened, among which 122 were up-regulated and 21 were down-regulated. All long non-coding RNA showed notably differential expression and the fold-change ofgene expression was more than 2 or less than 0.5 （q〈0.05）. Bioinformatics may provide an efficient approach to screen ovarian cancer-associated long non-coding RNA and a novel way to study long non-coding RNA for ovarian cancer.