利用基因芯片技术筛选肝癌早期复发相关基因

Identification of differentiated expression genes associated with early recurrence for hepatocellular carcinoma using genechip technology

目的:利用基因芯片技术在全基因组范围内检测、比较早期复发和非早期复发肝癌患者肿瘤组织基因表达谱的差异,筛选与肝癌早期复发相关的敏感基因。方法:由本单位肝癌数据标本库中选择早期复发组(ER组)和非早期复发组(nER组)各10例患者进入本部分研究。提取总RNA并纯化,检验RNA质量,反转录得到cDNA,并合成生物素标记的cRNA。用Affymetrix Human Geome U133plus2.0基因表达谱芯片与待检测样本的cRNA杂交;利用Gene array Scanner30007G荧光激光扫描系统对芯片进行扫描,芯片扫描结果图像采用GCOS1.2进行数据定量处理和分析,以芯片实验中检测到有表达的转录本为基础,采用SAM软件、以FDR≤20%的统计方法筛选出差异表达更显著的基因群,并以此基因群做两组样本的层次聚类分析和主成分分析,并进一步挖掘生物学信息。结果:经琼脂糖凝胶电泳检查RNA提取物的完整性证实待检样品RNA的质量可靠。两组样本间共有1646个基因表达存在差异(pvalue<0.05),利用SAM,FDR≤20%筛选出的41个探针作聚类分析和主成分分析,可将两组患者样本完整区分开来。根据差异表达基因的Ratio值,ER组较nER组表达上调>2倍的基因共有67个,其中有2个基因表达上调>4倍,13个基因表达上调>3倍,52个基因表达上调在2倍与3倍之间。ER组较nER组表达下调>2倍的基因共有66个,其中有7个基因表达下调>4倍,10个基因表达下调>3倍,49个基因表达下调在2倍与3倍之间。这些差异表达基因主要为细胞黏附运动相关基因、转录调节相关基因、细胞代谢相关基因、信号转导相关基因、细胞凋亡周期相关基因等等。结论:利用基因芯片技术筛选肝癌早期复发相关基因是可行的,并筛选到一组与肝癌早期复发相关的差异表达基因。对这些基因功能的深入研究,将有望筛选到与肝癌早期复发密切相关的分子靶标,为肝癌的生物治疗提供新的靶点和思路。

Objective To identify the differentiated expression genes associated with early recurrence after curative resection for hepatocellular carcinoma（HCC） using genechip technology. Methods Twenty cases selected from ER group and nER group randomly were entered into this study. Total RNA was extracted from each HCC frozen stored tissue. RNAs were checked its quality and integrity. And then the RNA was reverse transcribed into double strand cDNA. After the cDNA was transcribed into biotinylated cRNA target, purified and fragmented it according to the manufacturer′s instructions. The cRNAs were hybridized with the oligonucleotide microarray （Affymetrix Human Genome U133 plus 2.0）. The chips were scanned by using Gene array Scanner3000 7G. The picture signals of the fluorescence in the gene array were analyzed with GCOS 1.2 and the ratios of signal density（ER/nER） were obtained. The differential expression genes （DEGs） identified by SAM analysis were clustered and appeared as treeview pattern. Unsupervised hierarchial clustering analysis and principal component analysis were also performed by certain software. The biological process and the molecular function of the DEGs were further investigated by Gene Ontology and Netaffx. Results Total RNAs of the HCC tissues were all high quality. There were 1 646 probes been selected by t-test between the 2 groups（P 0.05） and 41 probes were identified by SAM analysis （FDR≤20%）. Above all, the 20 cases could be divided into 2 groups correctly by these 41 probes using unsupervised hierarchial clustering analysis and principal component analysis. The screening results showed that there were 2 DEGs up-regulated at least 4 fold （log ratio≥2）, 13 DEGs up-regulated at least 3 fold and 52 DEGs up-regulated 2-3 fold, totally. And there were 7 DEGs down-regulated at least 4 fold, 10 DEGs down-regulated at least 3 fold and 49 DEGs down-regulated 2-3 fold. It also showed that the pathological and physiological process of early recurrence in HCC is very complex. Numbers of kinds of genes involved in the procedure, including genes associated with cell adhension and cell motility, genes associated with transcription regulation, genes related to cell apoptosis and cell cycle, genes related to cell metabolism and genes associated with signal transduction, etc. Conclusions The application of Affymetrix gene chip technique for analysis of gene expressions profile is a powerful strategy to identify DEGs associated with early recurrence of HCC. The further study of the identified DEGs may provide us new insights into the molecular mechanism of early recurrence of HCC. Through comprehensively analysis of the more up or down-regulated genes, our research project will screen some new potential therapeutic targets for HCC.