【作者】 陈伟东；

【导师】 秦新裕； 孙益红； 施前；

【作者基本信息】 复旦大学 ， 外科学， 2010， 博士

【摘要】 本研究通过比较胃癌原发灶与配对淋巴结转移灶中小RNA表达谱,寻找差异表达小RNA,并检测其在胃癌原发灶中的表达量,研究差异表达小RNA与胃癌淋巴结转移的关系。最后通过生物信息学方法预测其靶基因,研究小RNA参与胃癌淋巴结转移的下游通路,旨在为胃癌淋巴结转移机制研究探寻潜在的靶基因。一、激光捕获显微切割纯化肿瘤与RNA质检体系的建立目的：建立激光显微切割纯化肿瘤与微量RNA提取、质检的标准流程,为后续实验提供质量可靠的RNA标本。方法：使用紫外分光光度仪、琼脂糖凝胶电泳、real-time PCR检测不同离体时间正常胃粘膜中RNA降解的情况。选取5例胃癌原发灶及配对淋巴结转移灶,使用激光捕获显微切割获得纯化肿瘤标本,紫外分光光度仪、琼脂糖凝胶电泳、Agilent2100生物分析仪检测纯化标本的RNA质量。结果：抽提自离体时间分别为0’,15’,30’,60’,120’,240’的胃正常粘膜中的总RNA未见明显降解；miR-125b、U6、18S和GAPDH在0’和240’两组中的表达量无明显差异。激光捕获显微切割8um厚切片10mm2能获得606-1038ng的总RNA量,激光捕获显微切割标本中存在总RNA降解。Agilent2100生物分析仪检测见激光捕获显微切割标本中总RNA降解,但小RNA水平未见明显降解。结论：离体时间在240’内的正常胃粘膜中总RNA无明显降解；总RNA水平的降解并不指示小RNA水平的降解；激光捕获显微切割所获总RNA质与量满足后续实验要求。二、胃癌原发灶与淋巴结转移灶间差异表达小RNA筛选及其与淋巴结转移的关系分析目的：寻找胃癌原发灶与配对淋巴结转移灶间差异表达的小RNA,研究差异表达小RNA在胃癌组织标本中的表达情况及其与淋巴结转移的关系。方法：使用小RNA芯片检测5例胃癌原发灶与配对淋巴结转移灶,寻找差异表达的小RNA。使用Stem-loop RT-PCR检测33例胃癌患者术后标本中差异表达小RNA的表达情况,并统计其表达与淋巴结转移的关系。结果：小RNA芯片共检测到5个表达有统计学差异的小RNA,相对原发灶,miR-24-1*、miR-510、miR-1284在淋巴结转移灶中低表达,miR-10a、miR-1259在淋巴结转移灶中高表达。miR-10a、miR-24-1*、miR-510在33例胃癌原发灶中的表达量检测显示,miR-10a在伴有淋巴结转移的原发灶中的表达量低于不伴淋巴结转移的(p=0.047),但在伴淋巴结转移的原发灶中,miR-10a表达量与淋巴结转移程度无相关性。结论：在胃癌原发灶与淋巴结转移灶中检测到miR-10a、miR-24-1*、miR-510、miR-1259、miR-1284等5个差异表达小RNA,其中miR-10a在伴淋巴结转移的胃癌原发灶中较不伴淋巴结转移的表达低,但其表达量与淋巴结转移程度无相关性。三、差异表达小RNA的靶基因预测与筛选目的：预测差异表达小RNA的靶基因,研究小RNA参与胃癌淋巴结转移的下游通路,为后续研究小RNA调控的胃癌淋巴结转移通路提供潜在分子目标。方法：使用TargetScan预测miR-10a、miR-24-1*和miR-510的靶基因,检索已发表的胃癌淋巴结转移相关基因谱或蛋白质谱相关研究,对比优化小RNA的靶基因预测。结果：使用TargetScan分别为miR-10a、miR-24-1*和miR-510预测到186个、438个和62个靶基因。结合已发表文献,进一步筛选得miR-10a靶基因CDK6、TPM4;miR-24-1*靶基因PURA;miR-510靶基因EGR1。结论：经生物信息学方法获得miR-10a靶基因CDK6、TPM4;miR-24-1*靶基因PURA;miR-510靶基因EGR1。四、小结经激光捕获显微切割获得的RNA标本质与量均满足后续小RNA研究的要求。小RNA芯片检测到胃癌原发灶与淋巴结转移灶间miR-10a、miR-24-1*、miR-510、miR-1259、miR-1284等5个差异表达小RNA,其中miR-10a在胃癌原发灶中的表达量与有无淋巴结转移有关,但与淋巴结转移程度无关。生物信息学研究获得miR-10a靶基因CDK6、TPM4;miR-24-1*靶基因PURA;miR-510靶基因EGR1。这四个基因可作为后续胃癌淋巴结转移机制研究的目标,其中miR-10a介导的通路可作为重点关注的对象。更多还原

【Abstract】 In this study of gastric cancer, we compare the primary gastric cancer and corresponding lymph node metastases in microRNA expression profiling to find the differential expression of microRNA. Meanwhile, we detect the lymph node metastasis predicting capbility of differential expression microRNA. Finally, adoption of bioinformatics predicts differential expression microRNA target genes to find out the candidate target genes suitable for follow up research.Part One:The establishment of tumor tissue purification and RNA quality control systemObjective:To establish the standard process of tumor tissue purification by laser microdissection and RNA extraction and quality control system. Methods:Detect normal gastric mucosa RNA in vitro degradation by UV spectrophotometer, agarose gel electrophoresis, real-time PCR. Select five cases of primary gastric cancer and corresponding lymph node metastases, purify the tumor tissue by laser microdissection and test RNA quality by UV spectrophotometer, agarose gel electrophoresis and Agilent2100 bio-analyzer. Results:According to the time in vitro, six groups were set in 0′,15′,30′,60′,120′,240′, respectively. No obvious RNA degradation was found by UV spectrophotometry and agarose gel electrophoresis.The real-time PCR detection found expression of miR-21, U6,18S, and GAPDH at 0′and 240′were no significant difference. UV spectrophotometer and agarose gel electrophoresis shown the existence of total RNA degradation in laser microdissected samples. Agilent2100 bio-analyzer indicated total RNA degradation, but no significant degradation of the microRNA. Conclusions:Within the in vitro time of 240′, the normal gastric mucosa had no significant degradation of total RNA. The degradation of total RNA does not indicate the degradation of microRNA. Laser microdissection derived total RNA quality and quantity meets the follow-up experiments’requirements.Part two:Microarray analysis of microRNA expression and validation in a gastric cancer cohortObjective:Search for different expressing microRNA between gastric cancer primary tumor and corresponding lymph node metastasis. Study the differential expressed microRNAs in a gastric cancer cohort. Methods:The use of microRNA microarray tested five cases of primary gastric cancer and corresponding lymph node metastases to find differential expression of microRNA. The use of stem-loop RT-PCR detected differential expressed microRNAs in a cohort with 33 cases of gastric cancer, and found the correlation between differential expressed microRNAs and lymph node metastasis. Results:Microarray had found five significant differential expressed microRNAs including miR-24-1*, miR-510, miR-1284 which were down-expressed in metastases, and miR-10a, miR-1259 which were up-expressed in metastases. Stem-loop RT-PCR found miR-10a expression is significantly correlated with metastasis, but the expression level is not correlated with the amount of metastatic lymph nodes. Conclusions:we have found five significant differential expressed microRNAs including miR-10a, miR-24-1*, miR-510, miR-1259, miR-1284. miR-10a expression is significantly correlated with metastasis, but the expression level is not correlated with the amount of metastatic lymph nodes.Part Three:Targets prediction of differential expression microRNAsObjective:Predict the target genes of differential expressed microRNAs to provide the candidates of further research. Methods:The use of TargetScan to predict the target genes of miR-10a、miR-24-1*、miR-510 respectively. Search for published dates from gastric cancer gene expression profiling or proteomics studies to improve the prediction efficiency. Results:We have found 186,438,62 genes of miR-10a, miR-24-1*, miR-510 by TargetScan respectively. Combined the published date, we further filter out CDK6 and TPM4 as miR-10a’s target gene, PURA as miR-24-1*’s and EGR1 as mi-510’s. Conclusions:we filter out CDK6 and TPM4 as miR-10a’s target gene, PURA as miR-24-1*’s and EGR1 as mi-510’s by bioinformatics.Part four:SummaryLaser microdissection derived total RNA quality and quantity meets the follow-up experiments’requirements. five significant differential expressed microRNAs were found in microarray analysis, including miR-10a, miR-24-1*, miR-510, miR-1259, miR-1284. miR-10a expression is significantly correlated with metastasis, but the expression level is not correlated with the amount of metastatic lymph nodes. CDK6 and TPM4 is found to be miR-10a’s target gene by bioinformatics, so as PURA to miR-24-1* and EGR1 to mi-510. These four genes can be candidates in following research.更多还原