【作者】 胡延佳；

【导师】 翦新春；

【作者基本信息】 中南大学 ， 外科学， 2009， 博士

【摘要】 口腔粘膜下纤维性变(oral submucous fibrosis,OSF)是一种慢性、隐匿性、具有癌变倾向的口腔粘膜病,咀嚼槟榔是主要致病因素,OSF属于口腔癌前状态,癌变率高达7.6%。其发病机制目前尚不清楚。基因芯片技术和生物信息学的发展极大提高了我们对生物学研究和探讨疾病分子机制的能力。其高通量、平行、微型化等显著优点已经受到世界各国科学家高度重视,并已广泛用于肿瘤等多个领域研究。本研究采用Affymetrix U133A 2.0芯片构建OSF粘膜组织的基因表达谱;以正常口腔粘膜组织为对照,采用多种生物信息学方法筛选疾病差异表达基因;经逆转录聚合酶链式反应(reverse transcription polymerasechain reation,RT-PCR)对部分候选基因进行了验证,为进一步探讨OSF发生的分子机制和发现治疗靶基因奠定基础。第一章口腔粘膜下纤维性变差异基因表达分析[目的]:建立OSF的基因表达谱并筛选其差异表达基因。[方法]:收集正常和OSF患者颊粘膜组织各4例,提取其总RNA并制备cDNA,合成生物素标记的cRNA探针,经GeneChip Test3验证了探针质量合格后,与Aflymetrix HG-U133A2.0寡核苷酸基因芯片进行杂交,经过洗涤,扫描后用GeneChip Operating Software(GCOS)软件读取每一张芯片探针组中每一位点的表达值,扣除背景信号,自动分析每一基因位点的表达参数并给出参考值,输出数据,并利用dchip2006软件对原始数据进行归一化处理,构建出OSF组织的基因表达谱。再利用基因芯片显著性分析(signficance analysis of microarray,SAM)中两因素不配对算法计算假阳性率,筛选出其OSF差异表达基因。[结果]:芯片杂交结果符合质量控制要求;按照q-value＜0.05,以fold change＞2或＜0.5为筛选标准,得到OSF差异表达基因共计865个,其中上调基因716个,下调基因149个。[结论]:在全基因组范围内建立了OSF的基因表达谱并筛选其差异表达基因,为进一步的发病机制研究和寻找OSF分子标记物奠定了基础。第二章运用生物信息学方法进行差异表达基因的二次筛选[目的]:运用生物信息学方法分析口腔粘膜下纤维性变差异表达基因,挖掘其隐含的生物学意义。[方法]:利用多种生物信息学工具和软件对前期筛选的865个OSF差异表达基因进行聚类分析,GO分析、Pathway分析、染色体定位、遗传关联疾病分析和MILANO分析。[结果]:聚类分析显示所筛选的基因能很好区分正常组与病例组,其中最显著的基因亚类为免疫相关基因。GO分析显示差异表达基因主要参与免疫反应,细胞粘附、炎症反应、DNA依赖的转录调节等生物过程;大部分基因表达蛋白位于细胞外区域、细胞外基质和细胞质膜、膜或整合于膜上。119个基因为未知功能基因,另外有大量的钙铁锌结合分子,其他基因与受体激活和细胞外基质结构构成等分子功能相关;Pathway分析结果显示差异表达基因主要参与抗原过程和呈递、细胞外基质受体相互作用、局部粘附、细胞粘附分子、细胞因子间相互作用、TGF-β信号通路等通路。染色体定位显示差异表达基因主要定位于1、2、5、6、7、11和12号染色体上(P＜0.01);而与这些基因有遗传关联的疾病主要有感染,免疫和心血管疾病等。MILANO分析显示大部分在纤维化疾病和肿瘤中研究广泛的基因在OSF中未曾研究。[结论]:运用生物信息学工具可快速、平行地分析大量的基因芯片数据,实现对差异基因初步的功能归类,为OSF的发病机制和流行病学研究提供新的思路。第三章半定量RT-PCR对部分差异表达基因的验证[目的]:筛选和验证候选基因,并初步探讨其在OSF中的作用。[方法]:根据多种生物信息学方法和文献复习确定候选基因;抽提11例口腔粘膜下纤维化组织和10例正常口腔粘膜组织的总RNA,通过逆转录聚合酶链式反应检测候选基因在口腔粘膜下纤维性变患者颊粘膜和正常颊粘膜中mRNA的表达。[结果]:挑选出6个与上皮间质转化(epithelial-mesenchymaltransition,EMT)相关候选基因:sFRP4、THBS1、MMP2、CDH11、ZO-1、CK18。所有的RT-PCR检测表现出与芯片结果一致的变化趋势,统计学处理sFRP4、ZO-1、CK18的P值＜0.01,THBS1和MMP2的P值＜0.05,CDH11的P值＞0.05。[结论]:基因芯片结果可靠,EMT可能在OSF发病机制中起着重要作用。更多还原

【Abstract】 Oral submucous Fibrosis(OSF) is a kind of chronic insidious disease and it predisposes to cancer, the main aetiological factor of which is betel nut chewing. As a precancerous condition regarded by WHO, the malignant transformation rate of OSF was 7.6%. The pathogenesis of OSF is still unknown. The development of gene chip technology and bioinformatics boosts greatly our ability of studying biology medicine and exploring the molecular mechanism of diseases. Gene chip technology has become more and more important because of its virtue of high throughput, parallel detection, micromation etc, and it is now widely used in many fields including medical and biological researches.In this study, we studied the gene expression profiles of human OSF and normal buccal mucosa tissues by using the Affymetrix U133A 2.0 chips, and analyzed the differentially expressed genes of OSF tissues screened by microarray by using several bioinformatics tools. Candidate genes were further validated using semi- quantitative RT-PCR. The results obtained from the present study, therefore, determine the molecular pathways potentially involved in OSF pathogenesis and lay the groundwork for future analysis of these potential markers/targets for clinical utility in the diagnosis, prognosis and treatment of OSF.Chapter I : Identification of differentially expressed genes associated with oral submucous fibrosisObjective: To establish the gene expression profiling of OSF and screen differentially expressed genes of OSF.Material and Methods: Total RNA was isolated from buccal mucosa tissue samples of 4 OSF patients and 4 normal persons. Five ug of total RNA were used to prepare biotinylated cRNAs for hybridization using the standard Affymetrix protocol. After sample quality evaluation using a control microarray (Test 3), the hybridization cocktails were hybridized to the human genome U133A 2.0 GeneChips. After hybridization, washing and scanning, the hybridization data were exported using GeneChip Operating software (GCOS 1.4). The raw signal of individual probes for the 8 arrays were normalized against the chip with median raw signal intensity using the dchip software (dChip2006). Differentially expressed genes were identified by supervised analysis with the Significance Analysis of Microarrays (SAM) software. Normalized expression values from dChip analysis were used for a two class paired SAM analysis. The SAM software estimated the false discovery rate and generated a q value for each gene.Results: The results of hybridization of GeneChips accorded with the standard of quality control for affymetrix genechip. Using a q value of < 5%, a total of 865 significant probe sets were identified to have more than 2-fold changes between the OSF and normal buccal mucosa tissues. There were 716 up-regulated and 149 down-regulated probe sets in OSF.Conclusion: The gene expression profiles of OSF were established in whole genome. Differentially expressed genes associated with OSF were identified to provide theoretical foundation for the further studies in the molecular pathogenesis involved in OSF and searching potential biomarkers.Chapter II: The second Screen of the Differential Expression Genes of OSF using bioinformatics toolsObjective: To apply the bioinformatics tools for analyzing the differentially expressed genes in OSF to obtain the implied biological significance.Material and Methods: Several bioinformatic analysises were used in the second screening of 865 differentially expressed genes in OSF, which included cluster analysis, gene ontology(GO) analysis, pathway analysis, chromosome location, analysis of genetic-association diseases and MILANO analysis..Results: Cluster analysis showed the differential expression genes could distinguish OSF from normal tissues perfectly, the most striking subclusters were the immunity-related genes. GO classification of the differentially expressed genes identified the biological process subgroups, including genes involved in immune response, cell adhesion, inflammatory response, regulation of transcription, DNA-dependent et al. Cellular component subgroups consisted of genes mainly located in extracellular region, extracellular matrix and plasma membrane, membrane, integral to membrane. The function of 119 genes remains unkown. The others were mostly related to ion bingding, extracellular matrix structural constituent, receptor activity et el. Pathway analysis suggested the differently expressed genes mainly involved in pathways including antigen processing and presentation, ECM-receptor interaction, focal adhesion, cell adhesion molecules, cytokine-cytokine receptor interaction, TGF-beta signaling pathway et al. A majority of the differentially expressed genes were located on chromosome 1, 2, 5, 6, 7, 11, 12 (P<0.01).The diseases genetic associated with OSF included infection, immune and cardiovascular diseases. MILANO analysis revealed many genes wildly studied in fibrosis and cancer showed occurrences of low frequency in studies of OSF.Conclusion: Bioinformatic tools can provide the quick and parallel analysis of massive data derived from genechips and enable the function classification of the differentially expressed genes, which provides new clues on the research of pathogenesis and epidemiology of OSF.Chapter III: Verification of partial differently expressed genes of OSF by semi quantitative RT-PCRObjective: To valid candidate genes and explore their significance in the pathogenesis of OSF.Material and Methods: Candidate genes were chosen according to the result of bioinformatics analysis and literature review. The total RNA of 11 specimens of OSF buccal mucosa and 10 specimens of normal mucosa was extracted respectively. The reverse transcription polymerase chain reaction (RT-PCR) was used to examine the levels of mRNA of candidate genes in the buccal mucosa of OSF and normal buccal mucosa. Results: Six EMT-related genes were chosen to be valid by RT-PCR, which included SFRP4, THBS1, MMP2, CDH11, ZO-1, and CK18. Except for CDH11, the results of RT-PCR showed the consistent trend of change with the results of genechip (P<0.05).Conclusion: The results of genechip were demonstrated credible. EMT might play an important role in the pathogenesis of OSF.更多还原