Affymetrix基因表达谱芯片在新疆维吾尔族与汉族胰腺癌组织样本间差异表达基因筛选中的运用

目的:运用基因表达谱芯片筛选并分析新疆维吾尔族与汉族胰腺癌组织样本间的差异表达基因。方法:收集我院2014年1月至2016年6月间行手术切除的维吾尔族与汉族胰腺导管细胞癌组织并提取总RNA,选取经Nanodrop 2000与Agilent 2100仪器质检合格的样本总RNA采用Affymetrix基因表达谱芯片筛选出差异表达基因并绘制统计图,运用基因本体(GO)分析及信号通路(Pathway)分析对这些差异表达基因的生物信息进行汇总分析。结果:通过基因表达谱芯片分析,新疆维吾尔族与汉族胰腺癌组织样本间共检测到1063个基因存在差异表达,在维吾尔族胰腺癌标本中显著上调表达的基因共281个,差异表达倍数最高的为IGLV1-44基因(差异倍数:9.99)下调表达的基因共782个,差异表达倍数最高的为CPB1基因(差异倍数:33.76);在Gene Ontology数据库中共检索到815个上述差异表达基因具有明确的GO分类,差异表达倍数最高的为CPB1基因(差异倍数:33.76);Pathway分析中共检测到30条信号通路包含有上述差异表达基因,共涉及196个基因,其中以FAK信号通路差异表达基因富集程度最高,差异表达倍数最高的基因为COL11A1基因(差异倍数:5.02)。结论:基因表达谱芯片分析结果显示,在新疆维吾尔族与汉族胰腺癌组织样本间存在大量的差异表达基因,这些基因与胰腺癌的增殖分化、侵袭转移及多药耐药等特性密切相关,且参与了多条生物体内重要信号转导通路的调控。     

利用生物信息学研究肥胖与2型糖尿病患者肝组织基因表达变化

目的:利用芯片数据分析工具对GEO基因芯片数据进行数据挖掘,系统分析肥胖与2型糖尿病患者肝组织相关基因表达的变化,探讨肥胖与2型糖尿病的联系及糖尿病早期预防和诊断的新靶点。方法:首先在公共芯片数据库中选择肥胖与2型糖尿病相关芯片数据(GSE15653),利用R等芯片数据分析工具分析肥胖与2型糖尿病患者肝组织基因的表达变化,并预测相关差异表达基因在血中蛋白表达。结果:肥胖患者与正常人肝组织比较发现412个差异表达基因,其中上调表达基因212个,下调表达基因200个,2型糖尿病患者中控制良好者与正常人肝组织比较发现486个差异表达基因,其中上调表达基因253个,下调表达基因233个,而2型糖尿病患者中控制不良者与正常人肝组织比较发现1051个差异表达基因,其中上调表达基因560个,下调表达基因491个;2型糖尿病控制良好者与肥胖患者肝组织有263个相同的表达变化基因,而2型糖尿病控制不良者与肥胖患者肝组织有131个相同的表达变化基因;结合蛋白质组学结果分析肥胖与2型糖尿病相关的差异表达基因中有30个蛋白表达产物是分泌型蛋白。结论:肥胖及2型糖尿病患者肝组织与正常肝组织比较基因表达均发生明显变化,其基因表达变化数目随疾病的严重性增加而增多,而且2型糖尿病的控制情况与肝组织基因表达变化有密切关系。肥胖与2型糖尿病相关的差异表达基因中表达分泌型蛋白的可进一步用于研发监测疾病发生发展的候选靶分子。     

细胞凋亡和代谢基因在转移倾向结肠癌中的研究

目的:应用基因微阵列技术初步筛选与不同转移倾向结肠癌相关的细胞凋亡和代谢相关基因,研究转移相关基因功能.方法:取结肠癌肝转移和无转移结肠癌组织,采用人全基因组表达谱芯片获得两组织的基因表达谱,分析比较两者之间细胞凋亡和代谢基因的差异表达情况;利用基因数据库检索结肠癌相关基因,分析基因功能.结果:应用含有16450个克隆(其中3869个未知)的cDNA微阵列分析发现,细胞凋亡或肿瘤相关基因中,2倍以上(Ratio值小于0.5或大于2.0)差异基因共216个,上调基因85个,下调基因129个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共32个,上调基因10个,下调基因22个.在细胞代谢相关基因中,2倍以上(Ratio值小于0.5或大于2.O)差异基因共205个,上调基因86个,下调基因119个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共15个,上调基因10个,下调基因5个.利用基因数据库检索分析发现5个基因与结肠癌转移关系密切.结论:结肠癌的发生和转移是多基因参与的,本实验应用基因微阵列技术发现细胞凋亡和代谢相关基因中发现5个基因与结肠癌转移关系密切.     

利用GenMAPP筛查鼻咽癌差异表达基因

利用GenMAPP软件对鼻咽癌和正常鼻咽上皮基因微阵列表达谱结果进行分析,筛查鼻咽癌差异表达基因. 结果显示：在17 000个基因中,与正常鼻咽上皮相比,在鼻咽癌中发生2倍以上差异表达的基因共有339个,其中有160个基因在鼻咽癌中表达上调,179个表达下调. 这些基因分别与细胞增殖、基因转录、凋亡、信号转导、DNA损伤修复、肿瘤分化和浸润转移及细胞周期调节等相关. 鼻咽癌的发生发展存在多基因表达调控的改变,对其差异表达基因的研究有助于阐明鼻咽癌发生发展机制.     

db/db小鼠糖尿病肾病相关基因的分析和克隆

用GM-U74A基因芯片分别检测了正常对照组（db/m小鼠）、糖尿病肾病组（db/db小鼠）、大黄酸治疗组（大黄酸150 mg/kg治疗12周）肾脏基因表达谱.发现在12 437个基因（包括表达序列标签）中,与正常对照组相比,糖尿病肾病组有1 085个基因表达下调,37个基因表达上调,其中变化幅度大于2倍,表达下调的有166个和表达上调的有29个.与糖尿病肾病组相比,大黄酸治疗组有384个基因表达下调,155个表达上调,其中变化幅度大于2倍,表达下调的有47个和表达上调的有30个.在此基础上,对其中的一个差异表达的表达序列标签(EST)进行了详细的生物信息学分析,发现它是一个未知功能基因——“REKEN cDNA 0610006H10”基因的一部分.在用RT-PCR进一步验证了其与糖尿病肾病的相关性后,对“REKEN cDNA 0610006H10”基因进行了克隆.     

乙醇胁迫抑制毕赤酵母表达外源蛋白的转录组学分析

在5 L发酵罐中进行毕赤酵母发酵表达猪?干扰素的实验,发现甘油培养末期乙醇的积累会抑制外源蛋白的表达。从转录组学角度系统分析不同浓度乙醇胁迫条件下,毕赤酵母甘油培养期和甲醇诱导期细胞的生理状态变化。研究结果表明,在甘油培养期,乙醇胁迫使得毕赤酵母细胞中的545个基因发生了显著差异表达(265个基因表达上调,280个基因表达下调),这些差异表达基因的功能主要涉及蛋白质合成、能量代谢、细胞周期和过氧化物酶代谢。乙醇胁迫增加了蛋白质错误折叠的情况,降低了核糖体和线粒体的结构完整性,使得甘油培养末期无法得到大量具有健全功能的酵母细胞。在甲醇诱导期,与甲醇代谢、蛋白质加工合成、氨基酸代谢等途径相关的294个基因发生了显著差异表达(171个基因表达上调,123个基因表达下调),导致内质网胁迫不能被及时解除,破坏了细胞内的氨基酸正常代谢。     

siRNA介导的PRR11表达抑制导致肺癌细胞系基因表达谱变化的分析

Proline rich 11(PRR11)是本课题组鉴定的一个新的肿瘤相关基因。为研究PRR11介导肺癌发生发展相关的分子机制,本研究分析了PRR11表达被抑制后人肺癌细胞系H1299的全基因组基因表达谱的变化。首先,采用siRNA抑制H1299细胞中PRR11的表达,提取总RNA,采用基因芯片分析全基因组基因表达谱的变化。然后,对呈现差异表达的基因进行GO和Pathway富集分析,并对部分重要的候选基因进行定量RT-PCR验证。基因芯片结果表明,采用siRNA有效抑制H1299细胞中PRR11表达后,共有550个基因的mRNA水平出现明显变化,其中139个基因表达上调,411个基因表达下调。生物信息学分析结果表明,上述差异表达的基因显著富集于细胞周期和MAPK通路。定量RT-PCR验证分析结果表明,PRR11表达抑制后确实可导致多个与细胞周期和肿瘤发生发展密切相关的基因(包括DHRS2、EPB41L3、CCNA1、MAP4K4、RRM1、NFIB)呈现显著的表达变化。这些结果提示,PRR11可能通过上述通路和/或基因的表达变化参与肺癌的发生发展过程。     

茶树种子发育过程的转录组分析

该研究以‘铁观音’茶树品种的种子为试验材料,采用转录组测序技术分析种子发育的3个时期(幼果期、膨大期、成熟期)的表达差异,探究茶树种子油脂代谢的分子机制。结果表明:(1)经转录组测序、组装后共获得30 940 581个clean reads,经数据合并拼接最终得到36 951条非冗余Unigene序列,其中28 476个Unigene可得到功能注释;在转录本中能够被注释到GO分类的Unigene有11 201条(30.3%),KEGG分析发现共有17 172个基因参与了127个代谢通路。(2)经KEGG通路筛选出14条与脂肪酸代谢相关的通路,且随着茶籽的发育,大部分脂肪酸调控途径相关基因呈下调趋势,其中上调基因数最多的有α-亚麻酸代谢途径和脂肪酸降解途径(有17个基因表达量上调),下调基因数最多的是甘油磷脂代谢途径(有58个基因表达量下调);在茶籽发育幼果期α-亚麻酸代谢途径中表达量上调的基因数超过表达量下调的基因数。(3)研究发现茶籽脂肪酸合成相关的基因涉及14个脂类调控途径,共409条差异基因;随着茶树种子发育到成熟期,上调的差异表达基因数量在减少,下调的差异表达基因数量增加,其中α-亚麻酸途径中的基因PLA2G16、DAD1、pldA、FabF、FabI表达量上调显著,随后表达量下调。(4)qRT-PCR检测结果表明,7个茶树FAD和1个ACP差异表达基因的水平与转录组测序结果基本一致;随着茶籽的发育,基因CsFAD7和Δ6-CsFAD从幼果期、果实膨大期至果实成熟期都为差异下调表达,CsFAD2、CsFAD6和Δ7-CsFAD为差异上调表达,CsFAD8、Δ8-CsFAD和CsACP在幼果期至果实膨大期差异上调表达,在果实膨大期至果实成熟期差异下调表达。     

基于TCGA数据库分析甲状腺癌基因表达谱

为分析甲状腺癌基因表达谱,筛选疾病相关的基因标志物。基于肿瘤基因组图谱(TCGA)数据库中的甲状腺癌基因表达数据,运用R/Bioconductor统计平台进行数据处理与统计学分析。分别应用edgeR算法和limma算法选取肿瘤组织与对照组间倍数改变 > 2,P< 0.05的基因作为差异基因;进一步运用Medcalc统计软件进行受试者工作特征曲线(ROC)分析,鉴定出有诊断标志物潜在应用价值的基因标志物。通过两种运算方法筛选出甲状腺癌组织中存在着1 945个差异基因(上调基因1 033个,下调基因912个);根据差异倍数进一步鉴定出11个基因在肿瘤组织中表达上调,且对鉴别肿瘤组与对照组有较好的应用价值。本研究分析了TCGA中的甲状腺癌表达谱数据,鉴定出了与疾病诊断显著相关的差异表达基因,能够为探索疾病发生发展机制及寻找新型分子标志物提供依据。     

microRNA在胰腺癌中的研究进展

胰腺癌是预后很差的恶性肿瘤,其分子机制的研究是治愈胰腺癌的希望.microRNA(miRNA)是一类小分子非编码RNA,通过降解或抑制靶基因mRNA的翻译调节靶基因的功能.近年来,研究发现miRNA在胰腺癌中异常表达,有上调,也有下调.虽然很多miRNA异常表达的机制尚不清楚,但启动子CpG甲基化与在胰腺癌中某些miRNA的下调有关.研究发现miRNA表达谱可用于胰腺癌和单个miRNA表达如miR-21、miR-34、miR-10a、miR-155、miR-196a及miR-200和let-7家族成员等可作为肿瘤标志物用于胰腺癌与正常胰腺、慢性胰腺炎、胰腺内分泌肿瘤等的诊断和鉴别诊断,预测胰腺癌的预后等.研究发现miRNA在胰腺癌中上调或下调参与胰腺癌的增殖、凋亡、侵袭、转移以及对化疗药物的耐药.研究发现miR-34、miR-200c等与胰腺癌干细胞的自我更新有关.这些miRNA研究将为胰腺癌的早期诊断、分子靶向治疗打下坚实的基础.     

Extensive up-regulation of gene expression in cancer: the normalised use of microarray data

Based on the assumption that only a few genes are differentially expressed in a disease and have balanced upward and downward expression level changes, researchers usually normalise microarray data by forcing all of the arrays to have the same probe intensity distributions to remove technical variations in the data. However, accumulated evidence suggests that gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalisation assumption. Here, we show that the medians of the original probe intensities increase in most of the ten cancer types analyzed in this paper, indicating that genes may be widely up-regulated in many cancer types. Thus, at least for cancer study, normalising all arrays to have the same distribution of probe intensities regardless of the state (diseased vs. normal) tends to falsely produce many down-regulated differentially expressed (DE) genes while missing many truly up-regulated DE genes. We also show that the DE genes solely detected in the non-normalised data for cancers are highly reproducible across different datasets for the same cancers, indicating that effective biological signals naturally exist in the non-normalised data. Because the powers of current statistical analyses using the non-normalised data tend to be low, we suggest selecting DE genes in both normalised and non-normalised data and then filter out the false DE genes extracted from the normalised data that show opposite deregulation directions in the non-normalised data.     

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.     

Comparative expression analysis of four breast cancer subtypes versus matched normal tissue from the same patients

Gene expression studies have been widely used in an effort to identify signatures that can predict clinical progression of cancer. In this study we focused instead on identifying gene expression differences between breast tumors and adjacent normal tissue, and between different subtypes of tumor classified by clinical marker status. We have collected a set of 20 breast cancer tissues, matched with the adjacent pathologically normal tissue from the same patient. The cancer samples representing each subtype of breast cancer identified by estrogen receptor ER(+/-) and Her2(+/-) status and divided into four subgroups (ER+/Her2+, ER+/Her2-, ER-/Her2+, and ER-/Her2-) were hybridized on Affymetrix HG-133 Plus 2.0 microarrays. By comparing cancer samples with their matched normal controls we have identified 3537 overall differentially expressed genes using data analysis methods from Bioconductor. When we looked at the genes in common of the four subgroups, we found 151 regulated genes, some of them encoding known targets for breast cancer treatment. Unique genes in the four subgroups instead suggested gene regulation dependent on the ER/Her2 markers selection. In conclusion, the results indicate that microarray studies using robust analysis of matched tumor and normal samples from the same patients can be used to identify genes differentially expressed in breast cancer tumor subtypes even when small numbers of samples are considered and can further elucidate molecular features of breast cancer.     

Combining bioinformatics techniques to explore the molecular mechanisms involved in pancreatic cancer metastasis and prognosis

This article aims to explore the underlying molecular mechanisms and prognosis‐related genes in pancreatic cancer metastasis. Pancreatic cancer metastasis‐related gene chip data were downloaded from GENE EXPRESSION OMNIBUS(GEO)database. Differentially expressed genes were screened after R‐package pre‐treatment. Functional annotations and related signalling pathways were analysed using DAVID software. GEPIA (Gene Expression Profiling Interactive Analysis) was used to perform prognostic analysis, and differential genes associated with prognosis were screened and validated using data from GEO. We screened 40 healthy patients, 40 primary pancreatic cancer and 40 metastatic pancreatic cancer patients, collected serum, designed primers and used qPCR to test the expression of prognosis‐related genes in each group. 109 differentially expressed genes related with pancreatic cancer metastasis were screened, of which 49 were up‐regulated and 60 were down‐regulated. Functional annotation and pathway analysis revealed differentially expressed genes were mainly concentrated in protein activation cascade, extracellular matrix construction, decomposition, etc In the biological process, it is mainly involved in signalling pathways such as PPAR, PI3K‐Akt and ECM receptor interaction. Prognostic analysis showed the expression levels of four genes were significantly correlated with the overall survival time of patients with pancreatic cancer, namely SCG5, CRYBA2, CPE and CHGB. qPCR experiments showed the expression of these four genes was decreased in both the primary pancreatic cancer group and the metastatic pancreatic cancer group, and the latter was more significantly reduced. Pancreatic cancer metastasis is closely related to the activation of PPAR pathway, PI3K‐Akt pathway and ECM receptor interaction. SCG5, CRYBA2, CPE and CHGB genes are associated with the prognosis of pancreatic cancer, and their low expression suggests a poor prognosis.