用基因表达谱芯片研究人正常肝和肝细胞癌中差异表达的基因

以基因表达谱芯片对人正常肝及肝癌组织基因表达的差异性进行了研究比较。奖４０９６条人ｃＤＮＡ用点样仪点在特制玻片上制备成表达谱芯片;利用肝和肝癌组织的ｍＲＮＡ通过逆转录方法,将Ｃｙ３和Ｃｙ５２种荧光分别标记到两种组织的ｃＤＮＡ上,制备成ｃＤＮＡ探针,并与表达谱芯片进行杂交及扫描,重复４次实验,通过计算机数据处理判定基因是否在上述２种组织中有表达差异,筛选出差异表达的基因共９０３条。基因芯片技术可同时     

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.     

Identification of Novel Genes Differentially Expressed in PMA-induced HL-60 Cells Using cDNA Microarrays

Identification of normal growth and differentiation-inducing proteins and their interaction in normal development have made it possible to elucidate the molecular basis of normal development and the mechanisms uncoupling growth and differentiation during tumor development. The development of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. cDNA microarrays provide a powerful tool for studying these phenomena. In the present study, a high-density microarray of human cDNA elements was used to search for differences in gene expression associated with differentiation of human promyelic leukemia HL-60 cells. Microarrays containing 3,063 human cDNAs were printed on glass slides with high-speed robotics. These DNA chips were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. The identification of known and novel phorbol ester-regulated genes in hematopoietic progenitor cells demonstrates the sensitivity of the assay.     

Identification of Novel Genes Differentially Expressed in PMA-induced HL-60 Cells Using cDNA Microarrays

Identification of normal growth and differentiation-inducing proteins and their interaction in normal development have made it possible to elucidate the molecular basis of normal development and the mechanisms uncoupling growth and differentiation during tumor development. The development of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. cDNA microarrays provide a powerful tool for studying these phenomena. In the present study, a high-density microarray of human cDNA elements was used to search for differences in gene expression associated with differentiation of human promyelic leukemia HL-60 cells. Microarrays containing 3,063 human cDNAs were printed on glass slides with high-speed robotics. These DNA 'chips' were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. The identification of known and novel phorbol ester-regulated genes in hematopoietic progenitor cells demonstrates the sensitivity of the assay.     

差异表达基因分离技术的研究进展

分离并克隆差异表达基因是生命科学的研究热点.近年来,以差示筛选、扣除杂交等基本方法为基础,先后出现了抑制差减杂交,微阵列技术等多种分析差异表达基因的技术, 使差异表达基因分离方法不断完善.对这些方法的优缺点、发展趋势及应用前景进行了简要综述.     

鼠精子发生时期差异表达基因的克隆(英文)

为了分离鼠精子发生时期表达的基因,本文采用mRNA差异显示法,以鼠的粗线期卵母细胞为对照,检测了出生后60天和16天鼠的睾丸。得到12个有差异的片段(Fig.1&Table 1)。克隆测序结果表明,其中5个与已知基因非常吻合,另外6个与一些未知功能的cDNA、ESTs有较高的同源性,只有1个与已知序列没有同源性。Northern杂交分析显示sp1和sp8主要在成年鼠睾丸表达(Fig.4B)。采用5RACE对sp1的cDNA进行了全长分析,该基因编码一个推测是高度磷酸化蛋白的541个氨基酸(Fig.2),其中包括一个核定位信号,无论在核苷酸水平上,还是在氨基酸水平上均没有明显的同源性,仅在2个蛋白区有少量同源氨基酸(Fig.3)。该基因在20-60天龄鼠的睾丸均有表达,并且具有很高的组织特异性只在睾丸里表达(Fig.4A)。因而,这个基因有可能参与减数分裂及其以后的整个过程。可以认为这是一个新基因。我们把它命名为peat (predominantly expressed in adult testis)。     

Using Human Microarrays to Identify Differentially Expressed Genes Associated with Increased Steroidogenesis in Boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

A Robust Statistical Method for Detecting Differentially Expressed Genes

DNA microarray technology allows researchers to monitor the expressions of thousands of genes under different conditions, and to measure the levels of thousands of different DNA molecules at a given point in the life of an organism, tissue or cell. A wide variety of different diseases that are characterised by unregulated gene expression, DNA replication, cell division and cell death, can be detected early using microarrays. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The detection of differential gene expression under two different conditions is very important in biological studies, and allows us to identify experimental variables that affect different biological processes. Most of the tests available in the literature are based on the assumption of normal distribution. However, the assumption of normality may not be true in real-life data, particularly with respect to microarray data.A test is proposed for the identification of differentially expressed genes in replicated microarray experiments conducted under two different conditions. The proposed test does not assume the distribution of the parent population; thus, the proposed test is strictly nonparametric in nature. We calculate the p-value and the asymptotic power function of the proposed test statistic. The proposed test statistic is compared with some of its competitors under normal, gamma and exponential population setup using the Monte Carlo simulation technique. The application of the proposed test statistic is presented using microarray data. The proposed test is robust and highly efficient when populations are non-normal.     

Three Dynamin-Encoding Genes Are Differentially Expressed in Developing Rat Brain

Abstract: Dynamin proteins are members of a recently described family of GTPases involved in receptor-mediated processes. To date, three different dynamin-encoding genes have been identified in mammalian tissues. Dynamin I is expressed only in neurons, whereas dynamin II is ubiquitously expressed. A third isoform, dynamin III, was originally isolated from a rat testis cDNA library and shown to be testis-specific. However, here we report the cloning and characterization of dynamin III from brain and lung, demonstrating a more extended pattern of expression for this isoform. In addition, we have investigated the temporal pattern of expression of these three genes during brain development. We find that both dynamin I and dynamin III mRNA levels are up-regulated during embryogenesis, whereas dynamin II mRNA levels remain unchanged. From these results, we conclude that dynamin III is not a testis-specific isoform and, furthermore, that rat brain expresses three different dynamin-encoding genes that are differentially regulated during development. Therefore, this large isoform diversity of dynamin proteins in brain predicts a significant complexity in the understanding of dynamin-based processes in this tissue.     

Function and Chromosome Location of Differentially Expressed Genes in Gastric Cancer

Using Affymetrix U133A oligonucleotide microarrays, screening was done for genes that were differentially expressed in gastric cancer (T) and normal gastric mucosa (C), and their chromosome location was characterized by bioinformatics. A total of 270 genes were found to have a difference in expression levels of more than eight times. Of them 157 were up-regulated (Signal Log Ratio [SLR]≥3), and 113 were down-regulated (SLR≤-3). Except for, four genes with unknown localization, a vast majority of the genes were sporadically distributed over every chromosome. However, chromosome 1 contained the most differentially expressed genes (26 genes, or 9.8%), followed by chromosomes 11 and 19 (both 24 genes, or 9.1%). These genes were also more likely to be on the short-arm of the chromosome (q), which had 173 (65%). When these genes were classified according to their functions, it was found that most (67 genes, 24.8%) belonged to the enzymes and their regulators groups. The next group was the signal transduction genes group (43 genes, 15.9%). The rest of the top three groups were nucleic acid binding genes (17, 6.3%), transporter genes (15, 5.5%), and protein binding genes (12, 4.4%). These made up 56.9% of all the differentially expressed genes. There were also 50 genes of unknown function (18.5%). Therefore it was concluded that differentially expressed genes in gastric cancer seemed to be sporadically distributed across the genome, but most were found on chromosomes 1, 11 and 19. The five groups associated genes abnormality were important genes for further study on gastric cancer.     

肝细胞癌相关差异基因的生物信息学及预后分析

肝细胞癌是全球癌症相关死亡的主要原因,目前对肝细胞癌的发病机制研究尚不完善,探索肝细胞癌发生、发展相关的分子标志物及其预后具有重要意义。从GEO数据库获得肝细胞癌组织和非癌组织的基因表达阵列数据GSE84402,利用GEO2R筛选差异表达基因;采用DAVID数据库对差异基因进行GO富集分析和KEGG通路分析;通过STRING数据库和Cytoscape软件构建差异表达基因对应的蛋白质相互作用网络,并从网络中筛选出核心基因(hub genes);结合KM plotter数据库的临床信息对hub genes进行预后分析。结果显示:共得到1 307个差异表达基因,其中上调基因741个,下调基因566个,这些差异表达基因主要涉及细胞分裂、细胞周期、DNA复制及物质代谢等生物学过程及生物通路。通过GO、KEGG及蛋白质相互作用网络筛选出BUB1、BUB1B、CCNA2、CCNB1、CCNB2、CDC20、CDK1、MAD2L1、PLK1等9个hub genes,进一步分析发现hub genes均与细胞周期的调控相关,表明细胞周期的调控失常在肝细胞癌的发生、发展过程中具有重要作用。生存分析显示9个hub genes在肝细胞癌患者中均为表达上调的基因,且与患者预后不良相关,这为寻找肝细胞癌患者预后相关生物标志物的研究提供了线索。     

胃癌差异表达基因在染色体上的定位及其功能分析

利用标准化的Affymetrix公司生产的U133A基因芯片检测胃癌（T）与切缘正常胃黏膜（C）基因表达谱差异,并利用生物信息学方法对检测结果进行差异基因在染色体定位和功能分析。结果表明：胃癌与正常胃黏膜比较差异8倍以上共有270个基因,其中表达上调[信号比的对数值（SLR）≥3]有157个,表达下调（SLR≤-3）有113个。从表达差异的基因在染色体定位分析,发现除4个基因未知其定位外,其余所有差异表达基因散在分布和各条染色体上,但以1号染色体为最多,有26个（占9．8％）,其次是11和19号染色体上分别有24个（各占9．1％）。而差异表达的基因发生在染色体短臂（q）上有173个（占65％）。从表达差异的基因功能分类看,属于酶和酶调控子基因最多（67个,24．8占％）,其次是信号传导基因（43个,占15．9％）,第3类是核酸结合基因（17个,占6．3％）,第4类是转运子基因（15个,占5．5％）,第5类是蛋白结合基因（12个,占4．4％）,还有功能未知的基因有50个,占18．5％。以上5大类共占基因总数56．9％。胃癌差异表达基因散在分布在各条染色体上,但以1、11、19号染色体差异表达基因居多。这5大类（酶和酶调控子、信号传导、核酸结合、转运子、蛋白结合）相关基因异常是今后研究胃癌的重要基因。     

cDNA芯片差异表达基因检测的非转换方法

cDNA微阵列数据中包含许多变异因素,用于检测差异表达基因和其它统计分析前,必须将这些“噪音”剔除。对数比法（背景校正、对数比转换和数据标准化）已经被广泛应用于cDNA微阵列数据分析中,然而这种方法却存在着一些亟待解决的缺陷。对此,该文提出一种非转换方法,它可免去对数比的转化过程,直接在背景校正后进行数据标准化,可以有效剔除实验“噪音”。研究结果表明：在检测差异表达基因的效率方面,非转换方法比常规的对数比法具有更好的稳健性和更高的检测功效,基因检出率和准确性大大提高。