Efficient molecular subtype classification of high‐grade serous ovarian cancer

High‐grade serous carcinomas (HGSCs) account for approximately 70% of all epithelial ovarian cancers diagnosed. Using microarray gene expression profiling, we previously identified four molecular subtypes of HGSC: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative), which correlate with patient survival and have distinct biological features. Here, we describe molecular classification of HGSC based on a limited number of genes to allow cost‐effective and high‐throughput subtype analysis. We determined a minimal signature for accurate classification, including 39 differentially expressed and nine control genes from microarray experiments. Taqman‐based (low‐density arrays and Fluidigm), fluorescent oligonucleotides (Nanostring), and targeted RNA sequencing (Illumina) assays were then compared for their ability to correctly classify fresh and formalin‐fixed, paraffin‐embedded samples. All platforms achieved > 90% classification accuracy with RNA from fresh frozen samples. The Illumina and Nanostring assays were superior with fixed material. We found that the C1, C2, and C4 molecular subtypes were largely consistent across multiple surgical deposits from individual chemo‐naive patients. In contrast, we observed substantial subtype heterogeneity in patients whose primary ovarian sample was classified as C5. The development of an efficient molecular classifier of HGSC should enable further biological characterization of molecular subtypes and the development of targeted clinical trials. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

Simultaneous sequencing of multiple polymerase chain reaction products and combined polymerase chain reaction with cycle sequencing in single reactions

DNA sequencing is considered the gold standard for nucleic acid identification and mutation detection. However, sequencing is labor intensive because it requires previous amplification and only a single sequence is analyzed at a time. We developed two novel strategies that substantially improve DNA sequencing. The first allows multiple polymerase chain reaction (PCR) products to be sequenced in a single sequencing reaction and analyzed simultaneously in a single lane or capillary. Simultaneous sequencing by this method, designated "SimulSeq," can provide either simultaneous single-direction sequencing of multiple genes or simultaneous forward and reverse sequencing from a single gene. In the second approach, designated "AmpliSeq," we demonstrate a technique combining PCR amplification and sequencing in a single reaction that is analyzed in a single lane or capillary. We demonstrate combined PCR with short bidirectional sequencing, and combined PCR with unidirectional sequencing. We anticipate that these methods will have utility in research and clinical settings where panels of mutations or large numbers of samples are be analyzed and/or when turnaround time is critical.     

Global analysis of differentially expressed genes in early gestational decidua and chorionic villi using a 9600 human cDNA microarray

The global gene expression profiles of the decidua and chorionic villi of early human pregnancies were analysed by using cDNA microarray technology. Decidual and villous placental tissues were obtained from first trimester abortus and mRNA was extracted for cDNA microarray analysis. The human cDNA microarray [9600 clones, including known regulatory genes and expressed sequence tags (EST)] with colorimetric detection was used to identify differentially expressed genes between early gestational decidua and villi. According to cDNA microarray analysis, we have identified 641 genes with highly expressed mRNA in both decidua and villi, 49 genes with higher expressions in decidua, and 75 genes with higher expression in chorionic villi. These differentially expressed genes were further grouped into categories by their putative functions, including: cell growth-related factors, hormones/cytokines, cell adhesion molecules, signal transduction molecules, apoptosis-related factors, cytoskeleton/extracellular matrix proteins, and EST. Immunohistochemical stainings of cathepsin L, leukaemia inhibitory factor-receptor, and proliferative cell nuclear antigen showed results consistent with the microarray data. Identification of the differentially expressed genes between decidua and villi by microarray provide a global profiling of the gene expression pattern. This work adds to our understanding of placentation by reporting the gene expression profiles during first trimester human pregnancies using cDNA microarray.     

基因表达系列分析技术研究进展

基因表达系列分析技术(SAGE)是一种新的基因表达分析技术,它可以大量获取全基因组范围基因表达的类别并量化分析。由于其克服了基因丰度的影响,SAGE在新基因的发现中具有独特的优点。同时,SAGE技术被成功应用于特异组织或细胞的转录组研究、mRNA群体间的全局化比较以及差异表达基因染色体分布的分析。文章主要述及了SAGE技术的原理、特点及其在应用上的最新进展。     

多实验平台下基因及异构体表达分析综述

转录组学研究近几年成为生命科学和医学领域的研究热点,基因表达水平测量则是转录组学研究的基础。差异基因表达分析对于了解基因功能具有重要作用,而差异异构体表达分析则能够反映选择性剪切变化的情况。当前大规模测量基因表达水平的实验平台主要包括基因芯片,以及基于高通量测序技术的RNA-Seq。首先介绍广泛使用的Affymetrix传统3'基因芯片、外显子芯片、较新的全转录组芯片,以及基于RNA-Seq技术的Illumina平台4个主流实验平台的技术原理;其次从基因表达水平计算和差异表达分析两方面介绍每个平台下一些主流数据分析方法和该研究设计的方法,分析每个平台下各数据分析方法的优劣,并进一步展示在标准数据集上一些代表性方法的对比结果。     

肾脏纤维化大鼠模型尿液中差异表达基因筛选及生物信息学分析

目的研究大鼠肾脏纤维化(RF)模型尿液中差异表达的基因,并对其进行生物信息学分析。方法将大鼠分为对照组和纤维化模型组,单侧输尿管结扎术建立大鼠肾脏纤维化模型,收集尿液。提取总RNA,构建测序文库并进行转录组测序。对差异表达的mRNA进行了GO分析和KEGG分析,并对miRNA的前体和lncRNA的家族进行预测和分类。结果肾脏纤维化模型组的尿液和对照组相比,得到813条表达上调转录本数据以及213条表达下调的转录本数据。结论利用转录组高通量测序结合相关生物信息学分析,可为肾脏纤维化诊断靶点提供新的可能。     

Gene expression profile differences in left and right liver lobes from mid-gestation fetal baboons: a cautionary tale

Interpretation of gene array data presents many potential pitfalls in adult tissues. Gene array techniques applied to fetal tissues present additional confounding pitfalls. The left lobe of the fetal liver is supplied with blood containing more oxygen than the right lobe. Since synthetic activity and cell function are oxygen dependent, we hypothesized major differences in mRNA expression between the fetal right and left liver lobes. Our aim was to demonstrate the need to evaluate RNA samples from both lobes. We performed whole genome expression profiling on left and right liver lobe RNA from six 90-day gestation baboon fetuses (term 180 days). Comparing right with left, we found 875 differentially expressed genes – 312 genes were up-regulated and 563 down-regulated. Pathways for damaged DNA binding, endonuclease activity, interleukin binding and receptor activity were up-regulated in right lobe; ontological pathways related to cell signalling, cell organization, cell biogenesis, development, intracellular transport, phospholipid metabolism, protein biosynthesis, protein localization, protein metabolism, translational regulation and vesicle mediated transport were down-regulated in right lobe. Molecular pathway analysis showed down-regulation of pathways related to heat shock protein binding, ion channel and transporter activities, oxygen binding and transporter activities, translation initiation and translation regulator activities. Genes involved in amino acid biosynthesis, lipid biosynthesis and oxygen transport were also differentially expressed. This is the first demonstration of RNA differences between the two lobes of the fetal liver. The data support the argument that a complete interpretation of gene expression in the developing liver requires data from both lobes.     

Computational selection of distinct class- and subclass-specific gene expression signatures

In this investigation we used statistical methods to select genes with expression profiles that partition classes and subclasses of biological samples. Gene expression data corresponding to liver samples from rats treated for 24 h with an enzyme inducer (phenobarbital) or a peroxisome proliferator (clofibrate, gemfibrozil or Wyeth 14,643) were subjected to a modified Z-score test to identify gene outliers and a binomial distribution to reduce the probability of detecting genes as differentially expressed by chance. Hierarchical clustering of 238 statistically valid differentially expressed genes partitioned class-specific gene expression signatures into groups that clustered samples exposed to the enzyme inducer or to peroxisome proliferators. Using analysis of variance (ANOVA) and linear discriminant analysis methods we identified single genes as well as coupled gene expression profiles that separated the phenobarbital from the peroxisome proliferator treated samples and discerned the fibrate (gemfibrozil and clofibrate) subclass of peroxisome proliferators. A comparison of genes ranked by ANOVA with genes assessed as significant by mixedlinear models analysis [J. Comput. Biol. 8 (2001) 625] or ranked by information gain revealed good congruence with the top 10 genes from each statistical method in the contrast between phenobarbital and peroxisome proliferators expression profiles. We propose building upon a classification regimen comprised of analysis of replicate data, outlier diagnostics and gene selection procedures to utilize cDNA microarray data to categorize subclasses of samples exposed to pharmacologic agents.     

Expression profiling of genes related to asthma exacerbations

Background Asthma is a chronic airway inflammatory disease; however, the molecular mechanisms that underlie asthma exacerbation are only partially understood.
Objective To identify gene expression signatures that reflect the acute exacerbation of asthma, we examined the differential expression of genes during asthma exacerbation and stable condition by using microarray analysis.
Methods The subjects were mite-sensitive asthmatic children and non-asthmatic control children. The children were divided into four groups (AE: asthma exacerbation, n =12; SA: stable asthma, n =11; IC: infected control, n =6; and NC: non-infected control, n =5). Total RNA was extracted from peripheral blood mononuclear cells and subjected to microarray analysis with Illumina Human Ref8 BeadChip arrays. Welch's t -test was performed to identify genes whose expression was altered during asthma exacerbation. Quantitative real-time RT-PCR was performed on samples collected from 43 asthmatic children and 11 control children to verify the microarray results.
Results The expression of 137/16 genes was significantly up/down-regulated during asthma exacerbation assessed by microarray analysis. Of the genes, 62 were also differentially expressed during upper respiratory infection. Many of the asthma exacerbation related genes were involved in defence responses and responses to external stimuli, but these associations disappeared after excluding the infection-related genes. Quantitative real-time RT-PCR confirmed that the genes related (S100A8 and GAS6) and unrelated to infections (CD200 and RBP7) were differentially expressed during asthma exacerbation ( P <0.01).
Conclusions Previously unidentified immune responses during asthma exacerbation may provide further clarification of the molecular mechanisms underlying asthma.     

阿尔茨海默病小鼠脑组织长链非编码RNA与信使RNA表达谱及调控网络的分析

目的 分析阿尔茨海默病（AD）小鼠脑组织长链非编码RNA（LncRNA）和信使RNA（mRNA）表达谱,构建竞争内源性RNA（ceRNA）调控网络,探讨差异表达LncRNA在AD发病机制中的潜在作用。方法 选取3只10月龄雄性APP/PS1转基因小鼠作为AD组,3只年龄及体质量相匹配的普通C57小鼠作为对照组。使用基因芯片技术检测2组小鼠脑组织LncRNA和mRNA的表达,筛选出差异表达的LncRNA和mRNA。对部分差异表达的LncRNA进行实时定量聚合酶链反应（qRT-PCR）。对差异表达的mRNA进行基因本体论（GO）和京都基因、基因组百科全书（KEGG）通路分析。随机挑选6个差异表达LncRNA构建ceRNA网络,进行AD的靶基因功能预测分析。结果 与对照组相比,AD组小鼠脑组织差异表达1.5倍以上的LncRNA有933个,其中上调222个,下调711个;差异表达1.5倍以上的mRNA有529个,其中上调189个,下调340个。qRT-PCR检测结果显示,AD组与对照组比较,7个差异表达的LncRNA上调或下调趋势与基因芯片结果一致,差异均有统计学意义（P值均<0.05）。GO和KEGG通路分析结果显示,差异表达基因主要参与氨基酸代谢、炎症反应和免疫反应。ceRNA调控网络靶基因的功能富集分析显示,LncRNA在胰岛素抵抗以及糖尿病并发症中的AGE-RAGE信号通路中显著富集。结论 AD小鼠脑组织LncRNA表达谱发生显著变化,由LncRNA Dgkb、Svip等构建的ceRNA调控网络有助于增进对AD发病分子机制的研究,差异表达的LncRNA或通路有可能成为潜在的治疗靶点。