微阵列数据揭示基因在癌样本中广泛表达

癌的发生与发展过程涉及大量基因的异常表达。在目前基因表达谱分析中采用的标准化方法通常假设在疾病中差异表达的基因的比例很小并且差异上、下调的比例大致相等。这个被研究者所广泛采用的标准化的前提假设尚未被充分地论证过。通过分析胰腺癌的两套表达谱数据,我们发现在胰腺癌样本中基因表达的中值显著高于正常样本,提示传统的标准化假设并不适用于胰腺癌表达谱数据。采用标准化数据会导致错误地判断大量的差异下调的基因并失查许多差异上调的基因。采用原始数据分析发现在胰腺癌中的基因表达有广泛上调的特征,为深入研究胰腺癌的发生和发展机制提供了新线索。     

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome

Nowadays, some researchers normalized DNA methylation arrays data in order to remove the technical artifacts introduced by experimental differences in sample preparation, array processing and other factors. However, other researchers analyzed DNA methylation arrays without performing data normalization considering that current normalizations for methylation data may distort real differences between normal and cancer samples because cancer genomes may be extensively subject to hypomethylation and the total amount of CpG methylation might differ substantially among samples. In this study, using eight datasets by Infinium HumanMethylation27 assay, we systemically analyzed the global distribution of DNA methylation changes in cancer compared to normal control and its effect on data normalization for selecting differentially methylated (DM) genes. We showed more differentially methylated (DM) genes could be found in the Quantile/Lowess-normalized data than in the non-normalized data. We found the DM genes additionally selected in the Quantile/Lowess-normalized data showed significantly consistent methylation states in another independent dataset for the same cancer, indicating these extra DM genes were effective biological signals related to the disease. These results suggested normalization can increase the power of detecting DM genes in the context of diagnostic markers which were usually characterized by relatively large effect sizes. Besides, we evaluated the reproducibility of DM discoveries for a particular cancer type, and we found most of the DM genes additionally detected in one dataset showed the same methylation directions in the other dataset for the same cancer type, indicating that these DM genes were effective biological signals in the other dataset. Furthermore, we showed that some DM genes detected from different studies for a particular cancer type were significantly reproducible at the functional level.     

The Influence of the Global Gene Expression Shift on Downstream Analyses

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.     

Ranking: a closer look on globalisation methods for normalisation of gene expression arrays

Data from gene expression arrays are influenced by many experimental parameters that lead to variations not simply accessible by standard quantification methods. To compare measurements from gene expression array experiments, quantitative data are commonly normalised using reference genes or global normalisation methods based on mean or median values. These methods are based on the assumption that (i) selected reference genes are expressed at a standard level in all experiments or (ii) that mean or median signal of expression will give a quantitative reference for each individual experiment. We introduce here a new ranking diagram, with which we can show how the different normalisation methods compare, and how they are influenced by variations in measurements (noise) that occur in every experiment. Furthermore, we show that an upper trimmed mean provides a simple and robust method for normalisation of larger sets of experiments by comparative analysis.     

Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer

In microarray-based case–control studies of a disease, people often attempt to identify a few diagnostic or prognostic markers amongst the most significant differentially expressed (DE) genes. However, the reproducibility of DE genes identified in different studies for a disease is typically very low. To tackle the problem, we could evaluate the reproducibility of DE genes across studies and define robust markers for disease diagnosis using disease-associated protein–protein interaction (PPI) subnetwork. Using datasets for four cancer types, we found that the most significant DE genes in cancer exhibit consistent up- or down-regulation in different datasets. For each cancer type, the 5 (or 10) most significant DE genes separately extracted from different datasets tend to be significantly coexpressed and closely connected in the PPI subnetwork, thereby indicating that they are highly reproducible at the PPI level. Consequently, we were able to build robust subnetwork-based classifiers for cancer diagnosis.     

ce‐Subpathway: Identification of ceRNA‐mediated subpathways via joint power of ceRNAs and pathway topologies

Competing endogenous RNAs (ceRNAs) represent a novel mechanism of gene regulation that may mediate key subpathway regions and contribute to the altered activities of pathways. However, the classical methods used to identify pathways fail to specifically consider ceRNAs within the pathways and key regions impacted by them. We proposed a powerful strategy named ce‐Subpathway for the identification of ceRNA‐mediated functional subpathways. It provided an effective level of pathway analysis via integrating ceRNAs, differentially expressed (DE) genes and their key regions within the given pathways. We respectively analysed one pulmonary arterial hypertension (PAH) and one myocardial infarction (MI) data sets and demonstrated that ce‐Subpathway could identify many subpathways whose corresponding entire pathways were ignored by those non‐ceRNA‐mediated pathway identification methods. And these pathways have been well reported to be associated with PAH/MI‐related cardiovascular diseases. Further evidence showed reliability of ceRNA interactions and robustness/reproducibility of the ce‐Subpathway strategy by several data sets of different cancers, including breast cancer, oesophageal cancer and colon cancer. Survival analysis was finally applied to illustrate the clinical application value of the ceRNA‐mediated functional subpathways using another data sets of pancreatic cancer. Comprehensive analyses have shown the power of a joint ceRNAs/DE genes and subpathway strategy based on their topologies.     

Summaries of Affymetrix GeneChip probe level data

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.     

Probing differentially expressed genes against a microarray database for in silico suppressor/enhancer and inhibitor/activator screens

High-density oligonucleotide arrays are widely used for analysis of gene expression on a genomic scale, but the generated data remain largely inaccessible for comparative analysis purposes. Similarity searches in databases with differentially expressed gene (DEG) lists may be used to assign potential functions to new genes and to identify potential chemical inhibitors/activators and genetic suppressors/enhancers. Although this is a very promising concept, it requires the compatibility and validity of the DEG lists to be significantly improved. Using Arabidopsis and human datasets, we have developed guidelines for the performance of similarity searches against databases that collect microarray data. We found that, in comparison with many other methods, a rank-product analysis achieves a higher degree of inter- and intra-laboratory consistency of DEG lists, and is advantageous for assessing similarities and differences between them. To support this concept, we developed a tool called MASTA (microarray overlap search tool and analysis), and re-analyzed over 600 Arabidopsis microarray expression datasets. This revealed that large-scale searches produce reliable intersections between DEG lists that prove to be useful for genetic analysis, thus aiding in the characterization of cellular and molecular mechanisms. We show that this approach can be used to discover unexpected connections and to illuminate unanticipated interactions between individual genes.     

Beyond comparing means: the usefulness of analyzing interindividual variation in gene expression for identifying genes associated with cancer development

Identifying genes associated with cancer development is typically accomplished by comparing mean expression values in normal and tumor tissues, which identifies differentially expressed (DE) genes. Interindividual variation (IV) in gene expression is indirectly included in DE gene identification because given the same absolute differences in means, genes with lower variance tend to have lower p-values. We explored the direct use of IV in gene expression to identify candidate genes associated with cancer development. We focused on prostate (PCa) and lung (LC) cancers and compared IV in the expression level of genes shown to be cancer related with that in all other genes in the human genome. Compared with all those other genes, cancer-related genes tended to have greater IV in normal tissues and a greater increase in IV during the transition from normal to tumorous tissue. Genes without significantly different mean expression values between tumor and normal tissues but with greater IV in tumor than in normal tissue (note: the DE-based approach completely ignores those genes) had stronger associations with clinically important features like Gleason score in PCa or tumor histology in LC than all other genes were. Our results suggest that analyzing IV in gene expression level is useful in identifying novel candidate genes associated with cancer development.     

Detection of differentially expressed gene sets in a partially paired microarray data set

Partially paired data sets often occur in microarray experiments (Kim et al., 2005; Liu, Liang and Jang, 2006). Discussions of testing with partially paired data are found in the literature (Lin and Stivers 1974; Ekbohm, 1976; Bhoj, 1978). Bhoj (1978) initially proposed a test statistic that uses a convex combination of paired and unpaired t statistics. Kim et al. (2005) later proposed the t3 statistic, which is a linear combination of paired and unpaired t statistics, and then used it to detect differentially expressed (DE) genes in colorectal cancer (CRC) cDNA microarray data. In this paper, we extend Kim et al.'s t3 statistic to the Hotelling's T2 type statistic Tp for detecting DE gene sets of size p. We employ Efron's empirical null principle to incorporate inter-gene correlation in the estimation of the false discovery rate. Then, the proposed Tp statistic is applied to Kim et al's CRC data to detect the DE gene sets of sizes p=2 and p=3. Our results show that for small p, particularly for p=2 and marginally for p=3, the proposed Tp statistic compliments the univariate procedure by detecting additional DE genes that were undetected in the univariate test procedure. We also conduct a simulation study to demonstrate that Efron's empirical null principle is robust to the departure from the normal assumption.     

Gene expression alterations in doxorubicin resistant MCF7 breast cancer cell line

Many molecular mechanisms contribute to the development of doxorubicin resistance and different cancers can express wide and diverse arrays of drug-resistance genes. The aim of this study was to identify the changes in gene expression associated with the development of doxorubicin resistance in MCF7 breast cancer cell line. The doxorubicin resistant MCF7 cell line was developed by stepwise selection of MCF7 cells and was tested using the MTT assay. The alterations in gene expression were examined using the real-time based PCR array. The findings showed an up-regulation of many phase I/II metabolizing genes, specifically, the CYP1A1 and the CYP1A2 that were up-regulated by 206- and 96-fold respectively. Drug efflux pump genes were also up-regulated profoundly. TOP2A was strongly down-regulated by 202-fold. Many other changes were observed in genes crucial for cell cycle, apoptosis and DNA repair. The findings of this project imply that the development of doxorubicin resistance is a multi-factorial process.     

结肠癌中核内miRNA的激活调控作用研究

为了探究增强子介导的核内miRNA在结肠癌发生中的作用,本研究筛选了结肠癌中的差异表达的miRNA数据、结肠的特异性增强子数据、结肠癌中差异表达基因数据,利用细胞核内miRNA靶向增强子预测算法,筛选miRNA调控的结肠特异性增强子;利用增强子靶基因预测数据,筛选核内miRNA调控的差异表达靶基因,并且构建核内miRNA-靶基因网络,并通过网络的分析和筛选获得结肠癌中关键的致病基因,同时对网络中的靶基因进行GO的功能注释。结果表明,我们所构建的核内miRNA-激活调控靶基因网络包含miRNA-靶基因关系对2 121个,259个节点,其中包含34个下调基因、183个上调的基因,7个下调的miRNA,35个上调的miRNA。而后我们分析了网络进行的节点度的整体分布情况,发现网络中大部分的节点的度都是小于10的,仅有少量miRNA结合和部分的差异表达基因节点的度大于10。核内miRNA主要通过激活调控了一些应激反应相关的功能和,同时,抑制调控了细胞周期、细胞凋亡、细胞死亡巨噬细胞代谢等相关功能,通过激活和抑制相关功能诱发结肠癌的发生。从核内miRNA的激活调控角度研究结肠癌的发病机制,是对原有细胞浆中miRNA抑制调控机制的补充,也为结肠癌的系统研究提供了新的视野。     

Carbohydrate Sequence of the Prostate Cancer-associated Antigen F77 Assigned by a Mucin O-Glycome Designer Array

Monoclonal antibody F77 was previously raised against human prostate cancer cells and has been shown to recognize a carbohydrate antigen, but the carbohydrate sequence of the antigen was elusive. Here, we make multifaceted approaches to characterize F77 antigen, including binding analyses with the glycolipid extract of the prostate cancer cell line PC3, microarrays with sequence-defined glycan probes, and designer arrays from the O-glycome of an antigen-positive mucin, in conjunction with mass spectrometry. Our results reveal F77 antigen to be expressed on blood group H on a 6-linked branch of a poly-N-acetyllactosamine backbone. We show that mAb F77 can also bind to blood group A and B analogs but with lower intensities. We propose that the close association of F77 antigen with prostate cancers is a consequence of increased blood group H expression together with up-regulated branching enzymes. This is in contrast to other epithelial cancers that have up-regulated branching enzymes but diminished expression of H antigen. With knowledge of the structure and prevalence of F77 antigen in prostate cancer, the way is open to explore rationally its application as a biomarker to detect F77-positive circulating prostate cancer-derived glycoproteins and tumor cells.     

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Background  

The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication.