Functional modules with disease discrimination abilities for various cancers

Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers. However, the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases, which would introduce low reproducibility of gene lists selected by statistical methods. Here, by analyzing seven cancer datasets, we showed that, in each cancer, a wide range of functional modules have altered gene expressions and thus have high disease classification abilities. The results also showed that seven modules are shared across diverse cancers, suggesting hints about the common mechanisms of cancers. Therefore, instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments, we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.     

Characterisation and correction of signal fluctuations in successive acquisitions of microarray images

Background  

In cancer studies, it is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. An important goal of such experiments is to identify a subset of genes that can potentially serve as predictive markers for cancer development and progression. Analyses of individual experiments may lead to unreliable gene selection results because of the small sample sizes. Meta analysis can be used to pool multiple experiments, increase statistical power, and achieve more reliable gene selection. The meta analysis of cancer microarray data is challenging because of the high dimensionality of gene expressions and the differences in experimental settings amongst different experiments.     

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

Background  

In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.     

Assessing gene significance from cDNA microarray expression data via mixed models.

The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.     

Extensive sequence variation in the 3′ untranslated region of the KRAS gene in lung and ovarian cancer cases

While cancer is a serious health issue, there are very few genetic biomarkers that predict predisposition, prognosis, diagnosis, and treatment response. Recently, sequence variations that disrupt microRNA (miRNA)-mediated regulation of genes have been shown to be associated with many human diseases, including cancer. In an early example, a variant at one particular single nucleotide polymorphism (SNP) in a let-7 miRNA complementary site in the 3′ untranslated region (3′ UTR) of the KRAS gene was associated with risk and outcome of various cancers. The KRAS oncogene is an important regulator of cellular proliferation, and is frequently mutated in cancers. To discover additional sequence variants in the 3′ UTR of KRAS with the potential as genetic biomarkers, we resequenced the complete region of the 3′ UTR of KRAS in multiple non-small cell lung cancer and epithelial ovarian cancer cases either by Sanger sequencing or capture enrichment followed by high-throughput sequencing. Here we report a comprehensive list of sequence variations identified in cases, with some potentially dysregulating expression of KRAS by altering putative miRNA complementary sites. Notably, rs712, rs9266, and one novel variant may have a functional role in regulation of KRAS by disrupting complementary sites of various miRNAs, including let-7 and miR-181.     

Estimating effect sizes of differentially expressed genes for power and sample-size assessments in microarray experiments

Summary In microarray screening for differentially expressed genes using multiple testing, assessment of power or sample size is of particular importance to ensure that few relevant genes are removed from further consideration prematurely. In this assessment, adequate estimation of the effect sizes of differentially expressed genes is crucial because of its substantial impact on power and sample‐size estimates. However, conventional methods using top genes with largest observed effect sizes would be subject to overestimation due to random variation. In this article, we propose a simple estimation method based on hierarchical mixture models with a nonparametric prior distribution to accommodate random variation and possible large diversity of effect sizes across differential genes, separated from nuisance, nondifferential genes. Based on empirical Bayes estimates of effect sizes, the power and false discovery rate (FDR) can be estimated to monitor them simultaneously in gene screening. We also propose a power index that concerns selection of top genes with largest effect sizes, called partial power. This new power index could provide a practical compromise for the difficulty in achieving high levels of usual overall power as confronted in many microarray experiments. Applications to two real datasets from cancer clinical studies are provided.     

Identification of gene expression signature modulated by nicotinamide in a mouse bladder cancer model

Background

Urinary bladder cancer is often a result of exposure to chemical carcinogens such as cigarette smoking. Because of histological similarity, chemically-induced rodent cancer model was largely used for human bladder cancer studies. Previous investigations have suggested that nicotinamide, water-soluble vitamin B3, may play a key role in cancer prevention through its activities in cellular repair. However, to date, evidence towards identifying the genetic alterations of nicotinamide in cancer prevention has not been provided. Here, we search for the molecular signatures of cancer prevention by nicotinamide using a N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN)-induced urinary bladder cancer model in mice.

Methodology/Principal Findings

Via microarray gene expression profiling of 20 mice and 233 human bladder samples, we performed various statistical analyses and immunohistochemical staining for validation. The expression patterns of 893 genes associated with nicotinamide activity in cancer prevention were identified by microarray data analysis. Gene network analyses of these 893 genes revealed that the Myc and its associated genes may be the most important regulator of bladder cancer prevention, and the gene expression signature correlated well with protein expression data. Comparison of gene expression between human and mouse revealed that BBN-induced mouse bladder cancers exhibited gene expression profiles that were more similar to those of invasive human bladder cancers than to those of non-invasive human bladder cancers.

Conclusions/Significance

This study demonstrates that nicotinamide plays an important role as a chemo-preventive and therapeutic agent in bladder cancer through the regulation of the Myc oncogenic signature. Nicotinamide may represent a promising therapeutic modality in patients with muscle-invasive bladder cancer.     

High-throughput method for detecting DNA methylation

Aberrant DNA methylation of CpG site is among the earliest and most frequent alterations in cancer. Detection of promoter hypermethylation of cancer-related gene may be useful for cancer diagnosis or the detection of recurrence. However, most of the studies have focused on a single gene only and gave little information about the concurrent methylation status of multiple genes. In this study, we attempted to develop a microarray method coupled with linker-PCR for detecting methylation status of multiple genes in the tumor tissue. A series of synthesized oligonucleotides were synthesised and purified to completely match with 16 investigated targets. Then they were immobilized on the aldehyde-coated glass slide to fabricate a DNA microarray for detecting methylation status of these genes. The results indicated that these genes were all methylated in the positive control. However, no methylated was found in these genes for the negative control. Only p16 and p15 genes were methylated in investigated genes for the gastric tumor tissue, whereas others were not methylated. The above results were validated by bisulfite DNA sequencing. Our experiments successfully demonstrated that the DNA microarray could be applied as a high-throughput tool to determine methylation status of the investigated genes.     

Meta-analysis of gene expression data: a predictor-based approach

MOTIVATION: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately. RESULTS: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A microarray study of altered gene expression in colorectal cancer cells after treatment with immunomodulatory drugs: differences in action in vivo and in vitro

Thalidomide and lenalidomide are FDA approved for the treatment of multiple myeloma, and along with pomalidomide are being investigated in a variety of other cancers. Although these agents display immunomodulatory, anti-angiogenic and anti-apoptotic effects, little is known about the primary mode of therapeutic action in patients with cancer. This paper describes a microarray study of the in vitro and in vivo effects of these drugs, and contrasts the difference in gene profiles achieved in the two models. In the current study, Agilent whole mouse genome oligonucleotide microarrays (44 K) were used to examine alterations in gene expression of colorectal cancer cells after treatment. Venn analysis revealed a divergence of gene signature for pomalidomide and lenalidomide, which although similar in vitro, different in vivo. Several clusters of genes involved in various cellular processes such as immune response, cell signalling and cell adhesion were altered by treatment, and common to the three drugs. Notably, the expressions of linked genes within the Notch/Wnt signalling pathway, including kremen2 and dtx4, highlighted a possible novel mechanistic pathway for these drugs. This study also showed that gene signatures were not greatly divergent in the models, and recapitulated the complex nature of these drugs. Overall, these microarray studies highlighted the diversity of this class of drug, which have effects ranging from cell signalling to translation initiation.     

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

Background

Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.

Results

We analyzed all human microarray studies from the Gene Expression Omnibus, and calculated the observed frequency of differential expression, which we called differential expression ratio, for every human gene. Analysis conducted in a comprehensive list of curated disease genes revealed a positive association between differential expression ratio values and the likelihood of harboring disease-associated variants. By considering highly differentially expressed genes, we were able to rediscover disease genes with 79% specificity and 37% sensitivity. We successfully distinguished true disease genes from false positives in multiple GWASs for multiple diseases. We then derived a list of functionally interpolating SNPs (fitSNPs) to analyze the top seven loci of Wellcome Trust Case Control Consortium type 1 diabetes mellitus GWASs, rediscovered all type 1 diabetes mellitus genes, and predicted a novel gene (KIAA1109) for an unexplained locus 4q27. We suggest that fitSNPs would work equally well for both Mendelian and complex diseases (being more effective for cancer) and proposed candidate genes to sequence for their association with 597 syndromes with unknown molecular basis.

Conclusions

Our study demonstrates that highly differentially expressed genes are more likely to harbor disease-associated DNA variants. FitSNPs can serve as an effective tool to systematically prioritize candidate SNPs from GWASs.     

List mania: interpreting microarray results with the L2L server

A new server for interpreting microarray results, list to list(L2L), is described. This tool offers a unique approach to understandthe meaning of microarray results, based on comparing them topreviously identified lists of differentially expressed genes.The usefulness of the server is demonstrated by studying differentialexpression in primary tumours versus metastases in colon cancer.     

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper.     

A permutation-based multiple testing method for time-course microarray experiments

Background  

Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.