Extracting information from cDNA arrays

Identification of crucial regulatory relationships between long non-coding RNAs and protein-coding genes in lung squamous cell carcinoma

PURPOSE

This study aimed to analyze the relationships of long non-coding RNAs (lncRNAs) and protein-coding genes in lung squamous cell carcinoma (LUSC).

METHODS

RNA-seq data of LUSC deposited in the TCGA database were used to identify differentially expressed protein-coding genes (DECGs) and differentially expressed lncRNA genes (DE-lncRNAs) between LUSC samples and normal samples. Functional enrichment analysis of DECGs was then performed. Subsequently, the target genes and regulators of DE-lncRNAs were predicted from the DECGs. Additionally, expression levels of target genes of DE-lncRNAs were validated by RT-qPCR after the silence of DE-lncRNAs.

RESULTS

In total, 5162 differentially expressed genes (DEGs) were screened from the LUSC samples, and there were seven upregulated lncRNA genes in the DEGs. The upregulated DECGs were enriched in GO terms like RNA binding and metabolic process. Meanwhile, the downregulated DECGs were enriched in GO terms like cell cycle. Furthermore, the lncRNAs PVT1 and TERC targeted multiple DECGs. PVT1 targeted genes related to cell cycle (e.g. POLA2, POLD1, MCM4, MCM5 and MCM6), and reduced expression of PVT1 decreased expression of the genes. TERC regulated several genes (e.g. NDUFAB1, NDUFA11 and NDUFB5), and reduced expression of TERC increased expression of the genes. Additionally, PVT1 was regulated by multiple transcription factors (TFs) identified from DECGs, such as HSF1; and TERC was modulated by TFs, such as PIR.

CONCLUSION

A set of regulatory relationships between PVT1 and its targets and regulators, as well as TERC and its targets and regulators, may play crucial roles in the progress of LUSC.

The curse of normalization

Despite its enormous promise to further our understanding of cellular processes involved in the regulation of gene expression, microarray technology generates data for which statistical pre-processing has become a necessity before any interpretation of data can begin. The process by which we distinguish (and remove) non-biological variation from biological variation is called normalization. With a multitude of experimental designs, techniques and technologies influencing the acquisition of data, numerous approaches to normalization have been proposed in the literature. The purpose of this short review is not to add to the many suggestions that have been made, but to discuss some of the difficulties we encounter when analysing microarray data.

Differences in gene expression profiles in dermal fibroblasts from control and patients with age-related macular degeneration elicited by oxidative injury

The pathogenesis of age-related macular degeneration (AMD) is still unknown but there is growing evidence that a combination of both oxidative injury and genetic factors may play a role. One particle hypothesis proposes that dysregulation of multiple genes in response to an oxidative injury could contribute to the development of AMD. While direct examination of ocular cells from AMD patients is difficult, AMD also appears to have a systemic component. Therefore, as is the case with other central nervous diseases, peripheral sites may also manifest any underlying genetic abnormalities. For the present study, biopsy-derived fibroblasts from 4 patients with the early form and 4 patients with the late form of AMD and 3 age-matched control patients were grown in culture and treated with a nonlethal dose of the oxidative stimulus menadione. Gene expression patterns were quantitatively and qualitatively examined using Human Genome U95A GeneChips (Affymetrix) and verified by real-time PCR analysis. In response to the oxidative injury 755 genes were found to be upregulated at least twofold in one of the patients groups. Cluster analysis of expression profiles detected six patterns of dysregulation initiated by oxidative injury specific for the disease groups (98 genes total). Clusters of genes dysregulated by the sublethal oxidative injury in either early and/or late AMD groups were further categorized by overrepresentation of GO "biological process" categories using Expression Analysis Systematic Explorer (EASE) software. This approach demonstrated that four major functional gene groups including inflammatory/innate immune response, transcriptional regulation, cell cycle, and proliferation were significantly overrepresented (Fisher test ranging from 0.0393 to 0.00018) in both AMD patients groups in response to the oxidative injury. Despite the small number of patients in the study, specific biological and statistical differences in gene expression profiles between control and AMD patients were identified but only in the presence of an environmental stimulus.

A simple framework to describe the regulation of gene expression in prokaryotes

Based on the bimolecular mass action law and the derived mass conservation laws, we propose a mathematical framework in order to describe the regulation of gene expression in prokaryotes. It is shown that the derived models have all the qualitative properties of the activation and inhibition regulatory mechanisms observed in experiments. The basic construction considers genes as templates for protein production, where regulation processes result from activators or repressors connecting to DNA binding sites. All the parameters in the models have a straightforward biological meaning. After describing the general properties of the basic mechanisms of positive and negative gene regulation, we apply this framework to the self-regulation of the trp operon and to the genetic switch involved in the regulation of the lac operon. One of the consequences of this approach is the existence of conserved quantities depending on the initial conditions that tune bifurcations of fixed points. This leads naturally to a simple explanation of threshold effects as observed in some experiments.

Broadly predicting specific gene functions with expression similarity and taxonomy similarity

Previous studies on computational gene functional prediction have not fully exploited the taxonomy structure of Gene Ontology (GO). They just select a few classes from GO into a set, and conduct classwise learning of these classes. The pre-selection of learning classes, often done according to the annotation sizes, limits the prediction breadth and depth. This way of pre-selecting learning classes ignores the taxonomy relations among classes, and so wastes the valuable functional knowledge encoded in the DAG structure of GO. This paper proposes GESTS, a novel gene functional prediction approach based on both gene expression similarity and GO taxonomy similarity, which circumvents the problem of arbitrary learning class pre-selection. GESTS is a semi-supervised approach that reasonably and efficiently incorporates the ontology-formed gene functional knowledge into automated functional analyses of local gene clustering. By integrating both expression similarity and taxonomy similarity into the learning process, GESTS achieves better prediction breadth, depth, and precision than previous studies on the fibroblast serum response dataset and the yeast expression dataset.

Towards precise classification of cancers based on robust gene functional expression profiles

BACKGROUND

Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.

RESULTS

Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.

CONCLUSION

This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Gene expression profiling: from microarrays to medicine

With the mapping of the human genome comes the ability to identify genes of interest in specific diseases and the pathways involved therein. Laboratory technology has evolved in parallel, providing us with the ability to assay thousands of these genes at once, a technique known as microarray analysis. The main #x003Fion that this type of technology raises is how we can apply this powerful technology to clinical medicine. Recently, advances in data analysis, as well as standardization of the technology, have allowed us to examine this #x003Fion, and indeed a few clinical trials currently being performed include microarrays as part of their protocol. In this review we outline the microarray technique and describe these types of studies in further detail.

Perspectives and limitations of gene expression profiling in rheumatology: new molecular strategies

The deciphering of the sequence of the human genome has raised the expectation of unravelling the specific role of each gene in physiology and pathology. High-throughput technologies for gene expression profiling provide the first practical basis for applying this information. In rheumatology, with its many diseases of unknown pathogenesis and puzzling inflammatory aspects, these advances appear to promise a significant advance towards the identification of leading mechanisms of pathology. Expression patterns reflect the complexity of the molecular processes and are expected to provide the molecular basis for specific diagnosis, therapeutic stratification, long-term monitoring and prognostic evaluation. Identification of the molecular networks will help in the discovery of appropriate drug targets, and permit focusing on the most effective and least toxic compounds. Current limitations in screening technologies, experimental strategies and bioinformatic interpretation will shortly be overcome by the rapid development in this field. However, gene expression profiling, by its nature, will not provide biochemical information on functional activities of proteins and might only in part reflect underlying genetic dysfunction. Genomic and proteomic technologies will therefore be complementary in their scientific and clinical application.

Quantitative oligonucleotide microarray fingerprinting of Salmonella enterica isolates

We report on a genome-independent microbial fingerprinting method using nucleic acid microarrays for microbial forensics and epidemiology applications and demonstrate that the microarray method provides high resolution differentiation between closely related microorganisms, using Salmonella enterica strains as the test case. In replicate trials we used a simple 192 probe nonamer array to construct a fingerprint library of 25 closely related Salmonella isolates. Controlling false discovery rate for multiple testing at alpha = 0.05, at least 295 of 300 pairs of S.enterica isolate fingerprints were found to be statistically distinct using a modified Hotelling T2 test. Although most pairs of Salmonella fingerprints are found to be distinct, forensic applications will also require a protocol for library construction and reliable microbial classification against a fingerprint library. We outline additional steps required to produce such a protocol.

Normalization methods for analysis of microarray gene-expression data

This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare two samples for dye effects and for treatment effects. The numbers of genes that reproducibly showed significant p-values for the unnormalized data and normalized data from different methods were evaluated for assessment of different normalization methods. The one-sample t-statistic of the ratio of red to green samples was used to test for dye effects using only control data. For treatment effects, in addition to the one-sample t-test of the ratio of the treated to control samples, the two-sample t-test for testing the difference between treated and control samples was also used to compare the two approaches. The method that combines a subset approach (median or lowess fit) for location adjustment with a global lowess fit for intensity adjustment appears to perform well.

Fingerprinting closely related xanthomonas pathovars with random nonamer oligonucleotide microarrays

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.

Variable selection and pattern recognition with gene expression data generated by the microarray technology

Lack of adequate statistical methods for the analysis of microarray data remains the most critical deterrent to uncovering the true potential of these promising techniques in basic and translational biological studies. The popular practice of drawing important biological conclusions from just one replicate (slide) should be discouraged. In this paper, we discuss some modern trends in statistical analysis of microarray data with a special focus on statistical classification (pattern recognition) and variable selection. In addressing these issues we consider the utility of some distances between random vectors and their nonparametric estimates obtained from gene expression data. Performance of the proposed distances is tested by computer simulations and analysis of gene expression data on two different types of human leukemia. In experimental settings, the error rate is estimated by cross-validation, while a control sample is generated in computer simulation experiments aimed at testing the proposed gene selection procedures and associated classification rules.

Multivariate approach for selecting sets of differentially expressed genes

An important problem addressed using cDNA microarray data is the detection of genes differentially expressed in two tissues of interest. Currently used approaches ignore the multidimensional structure of the data. However it is well known that correlation among covariates can enhance the ability to detect less pronounced differences. We use the Mahalanobis distance between vectors of gene expressions as a criterion for simultaneously comparing a set of genes and develop an algorithm for maximizing it. To overcome the problem of instability of covariance matrices we propose a new method of combining data from small-scale random search experiments. We show that by utilizing the correlation structure the multivariate method, in addition to the genes found by the one-dimensional criteria, finds genes whose differential expression is not detectable marginally.

Microarrays: handling the deluge of data and extracting reliable information

Application of powerful, high-throughput genomics technologies is becoming more common and these technologies are evolving at a rapid pace. Genomics facilities are being established in major research institutions to produce inexpensive, customized cDNA microarrays that are accessible to researchers in a broad range of fields. These high-throughput platforms have generated a massive onslaught of data, which threatens to overwhelm researchers. Although microarrays show great promise, the technology has not matured to the point of consistently generating robust and reliable data when used in the average laboratory. This article addresses several aspects related to the handling of the deluge of microarray data and extracting reliable information from these data. We review the essential elements of data acquisition, data processing and data analysis, and briefly discuss issues related to the quality, validation and storage of data. Our goal is to point out some of the problems that must be overcome before this promising technology can achieve its full potential.