Journal of clinical bioinformatics最新文献

The microRNA miR-155 is prominent in cancer biology. Among microRNAs that have been linked to cancer, it is the most commonly overexpressed in malignancies (PNAS 109:20047-20052, 2012). Since its discovery, miR-155 has been implicated in promoting cancers of the breast, lung, liver, and lymphatic system. As such, targeted therapies may prove beneficial to cancer treatment. This review discusses the important role of miR-155 in oncogenesis. It synthesizes information from ten recent papers on miR-155, and includes an analysis and discussion of its association with cancer, interactions with other miRNAs, mechanisms of action, and the most promising available treatment options.Current debates in the field include the importance of miRNAs in general and their utility as targets in preventing tumorigenesis (Blood 119:513-520, 2012). Most of the papers being reviewed here confirm the role of miR-155 in oncogenesis (EMBO Mol Med 1:288-295, 2009). While there is some controversy surrounding recent research that claims that miR-155 may display anti-oncogenic or pro-immunological benefits (Cell Rep 2:1697-1709, 2012), most research seems to point to the importance of anti-miRs, with anti-miR-155 in particular, for cancer therapy.

Background: Immunoglobulin (that is, antibody) and T cell receptor genes are created through somatic gene rearrangement from gene segment libraries. Immunoglobulin genes are further diversified by somatic hypermutation and selection during the immune response. Studying the repertoires of these genes yields valuable insights into immune system function in infections, aging, autoimmune diseases and cancers. The introduction of high throughput sequencing has generated unprecedented amounts of repertoire and mutation data from immunoglobulin genes. However, common analysis programs are not appropriate for pre-processing and analyzing these data due to the lack of a template or reference for the whole gene.

Results: We present here the automated analysis pipeline we created for this purpose, which integrates various software packages of our own development and others', and demonstrate its performance.

Conclusions: Our analysis pipeline presented here is highly modular, and makes it possible to analyze the data resulting from high-throughput sequencing of immunoglobulin genes, in spite of the lack of a template gene. An executable version of the Automation program (and its source code) is freely available for downloading from our website: http://immsilico2.lnx.biu.ac.il/Software.html.

Background: Next-generation sequencing has enabled examination of variation at the DNA sequence level and can be further enhanced by evaluation of the variants at the protein level. One powerful method is to visualize these data often revealing patterns not immediately apparent in a text version of the same data. Many investigators are interested in knowing where their amino acid changes reside within a protein. Clustering of variation within a protein versus non-clustering can show interesting aspects of the biological changes happening in disease.

Finding: We describe a freely available tool, Plot Protein, executable from the command line or utilized as a graphical interface through a web browser, to enable visualization of amino acid changes at the protein level. This allows researchers to plot variation from their sequencing studies in a quick and uniform way. The features available include plotting amino acid changes, domains, post-translational modifications, reference sequence, conservation, conservation score, and also zoom capabilities. Herein we provide a case example using this tool to examine the RET protein and we demonstrate how clustering of mutations within the protein in Multiple Endocrine Neoplasia 2A (MEN2A) reveals important information about disease mechanism.

Conclusions: Plot Protein is a useful tool for investigating amino acid changes and their localization within proteins. Command line and web server versions of this software are described that enable users to derive visual knowledge about their mutations.

Background: Quantification and normalization of RT-qPCR data critically depends on the expression of so called reference genes. Our goal was to develop a strategy for the selection of reference genes that utilizes microarray data analysis and combines known approaches for gene stability evaluation and to select a set of appropriate reference genes for research and clinical analysis of breast samples with different receptor and cancer status using this strategy.

Methods: A preliminary search of reference genes was based on high-throughput analysis of microarray datasets. The final selection and validation of the candidate genes were based on the RT-qPCR data analysis using several known methods for expression stability evaluation: comparative ∆Ct method, geNorm, NormFinder and Haller equivalence test.

Results: A set of five reference genes was identified: ACTB, RPS23, HUWE1, EEF1A1 and SF3A1. The initial selection was based on the analysis of publically available well-annotated microarray datasets containing different breast cancers and normal breast epithelium from breast cancer patients and epithelium from cancer-free patients. The final selection and validation were performed using RT-qPCR data from 39 breast cancer biopsy samples. Three genes from the final set were identified by the means of microarray analysis and were novel in the context of breast cancer assay. We showed that the selected set of reference genes is more stable in comparison not only with individual genes, but also with a system of reference genes used in commercial OncotypeDX test.

Conclusion: A selection of reference genes for RT-qPCR can be efficiently performed by combining a preliminary search based on the high-throughput analysis of microarray datasets and final selection and validation based on the analysis of RT-qPCR data with a simultaneous examination of different expression stability measures. The identified set of reference genes proved to be less variable and thus potentially more efficient for research and clinical analysis of breast samples comparing to individual genes and the set of reference genes used in OncotypeDX assay.

Background: DNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).

Results: We propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post-processing filtering to any given segmentation method.

Conclusions: Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed 'waves'.

Background: Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology.

Results: We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions).

Conclusions: Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Background: Whole genome microarray gene expression profiling is the 'gold standard' for the discovery of prognostic and predictive genetic markers for human cancers. However, suitable research material is lacking as most diagnostic samples are preserved as formalin-fixed, paraffin-embedded tissue (FFPET). We tested a new workflow and data analysis method optimized for use with FFPET samples.

Methods: Sixteen breast tumor samples were split into matched pairs and preserved as FFPET or fresh-frozen (FF). Total RNA was extracted and tested for yield and purity. RNA from FFPET samples was amplified using three different commercially available kits in parallel, and hybridized to Affymetrix GeneChip® Human Genome U133 Plus 2.0 Arrays. The array probe set was optimized in silico to exclude misdesigned and misannotated probes.

Results: FFPET samples processed using the WT-Ovation™ FFPE System V2 (NuGEN) provided 80% specificity and 97% sensitivity compared with FF samples (assuming values of 100%). In addition, in silico probe set redesign improved sequence detection sensitivity and, thus, may rescue potentially significant small-magnitude gene expression changes that could otherwise be diluted by the overall probe set background.

Conclusion: In conclusion, our FFPET-optimized workflow enables the detection of more genes than previous, nonoptimized approaches, opening new possibilities for the discovery, validation, and clinical application of mRNA biomarkers in human diseases.

Background: Like all other neurodegenerative diseases, Alzheimer's disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework.

Methods: In a previous paper, we developed an efficient SVM (Support Vector Machine) based method, which we have now applied to discover important biomarkers and target networks which provide strategies for AD therapy.

Results: The methodology selects a number of blood-based biomarkers (fewer than 10% of initial numbers on three AD datasets from NCBI), and the results are statistically verified by cross-validation. The resulting SVM is a classifier of AD vs. normal subjects. We construct target networks of AD based on MI (mutual information). In addition, a hierarchical clustering is applied on the initial data and clustered genes are visualized in a heatmap. The proposed method also performs gender analysis by classifying subjects based on gender.

Conclusions: Unlike other traditional statistical analyses, our method uses a machine learning-based algorithm. Our method selects a small set of important biomarkers for AD, differentiates noisy (irrelevant) from relevant biomarkers and also provides the target networks of the selected biomarkers, which will be useful for diagnosis and therapeutic design. Finally, based on the gender analysis, we observe that gender could play a role in AD diagnosis.

Background: Exploring stromal changes associated with tumor growth and development is a growing area of oncologic research. In order to study molecular changes in the stroma it is recommended to separate tumor tissue from stromal tissue. This is relevant to xenograft models where tumors can be small and difficult to separate from host tissue. We introduce a novel definition of cross-alignment/cross-hybridization to compare qualitatively the ability of high-throughput mRNA sequencing, RNA-Seq, and microarrays to detect tumor and stromal expression from mixed 'pseudo-xenograft' samples vis-à-vis genes and pathways in cross-alignment (RNA-Seq) and cross-hybridization (microarrays). Samples consisted of normal mouse lung and human breast cancer cells; these were combined in fixed proportions to create a titration series of 25% steps. Our definition identifies genes in a given species (human or mouse) with undetectable expression in same-species RNA but detectable expression in cross-species RNA. We demonstrate the comparative value of this method and discuss its potential contribution in cancer research.

Results: Our method can identify genes from either species that demonstrate cross-hybridization and/or cross-alignment properties. Surprisingly, the set of genes identified using a simpler and more common approach (using a 'pure' cross-species sample and calling all detected genes as 'crossers') is not a superset of the genes identified using our technique. The observed levels of cross-hybridization are relatively low: 5.3% of human genes detected in mouse, and 3.5% of mouse genes detected in human. Observed levels of cross-alignment are practically comparable to the levels of cross-hybridization: 6.5% of human genes detected in mouse, and 2.3% of mouse genes detected in human. We also observed a relatively high percentage of orthologs: 40.3% of cross-hybridizing genes, and 32.2% of cross-aligning genes.Normalizing the gene catalog to use Consensus Coding Sequence (CCDS) IDs (Genome Res 19:1316-1323, 2009), our results show that the observed levels of cross-hybridization are low: 2.7% of human CCDS IDs are detected in mouse, and 2.4% of mouse CCDS IDs are detected in human. Levels of cross-alignment using the RNA-Seq data are comparable for the mouse, 2.2% of mouse CCDS IDs detected in human, and 9.9% of human CCDS IDs detected in mouse. However, the lists of cross-aligning/cross-hybridizing genes contain many that are of specific interest to oncologic researchers.

Conclusions: The conservative definition that we propose identifies genes in mouse whose expression can be attributed to human RNA, and vice versa, as well as revealing genes with cross-alignment/cross-hybridization properties which could not be identified using a simpler but more established approach. The overall percentage of genes affected by cross-hybridization/cross-alignment is small, but includes genes that are of int

Background: MicroRNAs were found to play an important role in cancers and several literatures exist to describe the relationship between microRNA and cancer, but the expression pattern was still faintly. There is a need for a comprehensive collection and summary of the interactions under experimental support.

Description: TUMIR (http://www.ncrnalab.com/TUMIR/), a manually extracted database of experimentally supported microRNA-cancer relationship, aims at providing a large, high-quality, validated comprehensive resource of microRNA deregulation in various cancers. The current version includes a systematic literature search to May-1-2012 using PubMed database, contains data extracted from 205 literatures and 1163 entries describing a regulatory interaction between human microRNAs and cancers. Each entry in the database contains the details of microRNA name, the disease name, case number, control number, p value, the experimentally validated targets, sample type, and a brief description of patients' clinic pathologic parameters mentioned in the same paper. The website has several extensive external links to the related websites and any requests can be made by emailing to tumir_pumc@163.com.

Conclusion: TUMIR is an open access website and will be an accurate clue for the researchers who are interested in better understanding the relationship between miRNAs and cancer.