A decade of cancer gene profiling: from molecular portraits to molecular function
Cancer gene profiling has greatly profited from the progress in high-throughput technologies, including microarray-, sequencing-, and bioinformatics-based methods. The flood of data generated during the last decade has provoked a panel of "-omics" fields that significantly changed our understanding of malignant diseases. However, while the terms "-omics" and "-ome" in principle refer to the completeness of a genetic approach, we are in fact far from a complete understanding of cancer progression. We may understand gene expression patterns better and successfully use gene signatures for outcome prediction and prognosis, but truly promising molecular targets still have to find their way into novel therapeutic concepts. In this chapter, we will show how more comprehensive strategies, integrating multiple layers of genetic information, might in the future provide a more profound functional understanding of cancer.
Available from: geneticsmr.com
- "The study of gene differential expression in microarray studies plays a central role in bioinformatics today (Ramdayal, 2010; Sara et al., 2010). Several methods have been developed using a variety of statistical techniques (Farcomeni, 2008; Bremer et al., 2010). "
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ABSTRACT: HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression in low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. We developed an extension of HTself, originally released in 2005, by calculating P values instead of using a fixed acceptance level α. As before, the statistic used to compute single-spot P values is obtained from the Gaussian kernel density estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent P values that can be combined by well-known statistical methods. The combined P value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses P values combination. It is implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure.
Genetics and molecular research: GMR 12/2011; 10(4):3586-95. DOI:10.4238/2011.December.5.5 · 0.78 Impact Factor
Available from: Alnawaz Rehemtulla
- "Cell signaling is a mode of communication by which the intracellular information is conveyed from the site of instigation to the site of action. Recent advances in molecular profiling technologies such as microarrays and proteomics along with synergistic growth in the field of bio-informatics, have actuated our appreciation of signaling changes in patho-physiological conditions and led to identification of unique disease biomarkers [1-5]. For example growth factor such as EGFR or Her-2, may be considered as biomarkers in certain human cancers where they are amplified, overexpressed and/or mutated and immensely alter the downstream signaling [6-12]. "
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ABSTRACT: Recent advances in oncology have lead to identification of a plethora of alterations in signaling pathways that are critical to oncogenesis and propagation of malignancy. Among the biomarkers identified, dysregulated kinases and associated changes in signaling cascade received the lion's share of scientific attention and have been under extensive investigations with goal of targeting them for anti-cancer therapy. Discovery of new drugs is immensely facilitated by molecular imaging technology which enables non-invasive, real time, dynamic imaging and quantification of kinase activity. Here, we review recent development of novel kinase reporters based on conformation dependent complementation of firefly luciferase to monitor kinase activity. Such reporter system provides unique insights into the pharmacokinetics and pharmacodynamics of drugs that modulate kinase signaling and have a huge potential in drug discovery, validation, and drug-target interactions.
Cancer Cell International 07/2010; 10(1):23. DOI:10.1186/1475-2867-10-23 · 2.77 Impact Factor
Available from: harvard.edu
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ABSTRACT: Genome-wide sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to the genomic sequence. Now next generation sequencing (NGS) extends that reach into multiple almost complete genomes of the same species, revealing more and more details about how individual genomes as well as individual aspects of their regulation differ from each other. The inclusion of NGS-based transcriptome sequencing, chromatin-immunoprecipitation (ChIP) of transcription factor binding and epigenetic analyses (usually based on DNA methylation or histone modification ChIP) completes the picture with unprecedented resolution enabling the detection of even subtle differences such as alternative splicing of individual exons. Functional genomics aims at the elucidation of the molecular basis of biological functions and requires analyses that go far beyond the primary analysis of the reads such as mapping to a genome template sequence. The various and complex interactions between the genome, gene products and metabolites define biological function, which necessitates inclusion of results other than sequence tags in quite elaborative approaches. However, the extra efforts pay off in revealing mechanisms as well as providing the foundation for new strategies in systems biology and personalized medicine. This review emphasizes the particular contribution NGS-based technologies make to functional genomics research with a special focus on gene regulation by transcription factor binding sites.
Briefings in Bioinformatics 09/2010; 11(5):499-511. DOI:10.1093/bib/bbq018 · 9.62 Impact Factor
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