A Decade of Cancer Gene Profiling: From Molecular Portraits to Molecular Function

VTT Medical Biotechnology, Turku, Finland.
Methods in molecular biology (Clifton, N.J.) (Impact Factor: 1.29). 01/2010; 576:61-87. DOI: 10.1007/978-1-59745-545-9_5
Source: PubMed


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.

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    • "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.
    Preview · Article · Dec 2011 · Genetics and molecular research: GMR
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    • "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: High performance genetic analysis is an integration of various inter-correlated technologies. Of all the technologies, chemical reagents are indispensible for modifying DNA or RNA, yet the total performance of genetic analysis is sometimes limited by the insufficient capability of reagents. We have developed novel chemical reagents to increase accuracy and sensitivity in genetic analysis. We describe the development process from obtaining the original idea to marketing of the products and discuss important factors in the process.
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