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

ABSTRACT 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Single nucleotide polymorphisms (SNPs) are base differences in the human genome. These differences are favorable markers for genetic factors including those associated with risks of complex diseases and individual responses to drugs. When two duplex DNAs with different types of SNPs are mixed and reannealed, the two novel heteroduplexes containing mismatched base pairs are formed in addition to the two initial perfectly matched homoduplexes. Heteroduplex analysis recognizing the newly formed mismatched base pairs is useful for SNP detection. Various strategies to detect the mismatched base pairs were devised due to the potential applications of SNPs. However, they were not always convenient and accurate. Here, we propose a novel strategy to detect the mismatched base pairs by the specific interaction between the Hg2+ ion and a T:T mismatched base pair and that between the Ag+ ion and a C:C mismatched base pair. UV melting indicated that the melting temperature of only the heteroduplexes with the T:T and C:C mismatched base pair specifically increased on adding the Hg2+ and Ag+ ion, respectively. Fluorescence resonance energy transfer analyses indicated that the intensity of fluorophore emission of only the fluorophore and quencher-labeled heteroduplexes with the T:T and C:C mismatched base pair specifically decreased on adding the Hg2+ and Ag+ ion, respectively. We propose that the addition of the metal ion could be a convenient and accurate strategy to detect the mismatched base pair in the heteroduplex. This novel strategy might make the heteroduplex analysis easy and eventually lead to better SNP detection.
    Transition Metal Chemistry 01/2011; 36(2):131-144. · 1.40 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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. · 0.85 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The use of endothelial progenitor cells (EPCs) is a promising new treatment option for cardiovascular diseases. Many of the underlying mechanisms that result in an improvement of endothelial function in vivo remain poorly elucidated to this date, however. We summarize the current positions and potential applications of gene-expression profiling in the field of EPC biology. Based on our own and published gene-expression data, we demonstrate that gene-expression profiling can efficiently be used to characterize different EPC types. Furthermore, we highlight the potential of gene-expression profiling for the analysis of changes that EPCs undergo during culture and examine changes in gene transcription in diseased patients. Transcriptome profiling is a powerful tool for the characterization and functional analysis of EPCs in health and disease.
    Antioxidants & Redox Signaling 08/2011; 15(4):1029-42. · 8.20 Impact Factor

Olli Kallioniemi