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Laura Elo

Publications and source records attributed to Laura Elo.

2 recordsLinked to original sources

Th1 response and cytotoxicity genes are down-regulated in cutaneous T-cell lymphoma.

PURPOSE: Increased production of Th2 cytokines characterizes Sezary syndrome, the leukemic form of cutaneous T-cell lymphomas (CTCL). To identify the molecular background and to study whether shared by the most common CTCL subtype, mycosis fungoides, we analyzed the gene expression profiles in both subtypes. EXPERIMENTAL DESIGN: Freshly isolated cells from 30 samples, representing skin, blood, and enriched CD4(+) cell populations of mycosis fungoides and Sezary syndrome, were analyzed with Affymetrix (Santa Clara, CA) oligonucleotide microarrays, quantitative PCR, or immunohistochemistry. The gene expression profiles were combined with findings of comparative genomic hybridization of the same samples to identify chromosomal changes affecting the aberrant gene expression. RESULTS: We identified a set of Th1-specific genes [e.g., TBX21 (T-bet), NKG7, and SCYA5 (RANTES)] to be down-regulated in Sezary syndrome as well as in a proportion of mycosis fungoides samples. In both Sezary syndrome and mycosis fungoides blood samples, the S100P and LIR9 gene expression was up-regulated. In lesional skin, IL7R and CD52 were up-regulated. Integration of comparative genomic hybridization and transcriptomic data identified chromosome arms 1q, 3p, 3q, 4q, 12q, 16p, and 16q as likely targets for new CTCL-associated gene aberrations. CONCLUSIONS: Our findings revealed several new genes involved in CTCL pathogenesis and potential therapeutic targets. Down-regulation of a set of genes involved in Th1 polarization, including the major Th1-polarizing factor, TBX21, was for the first time associated with CTCL. In addition, a plausible explanation for the proliferative response of CTCL cells to locally produced interleukin-7 was revealed.

Cytotoxicity, Immunologic↗

Improving missing value estimation in microarray data with gene ontology.

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.

Algorithms↗