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Doulaye Dembélé

Publications and source records attributed to Doulaye Dembélé.

3 recordsLinked to original sources

Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer among men in the developed world. There is a need, for both clinical and scientific reasons, to find markers to identify patients with aggressive disease as early as possible, and to understand the events leading to malignant transformation and susceptibility to metastasis. We report the first large-scale gene expression analysis of a unique HNSCC location, the hypopharynx. Four normal and 34 tumour samples were analysed with 12 600 gene microarrays. Clusters of differentially expressed genes were identified in the chromosomal regions 3q27.3, 17q21.2-q21.31, 7q11.22-q22.1 and 11q13.1-q13.3, which, interestingly, have already been identified by comparative genomic hybridization (CGH) as major regions of gene amplification. We showed that six overexpressed genes (EIF4G1, DVL3, EPHB4, MCM7, BRMS1 and SART1) located in these regions are indeed amplified. We report 119 genes that are highly differentially expressed between 'early' tumours and normal samples. Of these, we validated by quantitative PCR six novel poorly characterized genes. These genes are potential new markers of HNSCC. Comparing patients with relatively nonaggressive and aggressive tumours (without or with clinical evidence of metastasis 3 years after surgery), we identified 164 differentially expressed genes potentially involved in the acquisition of metastatic potential. This study contributes to the understanding of HNSCC, staging patients into prognostic groups and identifying high-risk patients who may benefit from more aggressive treatment.

Adult↗

Fuzzy C-means method for clustering microarray data.

MOTIVATION: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. RESULTS: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. AVAILABILITY: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

Algorithms↗

Quality indicators increase the reliability of microarray data.

Large-scale gene expression profiling with DNA microarrays opens new dimensions to molecular biology but still lacks the overall precision of traditional low-scale techniques. We developed a novel strategy of data processing linking search stringency to quality indicators for efficient detection of low-level, regulated genes. Using retinoid-induced differentiation of NB-4 promyelocytic cells, the variation of expression profiles between biological duplicates was studied and compared with the changes induced by all-trans retinoic acid (atRA) treatment. An analysis of 4320 genes showed that retinoic acid has mainly geneactivating function in NB-4 cells. Treatment with atRA for 18 hours induced metabolic genes that may be associated with cell differentiation and signaling factors triggering later events leading to apoptosis; cytokine genes were among the highest stimulated by atRA. Notably, we identified a regulatory loop inhibiting MYC action: as MYC was downregulated, a cognate repressor of MYC was upregulated.

Cluster Analysis↗