Functional involvement of RINF, retinoid-inducible nuclear factor (CXXC5), in normal and tumoral human myelopoiesis.

Retinoids triggers differentiation of acute promyelocytic leukemia (APL) blasts by transcriptional regulation of myeloid regulatory genes. Using a microarray approach, we have identified a novel retinoid-responsive gene (CXXC5) encoding a nuclear factor, retinoid-inducible nuclear factor (RINF), that contains a CXXC-type zinc-finger motif. RINF expression correlates with retinoid-induced differentiation of leukemic cells and with cytokine-induced myelopoiesis of normal CD34(+) progenitors. Furthermore, short hairpin RNA (shRNA) interference suggests for this gene a regulatory function in both normal and tumoral myelopoiesis. Interestingly, RINF localizes to 5q31.3, a small region often deleted in myeloid leukemia (acute myeloid leukemia [AML]/myelodysplasia [MDS]) and suspected to harbor one or several tumor suppressor gene.

Salmon louse (Lepeophtheirus salmonis) transcriptomes during post molting maturation and egg production, revealed using EST-sequencing and microarray analysis.

BACKGROUND: Lepeophtheirus salmonis is an ectoparasitic copepod feeding on skin, mucus and blood from salmonid hosts. Initial analysis of EST sequences from pre adult and adult stages of L. salmonis revealed a large proportion of novel transcripts. In order to link unknown transcripts to biological functions we have combined EST sequencing and microarray analysis to characterize female salmon louse transcriptomes during post molting maturation and egg production. RESULTS: EST sequence analysis shows that 43% of the ESTs have no significant hits in GenBank. Sequenced ESTs assembled into 556 contigs and 1614 singletons and whenever homologous genes were identified no clear correlation with homologous genes from any specific animal group was evident. Sequence comparison of 27 L. salmonis proteins with homologous proteins in humans, zebrafish, insects and crustaceans revealed an almost identical sequence identity with all species. Microarray analysis of maturing female adult salmon lice revealed two major transcription patterns; up-regulation during the final molting followed by down regulation and female specific up regulation during post molting growth and egg production. For a third minor group of ESTs transcription decreased during molting from pre-adult II to immature adults. Genes regulated during molting typically gave hits with cuticula proteins whilst transcripts up regulated during post molting growth were female specific, including two vitellogenins. CONCLUSION: The copepod L.salmonis contains high a level of novel genes. Among analyzed L.salmonis proteins, sequence identities with homologous proteins in crustaceans are no higher than to homologous proteins in humans. Three distinct processes, molting, post molting growth and egg production correlate with transcriptional regulation of three groups of transcripts; two including genes related to growth, one including genes related to egg production. The function of the regulated transcripts is discussed in relation to post molting morphological changes in adult female salmon louse. There is clear evidence that transcription of the major yolk proteins is not induced before some of the post molting growth of abdomen and the genital segment has occurred. A hallmark for the observed growth is transcription of many putative cuticula proteins prior to the size increase.

Gene expression profile of oral squamous cell carcinomas from Sri Lankan betel quid users.

Oral squamous cell carcinoma (OSCC) is one of the major health problems in Sri Lanka and the disease is associated with the habit of Betel Quid (BQ) chewing. Using 35k oligo microarrays, we analyzed the gene expression profile of 15 Sri Lankan patients diagnosed with OSCCs and pair-wised normal controls and correlated the findings with the clinicopathological data. Following the recording of the scanned array images and data analysis, results for selected candidate genes were verified using QRT-PCR. Upon analysis, a total of 263 genes [71 (27%) of unknown functions previously not reported in OSCCs and 192 (73%) of known functions] were found as differentially expressed between tumors and controls. For the genes with known functions, 66 (34%; such as COL4A1, MMP1, MMP3, PLAU, SPARC and KRT19) were previously reported in OSCC and for the remaining 126 (66%; such as CD47, APOL3, RRAGC, BPIL1 and AZGP1) this is the first report in OSCCs. Hierarchical clustering of the differentially expressed 263 genes grouped the samples into several clusters with the larger one being dominated by tumors of stage 3 and 4. Two cases (a verrucous SCC and an advanced SCC), did not cluster with any of the other samples. We found two main biological pathways (cell communication and integrin-mediated cell adhesion) and 5 gene ontology categories (transcription regulator activity, structural molecule activity, intracellular signaling, cytoskeleton and signal transduction) of relevance to the OSCCs examined. Results from the QRT-PCR verified the results from the microarray experiment. This study provides valuable information on gene expression profile of OSCCs of habitual users of BQ from Sri Lanka. Of particular interest were the list of genes of known and unknown functions and the two biological pathways that we suggest as candidate genes in oral cancers associated with BQ chewing in Southeast Asia, in particular Sri Lanka. The suggested candidate genes might be used as molecular biomarkers in the early detection of the alarming problem of OSCCs in Southeast Asia in association with BQ use. These findings provide valuable information that might help in the selection of possible biomarkers that can be used in early detection of the alarming problem of oral cancer in Southeast Asia.

Gene expression profiles of head and neck carcinomas from Sudanese and Norwegian patients reveal common biological pathways regardless of race and lifestyle.

PURPOSE: To explore possible range of gene expression profiles in head and neck squamous cell carcinomas (HNSCC) and pairwised normal controls from Sudanese (n = 72) and Norwegian (n = 45) patients using a 15K cDNA microarray and to correlate the findings with clinicopathologic variables. EXPERIMENTAL DESIGN: Samples from Sudan were grouped according to anatomic location/patients' habit of toombak (snuff) use, and 37 pools of 2 to 11 tumors matched to 37 pools of their normal controls from the same patients, respectively, were prepared. For Norway, eight pools of 3 to 11 tumors matched to eight pools of their normal controls from the same patients, respectively, were prepared according to anatomic location. Pools (n = 45) were hybridized to microarrays. For controls, 33 of the pools were hybridized against Human Reference RNA. Scanned array images were recorded, and data analysis was done in groups. For verification, results for selected genes were analyzed using quantitative real-time PCR/immunohistochemistry. RESULTS: We identified 136 genes from Sudan and 154 from Norway as differentially expressed between tumors and controls. Changes of the genes found were confirmed in >70% of the pools by hybridization against Reference RNA. Seventy-three genes and three main pathways (signal transduction, cell communication, and ligand-receptor interaction) were of relevance to the HNSCCs from both countries. Hierarchical clustering of the 73 genes identified subclasses of mixed tumors from the two populations, two independent subgroups for Norwegian tumors by their anatomic sites, and five subgroups for Sudanese tumors by their toombak habits. Quantitative real-time PCR/immunohistochemistry validated the microarray-based data. CONCLUSIONS: Differences in gene expression between tumor and nontumor tissues were identified in HNSCCs. Analysis of the two population groups revealed a common set of 73 genes within three main biological pathways. This indicates that the development of HNSCCs is mediated by similar biological pathways regardless of differences related to race, ethnicity, lifestyle, and/or exposure to environmental carcinogens. Of particular interest, however, was the valuable association of gene expression signature found with toombak use and anatomic site of the tumors.

LSimpute: accurate estimation of missing values in microarray data with least squares methods.

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.

Analysis of gene-expression data using J-Express.

The J-Express package has been designed to facilitate the analysis of microarray data with an emphasis on efficiency, usability, and comprehensibility. The J-Express system provides a powerful and integrated platform for the analysis of microarray gene expression data. It is platform-independent in that it requires only the availability of a Java virtual machine on the system. The system includes a range of analysis tools and a project management system supporting the organization and documentation of an analysis project. This unit describes the J-Express tool, emphasizing central concepts and principles, and gives examples of how it can be used to explore gene expression data sets.

Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles.

Ovarian tumors range from benign to aggressive malignant tumors, including an intermediate class referred to as borderline carcinoma. The prognosis of the disease is strongly dependent on tumor classification, where patients with borderline tumors have much better prognosis than patients with carcinomas. We here describe the use of hierarchical clustering analysis of quantitative protein expression data for classification of this type of tumor. An accurate classification was not achieved using an unselected set of 1,584 protein spots for clustering analysis. Different approaches were used to select spots that were differentially expressed between tumors of different malignant potential and to use these sets of spots for classification. When sets of proteins were selected that differentiated benign and malignant tumors, borderline tumors clustered in the benign group. This is consistent with the biologic properties of these tumors. Our results indicate that hierarchical clustering analysis is a useful approach for analysis of protein profiles and show that this approach can be used for differential diagnosis of ovarian carcinomas and borderline tumors.