BRAFV600E mutations in malignant melanoma are associated with increased expressions of BAALC

<p>Abstract</p> <p>Bachground</p> <p>Activating <it>BRAF </it>mutations are present in approximately 50% of melanomas. Although different downstream target genes of the most common mutant V600E have been identified, the contribution of activating <it>BRAF </it>mutations to malignant transformation needs further clarification.</p> <p>Methods</p> <p>Microarray gene analysis was performed for human melanoma cell lines harboring BRAF^V600Emutations in comparison to cell lines without this mutation.</p> <p>Results</p> <p>This analysis revealed a more than two fold down-regulation of 43 and an increase of 39 gene products. <it>BAALC </it>(<it>Brain and acute Leukaemia, cytoplasmatic</it>) was most prominently regulated, since it was up-regulated in mutated cell lines by a mean of 11.45. Real time PCR analyses with RNA from melanoma cell lines (n = 30) confirmed the <it>BRAF</it>-activation dependent up-regulation of <it>BAALC</it>.</p> <p>Conclusion</p> <p><it>BAALC</it>, which has been associated with cell dedifferentiation and migration, may function as a downstream effector of activating <it>BRAF </it>mutations during melanomagenesis.</p

Downregulation of organic cation transporters OCT1 (SLC22A1) and OCT3 (SLC22A3) in human hepatocellular carcinoma and their prognostic significance

<p>Abstract</p> <p>Background</p> <p>Organic cation transporters (OCT) are responsible for the uptake and intracellular inactivation of a broad spectrum of endogenous substrates and detoxification of xenobiotics and chemotherapeutics. The transporters became pharmaceutically interesting, because OCTs are determinants of the cytotoxicity of platin derivates and the transport activity has been shown to correlate with the sensitivity of tumors towards tyrosine kinase inhibitors. No data exist about the relevance of OCTs in hepatocellular carcinoma (HCC).</p> <p>Methods</p> <p>OCT1 (<it>SLC22A1</it>) and OCT3 (<it>SLC22A3</it>) mRNA expression was measured in primary human HCC and corresponding non neoplastic tumor surrounding tissue (TST) by real time PCR (n = 53). Protein expression was determined by western blot analysis and immunofluorescence. Data were correlated with the clinicopathological parameters of HCCs.</p> <p>Results</p> <p>Real time PCR showed a downregulation of <it>SLC22A1 </it>and <it>SLC22A3 </it>in HCC compared to TST (p ≤ 0.001). A low <it>SLC22A1 </it>expression was associated with a worse patient survival (p < 0.05). Downregulation was significantly associated with advanced HCC stages, indicated by a higher number of T3 tumors (p = 0.025) with a larger tumor diameter (p = 0.035), a worse differentiation (p = 0.001) and higher AFP-levels (p = 0.019). In accordance, <it>SLC22A1 </it>was less frequently downregulated in tumors with lower stages who underwent transarterial chemoembolization (p < 0.001) and liver transplantation (p = 0.001). Tumors with a low <it>SLC22A1 </it>expression (< median) showed a higher <it>SLC22A3 </it>expression compared to HCC with high <it>SLC22A1 </it>expression (p < 0.001). However, there was no significant difference in tumor characteristics according to the level of the <it>SLC22A3 </it>expression.</p> <p>In the western blot analysis we found a different protein expression pattern in tumor samples with a more diffuse staining in the immunofluorescence suggesting that especially OCT1 is not functional in advanced HCC.</p> <p>Conclusion</p> <p>The downregulation of OCT1 is associated with tumor progression and a worse patient survival.</p

Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions

<p>Abstract</p> <p>Background</p> <p>The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information.</p> <p>Results</p> <p>The proposed method combines data sets posterior to the discretization of data sets based on the ranks of the gene expression ratios, and the statistical method is applied to the combined data set for predictive gene selection. The efficiency of the proposed method was evaluated using five colon cancer related data sets, which were experimented using cDNA microarrays with different RNA sources, and one experiment utilized oligonucleotide arrays. NCI-60 cell lines data sets were used, which were performed with two different platforms of cDNA microarrays and Affymetrix HU6800 oligonucleotide arrays. The combined data set by the proposed method predicted the test data sets more accurately than the separated data sets did. The biological significant genes were detected from the combined data set, which were missed on the separated data sets.</p> <p>Conclusion</p> <p>By transforming gene expressions using ranks, the proposed method is not influenced by systematic bias among chips and normalization method. The method may be especially more useful to find predictive genes from data sets which have different scale in gene expressions.</p

Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder

<p>Abstract</p> <p>Background</p> <p>Detecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms.</p> <p>Results</p> <p>We applied a random intercept model to account for confounding variables and case-control paired design. A variable selection scheme was developed to determine the effective confounders in each gene. Meta-analysis methods were used to integrate information from five studies and post hoc analyses enhanced biological interpretations. Simulations and application results showed that the adjustment for confounding variables and meta-analysis improved detection of biomarkers and associated pathways.</p> <p>Conclusions</p> <p>The proposed framework simultaneously considers correction for confounding variables, selection of effective confounders, random effects from paired design and integration by meta-analysis. The approach improved disease-related biomarker and pathway detection, which greatly enhanced understanding of MDD neurobiology. The statistical framework can be applied to similar experimental design encountered in other complex and heterogeneous diseases.</p

An Attempt for Combining Microarray Data Sets by Adjusting Gene Expressions

PURPOSE: The diverse experimental environments in microarray technology, such as the different platforms or different RNA sources, can cause biases in the analysis of multiple microarrays. These systematic effects present a substantial obstacle for the analysis of microarray data, and the resulting information may be inconsistent and unreliable. Therefore, we introduced a simple integration method for combining microarray data sets that are derived from different experimental conditions, and we expected that more reliable information can be detected from the combined data set rather than from the separated data sets. MATERIALS AND METHODS: This method is based on the distributions of the gene expression ratios among the different microarray data sets and it transforms, gene by gene, the gene expression ratios into the form of the reference data set. The efficiency of the proposed integration method was evaluated using two microarray data sets, which were derived from different RNA sources, and a newly defined measure, the mixture score. RESULTS: The proposed integration method intermixed the two data sets that were obtained from different RNA sources, which in turn reduced the experimental bias between the two data sets, and the mixture score increased by 24.2%. A data set combined by the proposed method preserved the inter-group relationship of the separated data sets. CONCLUSION: The proposed method worked well in adjusting systematic biases, including the source effect. The ability to use an effectively integrated microarray data set yields more reliable results due to the larger sample size and this also decreases the chance of false negatives.ope

Combining transcriptional datasets using the generalized singular value decomposition

Background Both microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies while the latter is a more targeted technique. Because of platform-dependent systematic effects, simple comparisons or merging of datasets obtained by these technologies are difficult, even though they may often be desirable. These difficulties are exacerbated if there is only partial overlap between the experimental conditions and genes probed in the two datasets. Results We show here that the generalized singular value decomposition provides a practical tool for merging a small, targeted dataset obtained by quantitative real-time PCR of specific genes with a much larger microarray dataset. The technique permits, for the first time, the identification of genes present in only one dataset co-expressed with a target gene present exclusively in the other dataset, even when experimental conditions for the two datasets are not identical. With the rapidly increasing number of publically available large scale microarray datasets the latter is frequently the case. The method enables us to discover putative candidate genes involved in the biosynthesis of the (1,3;1,4)-β-D-glucan polysaccharide found in plant cell walls. Conclusion We show that the generalized singular value decomposition provides a viable tool for a combined analysis of two gene expression datasets with only partial overlap of both gene sets and experimental conditions. We illustrate how the decomposition can be optimized self-consistently by using a judicious choice of genes to define it. The ability of the technique to seamlessly define a concept of "co-expression" across both datasets provides an avenue for meaningful data integration. We believe that it will prove to be particularly useful for exploiting large, publicly available, microarray datasets for species with unsequenced genomes by complementing them with more limited in-house expression measurements.Andreas W Schreiber, Neil J Shirley, Rachel A Burton and Geoffrey B Finche

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures

<p>Abstract</p> <p>Background</p> <p>Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich <it>in vivo </it>datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.</p> <p>Results</p> <p>The study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.</p> <p>Conclusions</p> <p>We have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich <it>in vivo </it>datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.</p