Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data

Background  

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Frequent cytogenetic abnormalities that occur in HCC suggest that tumor-modifying genes (oncogenes or tumor suppressors) may be driving selection for amplification or deletion of these particular genetic regions. In many cases, however, the gene(s) that drive the selection are unknown. Although techniques such as comparative genomic hybridization (CGH) have traditionally been used to identify cytogenetic aberrations, it might also be possible to identify them indirectly from gene-expression studies. A technique we have called comparative genomic microarray analysis (CGMA) predicts regions of cytogenetic change by searching for regional gene-expression biases. CGMA was applied to HCC gene-expression profiles to identify regions of frequent cytogenetic change and to identify genes whose expression is misregulated within these regions.     

Loss of Rhb1, a Rheb-related GTPase in fission yeast, causes growth arrest with a terminal phenotype similar to that caused by nitrogen starvation

The Rheb GTPase is most similar in primary sequence to the Ras, Rap, R-Ras, and Ral GTPases, which regulate cell growth and differentiation in many cell types. A likely fission yeast homologue of mammalian Rheb, which we designated Rhb1, was identified by genome sequencing. Our investigation of rhb1 showed that rhb1(-) cells arrested cell growth and division with a terminal phenotype similar to that of nitrogen-starved cells. In particular, cells depleted of Rhb1 arrested as small, round cells with 1N DNA content, arrested more quickly in low-nitrogen medium, and induced expression of fnx1 and mei2 mRNA, two mRNAs that were normally induced by nitrogen starvation. Since mammalian Rheb binds and may regulate Raf-1, a Ras effector, we tested for functional overlap between Ras1 and Rhb1 in fission yeast. This analysis showed that Ras1 overexpression did not suppress rhb1(-) mutant phenotypes, Rhb1 overexpression did not suppress ras1(-) mutant phenotypes, and ras1(-) rhb1(-) double mutants had phenotypes equal to the sum of the corresponding single-mutant phenotypes. Hence, there is no evidence for overlapping functions between Ras1 and Rhb1. On the basis of this study, we hypothesize that Rhb1 negatively regulates entry into stationary phase when extracellular nitrogen levels are adequate for growth. If this hypothesis is correct, then Rhb1 and Ras1 regulate alternative responses to limiting nutrients.     

Comparative gene expression profiling analysis of urothelial carcinoma of the renal pelvis and bladder

Background

Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.

Methods

For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.

Results

Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.

Conclusion

We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.