Background: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerousefforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancerrelatedsignals are generally lacking.Method: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression topair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset thistransformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). Theefficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioningwith informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio).Classification results were compared to the original datasets for up to 10-feature model classifiers.Results: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasetsrespectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient ofvariation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed datasetexhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV,coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higherclassification efficiency, especially with the colon dataset-from 87.1% to 93.5%. Over 90% of the top ten discriminating axes inboth datasets showed significant improvement after data transformation. The high classification efficiency achieved suggestedthat there exist some cancer-related signals in the form of pair-wise gene expression ratio.Conclusion: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformationachieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) thecomparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio betweensome pairs of genes can identify reliable markers for cancer.

The main proteinase of SARS-associated coronavirus (SARS-CoV) plays an important role in viral transcription andreplication, and is an attractive target for anti-SARS drug development. The important thing is to understand its binding mechanismwith possible ligands. In this study, we investigated possible noncanonical interactions, potential inhibitors, and bindingpockets in the main proteinase of SARS-CoV based on its recently determined crystal structure. These findings provide a wide clueto searching for anti-SARS drug. Interestingly, we found that similar structure patterns exist in SARS-CoV main proteinase withPoliovirus 3c Proteinase, Rhinovirus 3c Protease, Nsp4 Proteinase From Equine Arteritis Virus, Hepatitis C Virus Ns3 Protease,Hepatitis A Virus 3c Protease, and Dengue Virus Ns3 Protease. It suggests that the available drugs in these viruses could be used tofight SARS disease.

The origin of severe acute respiratory syndrome-associated coronavirus(SARS-CoV) is still a matter of speculation, although more than oneyear has passed since the onset of the SARS outbreak. In this study, we implementeda 3-step strategy to test the intriguing hypothesis that SARS-CoVmight have been derived from a recombinant virus. First, we blasted the wholeSARS-CoV genome against a virus database to search viruses of interest. Second,we employed 7 recombination detection techniques well documented in successfullydetecting recombination events to explore the presence of recombinationin SARS-CoV genome. Finally, we conducted phylogenetic analyses to furtherexplore whether recombination has indeed occurred in the course of coronaviruseshistory predating the emergence of SARS-CoV. Surprisingly, we found that 7putative recombination regions, located in Replicase 1ab and Spike protein, existbetween SARS-CoV and other 6 coronaviruses: porcine epidemic diarrheavirus (PEDV), transmissible gastroenteritis virus (TGEV), bovine coronavirus(BCoV), human coronavirus 229E (HCoV), murine hepatitis virus (MHV), andavian infectious bronchitis virus (IBV). Thus, our analyses substantiate the presenceof recombination events in history that led to the SARS-CoV genome.Like the other coronaviruses used in the analysis, SARS-CoV is also a mosaicstructure.

Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery.In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARSCoV).Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugscontaining the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. Thismeans that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for furtherSARS-CoV main proteinase inhibitor development.

Gene transcription in a set of 49 human primary lungadenocarcinomas and 9 normal lung tissue sampleswas examined using Affymetrix GeneChip technology.A total of 3442 genes, called the set MAD,were found to be either up- or down-regulated byat least 2-fold between the two phenotypes. Genesassigned to a particular gene ontology term werefound, in many cases, to be significantly unevenlydistributed between the genes in and outside MAD.Terms that were overrepresented in MAD includedfunctions directly implicated in the cancer cellmetabolism. Based on their functional roles andexpression profiles, genes in MAD were groupedinto likely co-regulated gene sets. Highly conservedsequences in the 5 kb region upstream of the genesin these sets were identified with the motif discoverytool, MoDEL. Potential oncogenic transcription factorsand their corresponding binding sites wereidentified in these conserved regions using theTRANSFAC 8.3 database. Several of the transcriptionfactors identified in this study have beenshown elsewhere to be involved in oncogenic processes.This study searched beyond phenotypicgene expression profiles in cancer cells, in orderto identify the more important regulatory transcriptionfactors that caused these aberrations in geneexpression.