BACKGROUND: Xenobiotic-metabolizing enzymes (XME) play a critical role in the activation and detoxification of several carcinogens. However, the role of XMEs in colorectal carcinogenesis is unclear. METHODS: We investigated the expression of XMEs in human colorectal tissues among patients with stage I-IV colorectal cancer (n = 71) from the ColoCare Study. Transcriptomic profiling using paired colorectal tumor and adjacent normal mucosa tissues of XMEs (GSTM1, GSTA1, UGT1A8, UGT1A10, CYP3A4, CYP2C9, GSTP1, and CYP2W1) by RNA microarray was compared using Wilcoxon rank-sum tests. We assessed associations between clinicopathologic, dietary, and lifestyle factors and XME expression with linear regression models. RESULTS: GSTM1, GSTA1, UGT1A8, UGT1A10, and CYP3A4 were all statistically significantly downregulated in colorectal tumor relative to normal mucosa tissues (all P ≤ 0.03). Women had significantly higher expression of GSTM1 in normal tissues compared with men (β = 0.37, P = 0.02). By tumor site, CYP2C9 expression was lower in normal mucosa among patients with rectal cancer versus colon cancer cases (β = -0.21, P = 0.0005). Smokers demonstrated higher CYP2C9 expression levels in normal mucosa (β = 0.17, P = 0.02) when compared with nonsmokers. Individuals who used NSAIDs had higher GSTP1 tumor expression compared with non-NSAID users (β = 0.17, P = 0.03). Higher consumption of cooked vegetables (>1×/week) was associated with higher CYP3A4 expression in colorectal tumor tissues (β = 0.14, P = 0.007). CONCLUSIONS: XMEs have lower expression in colorectal tumor relative to normal mucosa tissues and may modify colorectal carcinogenesis via associations with clinicopathologic, lifestyle, and dietary factors. IMPACT: Better understanding into the role of drug-metabolizing enzymes in colorectal cancer may reveal biological differences that contribute to cancer development, as well as treatment response, leading to clinical implications in colorectal cancer prevention and management.
- MeSH
- Amines metabolism MeSH
- Antioxidants metabolism MeSH
- Adult MeSH
- Heterocyclic Compounds metabolism MeSH
- Carcinogenesis pathology MeSH
- Carcinogens metabolism MeSH
- Colorectal Neoplasms pathology MeSH
- Smokers statistics & numerical data MeSH
- Middle Aged MeSH
- Humans MeSH
- Non-Smokers statistics & numerical data MeSH
- Polycyclic Aromatic Hydrocarbons metabolism MeSH
- Prospective Studies MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Neoplasm Staging MeSH
- Gene Expression Profiling statistics & numerical data MeSH
- Intestinal Mucosa enzymology pathology MeSH
- Xenobiotics metabolism MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Observational Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- MeSH
- Asthma genetics immunology blood MeSH
- Child MeSH
- Gene Expression MeSH
- Financing, Organized MeSH
- Genetic Predisposition to Disease MeSH
- Infant MeSH
- Humans MeSH
- Infant, Newborn MeSH
- Child, Preschool MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Air Pollution adverse effects MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Humans MeSH
- Infant, Newborn MeSH
- Child, Preschool MeSH
BACKGROUND: To strengthen research and differential diagnostics of mitochondrial disorders, we constructed and validated an oligonucleotide microarray (h-MitoArray) allowing expression analysis of 1632 human genes involved in mitochondrial biology, cell cycle regulation, signal transduction and apoptosis. Using h-MitoArray we analyzed gene expression profiles in 9 control and 13 fibroblast cell lines from patients with F1Fo ATP synthase deficiency consisting of 2 patients with mt9205deltaTA microdeletion and a genetically heterogeneous group of 11 patients with not yet characterized nuclear defects. Analysing gene expression profiles, we attempted to classify patients into expected defect specific subgroups, and subsequently reveal group specific compensatory changes, identify potential phenotype causing pathways and define candidate disease causing genes. RESULTS: Molecular studies, in combination with unsupervised clustering methods, defined three subgroups of patient cell lines--M group with mtDNA mutation and N1 and N2 groups with nuclear defect. Comparison of expression profiles and functional annotation, gene enrichment and pathway analyses of differentially expressed genes revealed in the M group a transcription profile suggestive of synchronized suppression of mitochondrial biogenesis and G1/S arrest. The N1 group showed elevated expression of complex I and reduced expression of complexes III, V, and V-type ATP synthase subunit genes, reduced expression of genes involved in phosphorylation dependent signaling along MAPK, Jak-STAT, JNK, and p38 MAP kinase pathways, signs of activated apoptosis and oxidative stress resembling phenotype of premature senescent fibroblasts. No specific functionally meaningful changes, except of signs of activated apoptosis, were detected in the N2 group. Evaluation of individual gene expression profiles confirmed already known ATP6/ATP8 defect in patients from the M group and indicated several candidate disease causing genes for nuclear defects. CONCLUSION: Our analysis showed that deficiency in the ATP synthase protein complex amount is generally accompanied by only minor changes in expression of ATP synthase related genes. It also suggested that the site (mtDNA vs nuclear DNA) and the severity (ATP synthase content) of the underlying defect have diverse effects on cellular gene expression phenotypes, which warrants further investigation of cell cycle regulatory and signal transduction pathways in other OXPHOS disorders and related pharmacological models.
- MeSH
- Principal Component Analysis MeSH
- Cell Line MeSH
- Phenotype MeSH
- Fibroblasts MeSH
- Financing, Organized MeSH
- Genome, Mitochondrial MeSH
- Humans MeSH
- DNA, Mitochondrial genetics MeSH
- Mitochondrial Diseases MeSH
- Mitochondrial Proton-Translocating ATPases genetics deficiency MeSH
- Models, Genetic MeSH
- Oligonucleotide Array Sequence Analysis methods statistics & numerical data MeSH
- Sequence Deletion MeSH
- Cluster Analysis MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Check Tag
- Humans MeSH
BACKGROUND: In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. RESULTS: In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. CONCLUSION: CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways.
- MeSH
- Algorithms MeSH
- Biomarkers analysis MeSH
- Models, Biological MeSH
- Child MeSH
- Epidemiologic Methods MeSH
- Risk Assessment methods MeSH
- Data Interpretation, Statistical * MeSH
- Humans MeSH
- Computer Simulation MeSH
- Proteome analysis MeSH
- Risk Factors MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Models, Statistical MeSH
- Environmental Exposure analysis statistics & numerical data MeSH
- Air Pollution statistics & numerical data MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
BACKGROUND: All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels. RESULTS: This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference. An attempt is made to quantitatively assess the magnitude of this effect from real data. Some preliminary ideas are offered to mitigate the consequences of random signal aggregation in the analysis of gene expression data. CONCLUSION: Resulting from the summation of expression intensities over a random number of individual cells, the observed signals may not adequately reflect the true dependence structure of intra-cellular gene expression levels needed as a source of information for network reconstruction. Whether the reported effect is extrime or not, the important point, is to reconize and incorporate such signal source for proper inference. The usefulness of inference on genetic regulatory structures from microarray data depends critically on the ability of investigators to overcome this obstacle in a scientifically sound way. REVIEWERS: This article was reviewed by Byung Soo KIM, Jeanne Kowalski and Geoff McLachlan.
- MeSH
- Humans MeSH
- Models, Genetic MeSH
- Statistics, Nonparametric MeSH
- Oligonucleotide Array Sequence Analysis methods statistics & numerical data MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Computational Biology methods statistics & numerical data MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Research Support, N.I.H., Extramural MeSH
This paper describes a comparative systems level analysis of the developmental proteome and transcriptome in the model antibiotic-producing eubacterium Streptomyces coelicolor, cultured on different media. The analysis formulates expression as the superposition of effects of regulatory networks and biological processes which can be identified using singular value decomposition (SVD) of a data matrix formed by time series measurements of expression of individual genes throughout the cell cycle of the bacterium. SVD produces linearly orthogonal factors, each of which can represent an independent system behavior defined by a linear combination of the genes/proteins highly correlated with the corresponding factor. By using SVD of the developmental time series of gene expression, as measured by both protein and RNA levels, we show that on the highest level of control (representing the basic kinetic behavior of the population), the results are identical, regardless of the type of experiment or cultivation method. The results show that this approach is capable of identifying basic regulatory processes independent of the environment in which the organism lives. It also shows that these processes are manifested equally on protein and RNA levels. Biological interpretation of the correlation of the genes and proteins with significant eigenprofiles (representing the highest level kinetic behavior of protein and/or RNA synthesis) revealed their association with metabolic processes, stress responses, starvation, and secondary metabolite production.
- MeSH
- Bacterial Proteins genetics metabolism MeSH
- RNA, Bacterial genetics metabolism MeSH
- Citric Acid Cycle MeSH
- Financing, Organized MeSH
- Data Interpretation, Statistical MeSH
- RNA, Messenger genetics metabolism MeSH
- Heat-Shock Proteins genetics metabolism MeSH
- Proteomics methods statistics & numerical data MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Streptomyces genetics metabolism growth & development MeSH
- Systems Biology MeSH
- Publication type
- Comparative Study MeSH
A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.
