EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

Theoretical cross-comparative analysis on dynamics of small intestine and colon crypts during cancer initiation.

Epigenetics is emerging as a fundamentally important area of biological and medical research that has implications for our understanding of human diseases including cancer, autoimmune and neuropsychiatric disorders. In the context of recent efforts on personalised medicine, a novel research direction is concerned with identification of intra-individual epigenetic variation linked to disease predisposition and development, i.e. epigenome-wide association studies. A computational model has been developed to describe the dynamics and structure of human intestinal crypts and to perform a comparative analysis on aberrant DNA methylation level induced in these during cancer initiation. The crypt framework, AgentCrypt, is an agent-based model of crypt dynamics, which handles intra- and inter-dependencies. In addition, the AgentCrypt model is used to investigate the effect of a set of potential inhibitors with respect to methylation modification in intestinal tissue during initiation of disease. Methylation level decrease over a relatively short period of 90 days is marked for the colon compared to the small intestine, although similar alterations are induced in both tissues. In addition, inhibitor effect is notable for abnormal crypt groups, with largest average methylation differences observed ≈0.75% lower in the colon and ≈0.79% lower in the small intestine with inhibitor present.

Comparative Correlation Structure of Colon Cancer Locus Specific Methylation: Characterisation of Patient Profiles and Potential Markers across 3 Array-Based Datasets.

Abnormal DNA-methylation is well known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Recent years have seen the increased use of large-scale technologies, (such as methylation microarray assays or specific sequencing of methylated DNA), to determine whole genome profiles of CpG island methylation in tissue samples. Comprehensive study of methylation array data from transcriptome high-throughput platforms permits determination of gene methylation markers, important for cancer profiling. Here, three large-scale methylation datasets for colon cancer have been compared to determine locus-specific methylation agreement. These data are from the GEO database, where colon cancer and apparently healthy adjacent tissues are represented by sample sizes 125 and 29 respectively in the first dataset, 24 of each in the second and 118 of each in the third. Several data analysis techniques have been employed, including Clustering, Discriminant Principal Component Analysis, Discriminant Analysis and ROC curves, in order (i) to obtain a better insight on the locus-specific concomitant methylation structures for these diverse data and (ii) to determine a robust potential marker set for indicative screening, drawn from all data taken together. The extent of the agreement between the analysed datasets is reported. Further, potential screening methylation markers, for which methylation profiles are consistent across tissue samples and several datasets, are highlighted and discussed.

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications.

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

A computational model for genetic and epigenetic signals in colon cancer.

Cancer, a class of diseases, characterized by abnormal cell growth, has one of the highest overall death rates world-wide. Its development has been linked to aberrant genetic and epigenetic events, affecting the regulation of key genes that control cellular mechanisms. However, a major issue in cancer research is the lack of precise information on tumour pathways; therefore, the delineation of these and of the processes underlying disease proliferation is an important area of investigation. A computational approach to modelling malignant system events can help to improve understanding likely "triggers", i.e. initiating abnormal micro-molecular signals that occur during cancer development. Here, we introduce a network-based model for genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Additionally, we describe a case study on tumour progression recorded for two gene networks on colon cancer, carcinoma in situ. Our results to date showed that tumour progression rate is higher for a small, closely-associated network of genes than for a larger, less-connected set; thus, disease development depends on assessment of network properties. The current work aims to provide improved insight on the way in which aberrant modifications characterize cancer initiation and progression. The framework dynamics are described in terms of interdependencies between three main layers: genetic and epigenetic events, gene relationships and cancer stage levels.

Colon cancer associated genes exhibit signatures of positive selection at functionally significant positions.

BACKGROUND: Cancer, much like most human disease, is routinely studied by utilizing model organisms. Of these model organisms, mice are often dominant. However, our assumptions of functional equivalence fail to consider the opportunity for divergence conferred by ~180 Million Years (MY) of independent evolution between these species. For a given set of human disease related genes, it is therefore important to determine if functional equivalency has been retained between species. In this study we test the hypothesis that cancer associated genes have different patterns of substitution akin to adaptive evolution in different mammal lineages. RESULTS: Our analysis of the current literature and colon cancer databases identified 22 genes exhibiting colon cancer associated germline mutations. We identified orthologs for these 22 genes across a set of high coverage (>6X) vertebrate genomes. Analysis of these orthologous datasets revealed significant levels of positive selection. Evidence of lineage-specific positive selection was identified in 14 genes in both ancestral and extant lineages. Lineage-specific positive selection was detected in the ancestral Euarchontoglires and Hominidae lineages for STK11, in the ancestral primate lineage for CDH1, in the ancestral Murinae lineage for both SDHC and MSH6 genes and the ancestral Muridae lineage for TSC1. CONCLUSION: Identifying positive selection in the Primate, Hominidae, Muridae and Murinae lineages suggests an ancestral functional shift in these genes between the rodent and primate lineages. Analyses such as this, combining evolutionary theory and predictions - along with medically relevant data, can thus provide us with important clues for modeling human diseases.

Computational micromodel for epigenetic mechanisms.

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach.

Cell type-dependent, infection-induced, aberrant DNA methylation in gastric cancer.

Epigenetic changes correspond to heritable modifications of the chromatin structure, which do not involve any alteration of the DNA sequence but nonetheless affect gene expression. These mechanisms play an important role in cell differentiation, but aberrant occurrences are also associated with a number of diseases, including cancer and neural development disorders. In particular, aberrant DNA methylation induced by H. Pylori has been found to be a significant risk factor in gastric cancer. To investigate the sensitivity of different genes and cell types to this infection, a computational model of methylation in gastric crypts is developed. In this article, we review existing results from physical experiments and outline their limitations, before presenting the computational model and investigating the influence of its parameters.