MOTIVATION: A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable biomarkers. Importantly, the integration of both expression and epigenetic alterations is more likely to identify relevant biomarkers. RESULTS: We present a new analysis framework, using ESCC progression-associated gene regulatory network (GRN escc), to identify differentially methylated CpG sites prognostic of ESCC progression. From the CpG loci differentially methylated in 50 tumor-normal pairs, we selected 44 CpG loci most highly associated with survival and located in the promoters of genes more likely to belong to GRN escc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRN escc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRN escc were correlated with patient survival. We further characterized the GRN escc network topology and observed that the genes with methylated CpG loci associated with survival deviated from the center of mass and were less likely to be hubs in the GRN escc. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies, especially with partial genome coverage.

BACKGROUND: Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. RESULTS: We propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The similarity between paired probe sequences is quantified as a bridge to connect these datasets together. The ABs can be subsequently identified from an "improved" common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Our approach is evaluated with two sets of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma datasets. Based on an unbiased reciprocal test, we demonstrate that each gene in a group of ABs is required to maintain high cancer sample classification accuracy, and we observe that ABs are not limited to genes common to all platforms. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that our ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions. CONCLUSIONS: The proposed meta-analysis method called MiningABs is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment. Java source code, tutorial, example and related materials are available at "http://sourceforge.net/projects/miningabs/".

BACKGROUND: Observation of gene expression changes implying gene regulations using a repetitive experiment in time course has become more and more important. However, there is no effective method which can handle such kind of data. For instance, in a clinical/biological progression like inflammatory response or cancer formation, a great number of differentially expressed genes at different time points could be identified through a large-scale microarray approach. For each repetitive experiment with different samples, converting the microarray datasets into transactional databases with significant singleton genes at each time point would allow sequential patterns implying gene regulations to be identified. Although traditional sequential pattern mining methods have been successfully proposed and widely used in different interesting topics, like mining customer purchasing sequences from a transactional database, to our knowledge, the methods are not suitable for such biological dataset because every transaction in the converted database may contain too many items/genes. RESULTS: In this paper, we propose a new algorithm called CTGR-Span (Cross-Timepoint Gene Regulation Sequential pattern) to efficiently mine CTGR-SPs (Cross-Timepoint Gene Regulation Sequential Patterns) even on larger datasets where traditional algorithms are infeasible. The CTGR-Span includes several biologically designed parameters based on the characteristics of gene regulation. We perform an optimal parameter tuning process using a GO enrichment analysis to yield CTGR-SPs more meaningful biologically. The proposed method was evaluated with two publicly available human time course microarray datasets and it was shown that it outperformed the traditional methods in terms of execution efficiency. After evaluating with previous literature, the resulting patterns also strongly correlated with the experimental backgrounds of the datasets used in this study. CONCLUSIONS: We propose an efficient CTGR-Span to mine several biologically meaningful CTGR-SPs. We postulate that the biologist can benefit from our new algorithm since the patterns implying gene regulations could provide further insights into the mechanisms of novel gene regulations during a biological or clinical progression. The Java source code, program tutorial and other related materials used in this program are available at http://websystem.csie.ncku.edu.tw/CTGR-Span.rar.

BACKGROUND: Frequent pattern mining analysis applied on microarray dataset appears to be a promising strategy for identifying relationships between gene expression levels. Unfortunately, too many itemsets (co-expressed genes) are identified by this analysis method since it does not consider the importance of each gene within biological processes to a cellular response and does not take into account temporal properties under biological treatment-control matched conditions in a microarray dataset. RESULTS: We propose a method termed TIIM (Top-k Impactful Itemsets Miner), which only requires specifying a user-defined number k to explore the top k itemsets with the most significantly differentially co-expressed genes between 2 conditions in a time course. To give genes different weights, a table with impact degrees for each gene was constructed based on the number of neighboring genes that are differently expressed in the dataset within gene regulatory networks. Finally, the resulting top-k impactful itemsets were manually evaluated using previous literature and analyzed by a Gene Ontology enrichment method. CONCLUSIONS: In this study, the proposed method was evaluated in 2 publicly available time course microarray datasets with 2 different experimental conditions. Both datasets identified potential itemsets with co-expressed genes evaluated from the literature and showed higher accuracies compared to the 2 corresponding control methods: i) performing TIIM without considering the gene expression differentiation between 2 different experimental conditions and impact degrees, and ii) performing TIIM with a constant impact degree for each gene. Our proposed method found that several new gene regulations involved in these itemsets were useful for biologists and provided further insights into the mechanisms underpinning biological processes. The Java source code and other related materials used in this study are available at "http://websystem.csie.ncku.edu.tw/TIIM_Program.rar".

Sequential pattern mining techniques have been widely used in different topics of interest, such as mining customer purchasing sequences from a transactional database. Notably, observation of gene expressions to discover gene regulations during biological or clinical progression via microarray approaches has become the dominant trend. By converting microarray datasets into the format of transactional databases, sequential patterns implying gene regulations could be identified. However, there exists no effective method in current studies that can handle such kind of dataset as every transaction may contain too many items/genes and the resultant patterns are very susceptible to item order. We propose a new method called CTGR-Span (Cross-Timepoint Gene Regulation Sequential Patterns) to efficiently mine CTGR-SPs (cross-timepoint gene regulation sequential patterns). The proposed method was experimented with two publicly available human time course microarray datasets and it outperformed traditional methods over 2,000 times in terms of the execution efficiency. Furthermore, via a Gene Ontology enrichment analysis, the resultant patterns are more meaningful biologically compared to previous literature reports. Hence, it could provide biologists more insights into the mechanisms of novel gene regulations in certain disease progressions.

MOTIVATION: Association rule analysis methods are important techniques applied to gene expression data for finding expression relationships between genes. However, previous methods implicitly assume that all genes have similar importance, or they ignore the individual importance of each gene. The relation intensity between any two items has never been taken into consideration. Therefore, we proposed a technique named REMMAR (RElational-based Multiple Minimum supports Association Rules) algorithm to tackle this problem. This method adjusts the minimum relation support (MRS) for each gene pair depending on the regulatory relation intensity to discover more important association rules with stronger biological meaning. RESULTS: In the actual case study of this research, REMMAR utilized the shortest distance between any two genes in the Saccharomyces cerevisiae gene regulatory network (GRN) as the relation intensity to discover the association rules from two S.cerevisiae gene expression datasets. Under experimental evaluation, REMMAR can generate more rules with stronger relation intensity, and filter out rules without biological meaning in the protein-protein interaction network (PPIN). Furthermore, the proposed method has a higher precision (100%) than the precision of reference Apriori method (87.5%) for the discovered rules use a literature survey. Therefore, the proposed REMMAR algorithm can discover stronger association rules in biological relationships dissimilated by traditional methods to assist biologists in complicated genetic exploration.