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A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

ABSTRACT: BACKGROUND: The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. RESULTS: In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method). This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS) and Progression Score (PS) in progression analysis, True Positive Rate (TPR) in gene pair analysis, and Pathway Enrichment Score (PES) in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From this research, several gene interaction networks inferred could provide clues for the mechanism of prostate cancer progression. CONCLUSION: The SIG method reliably identifies cancer progression correlated gene pairs, and performs well both in gene pair ontology analysis and in pathway enrichment analysis. This method provides an effective means of understanding the molecular mechanism of carcinogenesis by appropriately tracking down the process of cancer progression.

SUBMITTER: Mo WJ

PROVIDER: S-EPMC2737000 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

Mo Wen Juan WJ Fu Xu Ping XP Han Xiao Tian XT Yang Guang Yuan GY Zhang Ji Gang JG Guo Feng Hua FH Huang Yan Y Mao Yu Min YM Li Yao Y Xie Yi Y

BMC genomics 20090729

<h4>Background</h4>The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression.<h4>Results</h4>In this investigation we ...[more]

PMID: 19640296

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Project description: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".

Project description:Temporal variation in demographic processes can greatly impact population dynamics. Perturbations of statistical coefficients that describe demographic rates within matrix models have, for example, revealed that stochastic population growth rates (log(? s)) of fast life histories are more sensitive to temporal autocorrelation of environmental conditions than those of slow life histories. Yet, we know little about the mechanisms that drive such patterns. Here, we used a mechanistic, functional trait approach to examine the functional pathways by which a typical fast life history species, the macrodetrivore Orchestia gammarellus, and a typical slow life history species, the reef manta ray Manta alfredi, differ in their sensitivity to environmental autocorrelation if (a) growth and reproduction are described mechanistically by functional traits that adhere to the principle of energy conservation, and if (b) demographic variation is determined by temporal autocorrelation in food conditions. Opposite to previous findings, we found that O. gammarellus log(? s) was most sensitive to the frequency of good food conditions, likely because reproduction traits, which directly impact population growth, were most influential to log(? s). Manta alfredi log(?s ) was instead most sensitive to temporal autocorrelation, likely because growth parameters, which impact population growth indirectly, were most influential to log(? s). This differential sensitivity to functional traits likely also explains why we found that O. gammarellus mean body size decreased (due to increased reproduction) but M. alfredi mean body size increased (due to increased individual growth) as food conditions became more favorable. Increasing demographic stochasticity under constant food conditions decreased O. gammarellus mean body size and increased log(? s) due to increased reproduction, whereas M. alfredi mean body and log(? s) decreased, likely due to decreased individual growth. Our findings signify the importance of integrating functional traits into demographic models as this provides mechanistic understanding of how environmental and demographic stochasticity affects population dynamics in stochastic environments.

Project description:A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV) or ionizing radiation (IR)-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying biological processes affected by IR- and/or UV- induced DNA damage.EPIG competed with CLICK and performed better than CAST in extracting patterns from simulated data. EPIG extracted more biological informative patterns and co-expressed genes from both C. elegans and IR/UV-treated human fibroblasts. Using Gene Ontology analysis of the genes in the patterns extracted by EPIG, several key biological categories related to p53-dependent cell cycle control were revealed from the IR/UV data. Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition. EPIG can be applied to data sets from a variety of experimental designs.

Dataset Information

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

Publications

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

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