Inference of epigenetic subnetworks and expression-based analysis using a breast cancer dataset

• Author / Creator

  Jing, Anqi

• Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation. However, the knowledge about the degree to which epigenetic changes might cause differences in gene expression in cancer is currently lacking. By considering the change of gene expression as the response of altered DNA methylation, we introduce a novel analytical framework to identify epigenetic subnetworks in which the methylation status of a set of highly correlated genes is predictive of a set of gene expression. By detecting highly correlated modules as representatives of the regulatory scenario underling the gene expression and DNA methylation, the dependency between DNA methylation and gene expression is explored by a Bayesian regression model with the incorporation of g-prior followed by a strategy of an optimal predictor subset selection. The subsequent network analysis indicates that the detected epigenetic subnetworks are highly biologically relevant and contain many verified epigenetic causal mechanisms. Moreover, a survival analysis indicates that they might be effective prognostic factors associated with patient survival time. The alterations in gene expression are often ignored as stochastic noises, specifically those arising from variations in transcriptional regulation or biochemical modifications within cells. To evaluate if such alterations contribute to cancer progression, we performed an expression-based analysis to detect exclusively expression-altered (EEA) genes, i.e., genes with altered expression not caused by genetic mutations, and we investigated the pattern of their aberrant expression in breast cancer. Based on these investigations, we found that the alterations in EEA genes are instigated by hypoxia-related molecular events, predominantly in two groups of genes that control chromosomal instability (CIN) and remodel tumor microenvironment (TME). We conclude that alterations are not stochastic and that hypoxia induces CIN and TME remodeling to permit further tumor progression.

• Subjects / Keywords

  • Bayesian regression
  • Bioinformatics
  • Gene expression alterations
  • Epigenetic subnetworks

• Graduation date

  Fall 2018

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3959CQ2Q

• License

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