Characterization of Genomic Modulation in Cancers
Date Issued
2015
Date
2015
Author(s)
Chiu, Yu-Chiao
Abstract
Differential network biology, an emerging field in regulatory biology, studies the dynamics of genomic regulatory networks among cellular conditions. From the viewpoint of genomic modulation, such differential regulation can be modulated by modulator genes. Due to computational complexity, a systematic exploration into genomic modulation on a genome-wide scale has not been carried out. The thesis aims to comprehensively investigate the landscape of modulated genomic regulation and to pave the way to better understanding complex interactomes of cancers. Specifically, we proposed several bioinformatics methods to study three forms of genomic modulation characterized by the quantitative features of modulators: two-state, multi-state, and continuous modulation. In the study of two-state modulation, we started by investigating the best known modulator, estrogen receptor (ER). We devised a novel statistical inference that greatly improved computation efficiency from previous methods. Analyzing ER modulated gene and gene set (representing biological functions) interactions, we identified a ER+ dependent interaction between TGF and NFB, which was associated with patients’ prognosis. In acute myeloid leukemia, we investigated a novel role of NPM1 gene mutation as a modulator in microRNA (miRNA)-mRNA regulation (MMR). Significant pairwise correlation of hundreds of MMR pairs was seen specifically in NPM1-wild type patients, corroborated in independent cohorts and in vitro models. We further showed that the dynamic regulations of nine MMR pairs were independent predictors of prognosis. As for the multi-state genomic modulation, we studied a layer of gene-gene coexpression through the competition for common targeting miRNAs, namely the competing endogenous RNA (ceRNA) regulation. Our analysis indicated that ceRNA regulation was modulated by miRNAs. We investigated the modulation of miRNAs in ceRNA regulation in GBM and showed its involvement in synaptic transmission and tumor-related functions. Furthermore, we identified that the regulatory strength, rather than expressional abundance, of three immune response genes was predictive of survival. We proposed a regression model to analyze the gene regulatory networks modulated by continuous-state genomic features, including miRNAs and transcription factors. Applying the method to expression profiles of GBM, our results, again, suggested modulated regulation is involved in essential cellular processes. Our extended analysis to study the interactions among multiple modulator genes by a multiple regression further revealed the complex co-modulation between ESR1 and ERBB2 genes in breast cancer. In conclusion, we comprehensively investigated genomic modulation in cancers and illuminated its significance in cellular functions and potential as prognostic biomarkers, contributing to a better understanding of complex cancer genomes and interactomes.
Subjects
Cancer
genomic modulation
differential network biology
gene regulatory network
bioinformatics
SDGs
Type
thesis