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Title: Genomic and transcriptomic analysis of gastric cancer: Systematic studies on transcriptional bias in aneuploidy and gene coexpression meta-network
Keywords: Gastric Cancer, Microarrays, Aneuploidy, Wavelet Transforms, Gene Coexpression Network
Issue Date: 19-Sep-2006
Citation: AMIT AGGARWAL (2006-09-19). Genomic and transcriptomic analysis of gastric cancer: Systematic studies on transcriptional bias in aneuploidy and gene coexpression meta-network. ScholarBank@NUS Repository.
Abstract: Whole-genome sequencing projects have imparted much of the initial momentum for genome-wide studies, but it is microarrays and their application to cancer that has proved instrumental in establishing the power of the global view of genetics. Collections of global a??microarray snapshotsa?? of the biological activity at molecular-level in the biological samples are now providing detailed characterizations and aiding in attaining an improved understanding of cancer. A key challenge now lies is in developing statistical and computational techniques that can extract biologically meaningful information from colossal amounts of data generated by the global transcription profiling studies. This thesis deals with developing two new methods to investigate the expression profiles of cancers. First, the existence of transcriptional bias in the regions of aneuploidy is addressed by showing pervasive imprinting of aneuploidy on the cancer transcriptome by reconstructing portraits of chromosomal aberrations using an individual tumora??s gene expression profile. A signal processing technique called wavelet transform is applied to a series of genomically arranged expression profiles to identify regions of coordinated transcription. These regions were subsequently shown to coincide with regions of aneuploidy. It is suggested that aneuploidy may contribute to tumor behavior by subtly altering the expression levels of hundreds of genes in the oncogenome. Second, a probabilistic methodology to construct a gastric cancer coexpression network is developed using genes that behave similarly across multiple datasets from disparate expression profiling platforms. The gene-gene coexpression interactions from different expression datasets of gastric cancer are systematically coalesced into a single unified coexpression interaction matrix. Subsequently a network is deduced and methodically explored at the level of network topology and functional modules. The cellular pathways and biological processes regulating the behavior of gastric cancer are described and its applicability to gene functional discovery is also shown through a case study. The methodologies developed in thesis, although, specific to gastric cancers, are applicable to other cancers as well.
Appears in Collections:Ph.D Theses (Open)

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