| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 102 === With well-developed technology in microarray, using statistical tests to analyze microarray data is one of the most common strategies to identify differentially expressed genes. In recent years, tremendous efforts have been made to discover gene regulation among differentially expressed genes to provide insights into the molecular mechanisms and effective prognosis in cancers. However, such gene interactions have focused on certain static cellular conditions, ignoring the fact that interaction among genes in cells is a dynamic process. Dynamic gene regulation networks analysis provide better interpretation and understanding for complex biological phenomena. In cancers, the dynamic gene regulation can be modulated by some key modulator genes. That is some inter-gene regulation can be strengthened (or weakened) specifically, when certain modulator genes are over- or under-expressed. Some oncogenes or tumor suppressors have been reported to perform their function through acting as modulator genes, such as estrogen receptor (ER) in breast cancers. Several computational methods were developed to dissect the modulated regulatory networks under single modulator gene. However, systematic and genome-wide screening of novel modulator genes is not previously explored.
In the study we developed a statistical model based on Fisher transformation analysis pipeline for comprehensively identifying key modulator genes from genome-wide expression profiling of breast cancer. We designed two parameters, connectivity (Con), and degree of genome-wide change (DGC), to measure the modulation strength. In our data, ESR1, the most well-studied modulator gene, exhibited significant power of modulation in both parameters, revealing that the proposed model was sensitive and capable of identifying modulators. Therefore, we identified 237 key modulator genes, 51 of them with higher power than ESR1. In order to statistically evaluate the robustness of the proposed model, we employed the normalized Canberra distance to measure similarity among the ranked lists of modulator genes in the discovery dataset and three independent validation datasets.
For biological interpretation of these 237 identified key modulators, regulatory networks governed by these modulators were constructed. We identified novel regulation and functional interaction of hub gene under specific modulator gene status. Furthermore, 15 survival-associated modulator genes, which dependent on modulator gene status were identified and verified in the validation datasets.
In summary, we provided a comprehensive analysis of measuring gene regulation by each modulator gene and constructed modulated gene regulation networks in breast cancer. We expect that the modulator identification of the gene interactions will further enhance our understanding in gene interaction and systemic influence to discover complex genetic regulations in cancers.
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