Application Of Molecular Network Analysis In The Discovery Of Cancer Biomarkers And The Exploration Of Evolutionary Mechanisms Of Gene Interaction Networks

It is a main paradigm of current biological research for the systems biological research strategy based on the analysis of molecular networks. The rapid development of biological techniques and the large amount of biological data it brings about, and the increasing maturity of systems biological research approaches, provide us the opportunity to conduct systems biological studies based on molecular network analysis in a large number of biological fields. In this article, I will introduce the studies on cancer research and evolutionary biology based on the construction and analysis of molecular networks.The early diagnosis of cancer is critical to the prevention and treatment of cancer, therefore the detection of accurate and effective cancer diagnosis biomarkers is of great importance. Through integrating both mi RNA and m RNA expression profiling data, and mi RNA-m RNA regulation data, we developed a novel bioinformatics pipeline for the prediction of cancer mi RNA biomarkers, and implemented this pipeline with both Java and R programming languages. Afterwards, we applied our proposed algorithm to detect potential mi RNA biomarkers in prostate cancer, and the prediction results were confirmed by both low-throughput experiments and systematic analysis. By continuously improving and updating our algorithm, we also applied our pipeline in the studies for other types of cancer, such as clear cell renal cell carcinoma, and obtained desirable research results.The study on the evolution process of gene interaction networks is a critical point in evolutionary biology, and also an important approach to explore some big open questions, like the biological phenotypic evolution and even for the origin of species. In this study, we tried to probe the evolutionary pattern of gene interaction networks in mammals(both Human and Mouse) from a new angle of new genes. We found a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identified a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explored the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.