| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 100 === Pathway analysis of high-throughput genomic data generated by microarray has been increasingly prevalent in exploring the underlying molecular mechanisms and complex cancer progression of biological systems. Considering that the upstream genes play more important roles than downstream genes do in signaling pathways, utilization of topology information may identify pathways that are more related to conditions being studied, but it is still in early stage and rather incomplete. In addition, even though microRNA has been reported to play a key role in diverse cellular functions by down-regulating its target mRNAs at the post-transcriptional level, only little research in pathway analysis incorporated with microRNA. Therefore, the purpose of this study is to improve the pathway analysis for a better understanding of underlying mechanisms by integrating microRNA and mRNA microarray profiling in a topology-based approach at the systems biology level.
An improved approach, named MiR-PathOgen, is developed as a MATLABR-based application. MiR-PathOgen consists of a reconstructed miRNA-integrated pathway database and three algorithms, including an over-representation analysis, an impact factor method and the reliability of a system. To construct a microRNA-integrated pathway database, we first collected pathway information from BioCarta and pathway interaction database, as well as a list of validated microRNA-gene interactions from miRecords and TarBase. Next, over-representation analysis was executed using hypergeometric distribution. The impact factor method incorporated several biological factors, such as regulatory relationship, gene positions and expression changes. Lastly, the results of above analyses were combined by concepts of the reliability of a system to conduct an overall score for pathway rankings.
The simulation results of the impact factor analysis illustrated that this topology-based method was able to robustly report the pathways in condition of connected or central distributed microRNAs and genes with higher ranking. Also, the combination of the over-representation analysis and the impact factor analysis at the system level performed a reliable systematical transformation to identify significant pathways. Furthermore, applying our algorithms in three human datasets suggested that MiR-PathOgen is capable of identifying microRNA and gene activated pathways that were related to a given condition being studied. In summary, MiR-PathOgen is a reliable and reproducible pathway analysis tool to concurrently analyze microRNA and mRNA genomic data, which is beneficial for a better understanding of complex biological systems.
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