| Summary: | 碩士 === 慈濟大學 === 醫學資訊學系碩士班 === 100 === MicroRNAs (miRNAs) are a class of small noncoding (19- to 24-nucleotide) RNAs that regulate the expression of target mRNAs at the post-transcriptional level. In recent years, many researches indicate that miRNAs play an important role in the prognosis of breast cancer. The change of miRNAs expression profiles can be reflected in gene expression profiles in many cases; therefore, it provides a new venue to explore miRNA biomarkers using gene expression profiles.
Recently, a method called regulator effect score (RE-score) is developed to calculate the activities of miRNAs. The RE-score method calculates the average difference of expression values between target genes and non-target genes. This method, however, does not take into consideration the co-reaction (or interaction) of miRNAs themselves, and also neglect the change among gene expression. Meanwhile, it assumes that every miRNA regulate their target genes in the same level.
In this study, we propose a new method, we called it regulator contribution score (RC-score), in order to improve the disadvantages of RE-score. RC-score calculates the weight of every miRNA from gene expression profile of their target genes. RC-score reflects the contribution of every miRNA in the change of gene expression due to their individual inhibition ability to mRNA and interaction among miRNAs themselves. We then explore the potential important biomarkers in breast cancer prognosis using these two scores. The key biomarkers identified from both RE-score and RC-score are then further used to predict the performance of breast cancer prognosis outcome. On the other hand, we also develop a network topology to explore the relationship of those important miRNA and gene biomarkers.
For calculating the activity scores of each miRNA, we use two gene sets, one is 50 significant genes selected by combining T-test and target prediction, the other is 20 literature genes from OMIM. The top 25 miRNAs are selected from both gene sets using RE-score. As a result, we identified that there are 11 miRNAs of these 25 miRNAs are true biomarkers that have been evidenced by medical literature. Furthermore, the accuracy of breast cancer prognosis using the top 50 miRNAs and clinical data can reach 93.2% in GASVM classifier.
Another important result comes from the analysis of network topology. The network of top 25 miRNAs with the lowest p-value calculated from RE-score using 20 literature gene set, shows that the average of 5.88 literature genes are connected by each miRNA, that is 1.19 genes higher than the network from all miRNAs .
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