Hierarchical Structural Component Models for Integrative Analysis of miRNA and mRNA expression data

Abstract

Identification of multi-markers is one of the most challenging issues in this new era of personalized medicine. Although many methods have been developed to identify candidate markers for each type of omics data, few can facilitate multi-marker identification. It is well known that microRNAs (miRNAs) affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration (HisCoM-mimi) model that accounts for this biological relationship, to efficiently study and identify such integrated markers.<br/> In this thesis, we suggest two types of HisCoM-mimi. First type of HisCoM-mimi is used for discriminant analysis. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods.<br/> Second type of HisCoM-mimi is used for survival analysis (mimi-surv). As the result of comparison study of HisCoM-mimi for discriminant analysis, we found the statistical power of mimi-surv to be better than other models in simulated comparisons. In analysis of real clinical data, mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of PDAC patients. Interestingly, miR-93, a previously unidentified PDAC-related miRNA, was found by mimi-surv, both in patient data from Seoul National University Hospital and The Cancer Genome Atlas (TCGA). Also, methods that use known structure for miRNA-mRNA regularization, found more PDAC related miRNAs than others.<br/> As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for diagnosis or prognosis of cancer, providing a much broader biological interpretation<br/>