COMPUTATIONAL EVIDENCE FROM TWO CORRELATED DATA SOURCES AT DIFFERENT MOLECULAR LEVELS FOR AF-VHD-SPECIFIC MICRORNA SIGNATURE

The important roles of microRNAs (miRNAs) in the pathological process of the cardiovascular system have been recognized. However, identification of miRNAs related to valvular heart disease with atrial fibrillation (AF-VHD) has been difficult and very slow because of complex pathological mechanism of AF-VHD. Analysis of microarray expression profiles provides the possibility to rapid prediction of disease-regulating miRNAs and can lay a theoretical foundation for further experimental studies. A computational method is proposed to predict AF-VHD-specific miRNAs by combining miRNA and gene expression data, which are strongly correlated. Using the proposed method, a 45-miRNA AF-VHD-specific signature is predicted. Compared with other related results, 15 of 45 miRNAs are the same and the rest 30 miRNAs are different. Our analysis shows that 11 of 30 new miRNAs are associated with the diseases inducing AF-VHD and the remaining 19 miRNAs have good combinational discrimination power. Therefore, the AF-VHD signature we have predicted is confirmed to be reliable and specific. In a word, this study proposes an effective computational strategy in prediction of disease-regulating miRNAs and finds some AF-VHD-specific miRNAs, which provides new insight into the further experimental study and molecular mechanism leading to the development of AF-VHD.