Unravelling the Hidden Relationship Between Subtype of Ion Channel and Channlopathy Based on CTWC Approach

Ion channels are important in many important physiological processes such as sensory transduction, action-potential generation and muscle contraction. Cardiomyopathy is a complex and multi-gene disease which hasn´t been systematically analyzed by the perspective of ion channel genes. The aim of this study was to develop a bioinformatics approach to seek the transcriptional features leading to the hidden subtyping of a complex clinical phenotype. The basic strategy was to iteratively partition in two ways sample and feature space with super-paramagnetic clustering technique and to seek for hard and robust gene clusters that lead to a natural partition of disease samples and that have the highest functionally biological interaction network evaluated with PathwayStudio. Based on a novel functional evaluation measure, we select ion channel gene clusters which can partition samples well, but traditional ion channel classes cannot overcome this problem. The results showed that the proposed algorithm is a promising computational strategy for peeling off the hidden genetic heterogeneity based on ion channel transcriptionally profiling channelopathy disease samples, which may lead to an improved diagnosis and treatment of cancers.