Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.

摘要: Breast cancer is a complex disease in which different genomic patterns exists depending on subtypes. Recent researches present that multiple subtypes of breast occur at rates, and play crucial role planning treatment. To better understand underlying biological mechanisms subtypes, investigating the specific gene regulatory system via desirable. Gene expression, as an intermediate phenotype, estimated based methylation profiles to identify impact epigenomic features transcriptomic changes cancer. We propose kernel weighted l1-regularized regression model incorporate tumor subtype information further reveal regulations affected by For proper control subtype-specific estimation, samples from are learned rate target estimates. Kolmogorov Smirnov test conducted determine learning each sample subtype. It observed genes might be sensitive show prediction improvement when using our proposed method. Comparing standard method, overall performance also enhanced incorporating In addition, we identified network structures associations between expression DNA methylation. this study, lasso for identifying expressions profiles. Identification associated with helpful treatment developing new therapies.