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

Abstract

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

Gene expression and DNA methylation analyses suggest that immune process‐related ADCY6 is a prognostic factor of luminal‐like breast cancer

“…24 The evidence indicate that DNA hypermethylation is a common phenomenon in the development of breast cancer. [14][15][16] It always prevents the expression of key genes, including those involved in cell cycle regulation, tumor susceptibility, tumor metabolic enzymes and with cell adhesion molecules, among others. 25 Hypermethyla-Hypermethylation often inhibits the expression of downstream genes, while hypomethylation often promotes the expression of downstream genes.…”

“…[11][12][13] In addition, many studies have shown that the state of DNA methylation in breast cancer tissues is closely related to the heterogeneity of breast cancer. [14][15][16] However, the relationship between DNA methylation and the tumor-immune microenvironment of breast cancer remains to be elucidated. This study explored the relationship among DNA methylation, gene expression profile and tumor-immune microenvironment in luminal-like breast cancer, and identified ADCY6 as a key gene that regulates immune cell infiltration of luminal-like breast cancer.…”

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

“…To better understand the complex biological mechanism underlying cancer progression and prognosis, a comprehensive analysis using multi-omics data has attracted great attention to reveal the distinctive and shared molecular features of cancers. Many multi-omics studies have been conducted to discover novel biomarkers associated with cancers and predict clinical outcomes precisely (Huang et al, 2017;Kim et al, 2014;Lee et al, 2017;Shivakumar et al, 2017;Sohn et al, 2013;El-Manzalawy et al, 2018;Kim et al, 2015bKim et al, , 2012. For a comprehensive analysis of multi-omics data, it is crucial to understand the complex interplay between genes across different omics layers.…”

Evolution of Translational Bioinformatics: lessons learned from TBC 2016

“…Lee et al (Ajou University, Korea) propose a novel method that incorporates subtype information to better explain the variability in gene expression based on the methylation profile [3]. They address the issue of infeasibility of separately estimating association patterns between gene expressions and DNA methylation features by employing a kernel-weighted lasso model that can incorporate information from samples in different subtypes while allowing subtype-specific estimations.…”