Obesity, Prostate Cancer and DNA methylation
Not stated
- Funding
- Competition Funded PhD Project (Students Worldwide)
- Application deadline
- Year-round applications
About the project
About the Project Prostate Cancer is the most common cancer in men in the US and UK. Obesity is a known risk factor for many cancers and levels of obesity are increasing. Despite the disease and potential risk factor being so common, the evidence for obesity as a risk factor for prostate cancer is inconsistent – as evidenced by a recent meta-analysis (despite the meta-analysis itself not being entirely satisfactory). DNA methylation involves the addition of methyl groups to certain positions in the genome that contribute to the regulation of genetic activity. DNA methylation patterns can be copied from one cell to another, but differ between cell types as part of the ‘epigenetic’ code that allows e.g. a prostate cell to appear and behave differently from a skin cell behaviour despite both cells sharing the same genetic sequence. Methylation changes are known to be early and recurrent in prostate cancers (Massie et al 2017), and obesity is known also to alter methylation patterns. We might therefore wonder whether consideration of methylation patterns is required to understand the relationship between obesity and prostate cancer. However, including high-dimensional molecular data as a mediating factor in causal analyses is difficult. Moreover, we have to acknowledge that other factors may be involved such as diabetes and genetics. This project will use large public data sets (e.g. Genomics England, UK Biobank, Generation Scotland, ICGC, PPCG) to investigate the relationship between obesity and prostate cancer and the potential role of methylation within that. The project may involve (but will not be restricted to) - Improved meta-analysis of obesity risk for prostate - Developing methods for incorporating high-dimensional data in a causal mediation analysis. - Developing novel methods for dimension reduction in methylation data sets. - Developing methods for meta-analysis of high-dimensional methylation data.