Doctor of Philosophy, Imperial College of Science, Technology & Medicine (2014)
Bachelor of Science, University College Dublin (2009)
Obesity results from the interaction of genetic and environmental factors, which may involve epigenetic mechanisms such as DNA methylation (DNAm).We have followed the PRISMA protocol to select studies that analyzed DNAm at baseline and end point of a weight loss intervention using either candidate-locus or genome-wide approaches.Six genes displayed weight loss associated DNAm across four out of nine genome-wide studies. Weight loss is associated with significant but small changes in DNAm across the genome, and weight loss outcome is associated with individual differences in baseline DNAm at several genomic locations.The identified weight loss associated DNAm markers, especially those showing reproducibility across different studies, warrant validation by further studies with robust design and adequate power.
View details for DOI 10.2217/epi-2016-0182
View details for Web of Science ID 000401642200014
View details for PubMedID 28517981
The incidence of Papillary thyroid carcinoma (PTC), the most common type of thyroid malignancy, has risen rapidly worldwide. PTC usually has an excellent prognosis. However, the rising incidence of PTC, due at least partially to widespread use of neck imaging studies with increased detection of small cancers, has created a clinical issue of overdiagnosis, and consequential overtreatment. We investigated how molecular data can be used to develop a prognostics signature for PTC.The Cancer Genome Atlas (TCGA) recently reported on the genomic landscape of a large cohort of PTC cases. In order to decrease unnecessary morbidity associated with over diagnosing PTC patient with good prognosis, we used TCGA data to develop a gene expression signature to distinguish between patients with good and poor prognosis. We selected a set of clinical phenotypes to define an 'extreme poor' prognosis group and an 'extreme good' prognosis group and developed a gene signature that characterized these.We discovered a gene expression signature that distinguished the extreme good from extreme poor prognosis patients. Next, we applied this signature to the remaining intermediate risk patients, and show that they can be classified in clinically meaningful risk groups, characterized by established prognostic disease phenotypes. Analysis of the genes in the signature shows many known and novel genes involved in PTC prognosis.This work demonstrates that using a selection of clinical phenotypes and treatment variables, it is possible to develop a statistically useful and biologically meaningful gene signature of PTC prognosis, which may be developed as a biomarker to help prevent overdiagnosis.
View details for DOI 10.1186/s12885-016-2771-6
View details for PubMedID 27633254