A novel analysis strategy for integrating methylation and expression data reveals core pathways for thyroid cancer aetiology. (December 2015)

• Record Type: Journal Article
• Authors: Ozer, Bugra; Sezerman, Osman
• Is Part Of: BMC genomics. Volume 16:Number 12(2015)
• Page Start: 1
• Page End: 19
• Publication Date: 2015-12
• Subjects: Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/1471-2164-16-S12-S7
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract Background Recently, a wide range of diseases have been associated with changes in DNA methylation levels, which play a vital role in gene expression regulation. With ongoing developments in technology, attempts to understand disease mechanism have benefited greatly from epigenetics and transcriptomics studies. In this work, we have used expression and methylation data of thyroid carcinoma as a case study and explored how to optimally incorporate expression and methylation information into the disease study when both data are available. Moreover, we have also investigated whether there are important post-translational modifiers which could drive critical insights on thyroid cancer genetics. Results In this study, we have conducted a threshold analysis for varying methylation levels to identify whether setting a methylation level threshold increases the performance of functional enrichment. Moreover, in order to decide on best-performing analysis strategy, we have performed data integration analysis including comparison of 10 different analysis strategies. As a result, combining methylation with expression and using genes with more than 15% methylation change led to optimal detection rate of thyroid-cancer associated pathways in top 20 functional enrichment results. Furthermore, pooling the data from different experiments increased analysis confidence by improving the data range. Consequently, we have identified 207 transcription factors and 245 post-translational modifiers with more than 15% methylation change which may be important in understanding underlying mechanisms of thyroid cancer. Conclusion While only expression or only methylation information would not reveal both primary and secondary mechanisms involved in disease state, combining expression and methylation led to a better detection of thyroid cancer-related genes and pathways that are found in the recent literature. Moreover, focusing on genes that have certain level of methylation change improved the functional enrichment results, revealing the core pathways involved in disease development such as; endocytosis, apoptosis, glutamatergic synapse, MAPK, ErbB, TGF-beta and Toll-like receptor pathways. Overall, in addition to novel analysis framework, our study reveals important thyroid-cancer related mechanisms, secondary molecular alterations and contributes to better knowledge of thyroid cancer aetiology.