Optimal dna methylation biomarkers for differentiating normal and renal cancer samples
Abstract
This thesis project is motivated by the urgent need of biomarkers for differentiating normal and renal cancer samples in routine clinical practice. DNA methylation plays an important role in cancer development. DNA methylation biomarkers, which use specific methylation changes, provide a range of opportunities for diagnosis, prognosis, monitoring outcome of treatment. The aim of this study is to identifying optimal DNA methylation biomarkers for differentiating normal and renal cancer samples with minimal cost and more accuracy than the other sets of DNA methylation biomarkers in other studies. We performed a series of statistical methods, including paired t test, Bonferroni correction, Heirarcical cluster analysis, and the combination of structure trimming and top rank p values on the DNA methylation data of level three of 199 paired patient samples of kidney renal clear cell carcinoma (KIRC) from The Cancer Genome Atlas (TCGA). We successfully identified a set of the optimal DNA methylation biomarkers consisting about 12 genes for differentiating the normal and renal cancer samples. Up to date, this set of biomarker is the best one with minimal cost and the more accuracy than the other sets of DNA methylation biomarkers in other studies. The optimal DNA methylation biomarkers of 12 genes were validated by cluster analysis on the DNA methylation data of 51 paired patient samples of KIRC. The result shows the 12 biomarkers are able to effectively distinguish normal and renal cancer samples. Therefore, we can use the optimal DNA methylation biomarkers of 12 genes as candidates for future diagnosis, prognosis and monitoring outcomes of treatment in kidney renal clear cell carcinoma. However, there is a need to use a large clinical trial to further validate the set of biomarkers before it is used to clinical practice. In addition, we developed the method of combining structure trimming and top rank p values to identify optimal DNA methylation biomarkers. The method developed can be used on other types of cancers to identify the optimal DNA methylation biomarkers.
Subject Area
Biostatistics|American studies|Bioinformatics
Recommended Citation
Zhang, Shaojie, "Optimal dna methylation biomarkers for differentiating normal and renal cancer samples" (2013). Texas Medical Center Dissertations (via ProQuest). AAI1552540.
https://digitalcommons.library.tmc.edu/dissertations/AAI1552540