Proteomics Strategies to Develop Proteins of Post-translational Modifications in Plasma-Derived Extracellular Vesicles as Disease Markers
Date of Award
Doctor of Philosophy (PhD)
W Andy Tao
Committee Member 1
Committee Member 2
Committee Member 3
Blood tests, which are the most wide spread diagnosis procedure in clinical analysis, apply blood biomarkers to categorize patients and support treatment decisions. However, existing biomarkers often lack specificity and are far from comprehensive. Mass spectrometry-based proteomics allow users to characterize plasma protein in great depth and has become a powerful tool in the biomarker discovery area. However, because of the extremely high dynamic range of plasma, being able identify thousands of plasma proteins using methods such as Liquid chromatography-tandem mass spectrometry (LC-MS/MS) remains a challenge. Furthermore, recent discoveries of extracellular vesicles (EVs) have proven that EVs have a high possibility for becoming the source for biomarker discovery and disease diagnosis. In addition to the protein in EVs, post-translation modification proteins (PTMs proteins) are also interesting targets because the PTMs proteins are involved with many cancer-related signaling transductions. This dissertation proposes proteomics strategies of using PTMs proteins in plasma-derived extracellular vesicles as breast cancer markers. Initially, Chapter One highlights the potential of using phosphoproteins in extracellular vesicles as markers for breast cancer. Chapter Two delves into the development of a pipeline proteomics strategy that utilizes glycoproteins in EVs as breast cancer markers. Finally, Chapter Three explores the details of different subtypes, which presents the possibility of leveraging three PTMs including phosphorylation, acetylation and glycosylation to distinguish three major breast cancer subtypes.
Chen, I-Hsuan (Blair), "Proteomics Strategies to Develop Proteins of Post-translational Modifications in Plasma-Derived Extracellular Vesicles as Disease Markers" (2018). Open Access Dissertations. 1702.