谷歌浏览器插件
订阅小程序
在清言上使用

CL-01 Translation of Clinical Proteomics : Opportunities and Challenges

semanticscholar(2013)

引用 0|浏览7
暂无评分
摘要
Over the past 10 years, large amount of global efforts have been made to discover serum/plasma protein biomarkers by comparative proteomic analysis of blood circulating proteins. The most commonly used approach was the single-center case-control design. In case-control proteomic studies, quantitative profiles of serum/plasma proteins were first obtained in an untargeted manner, and then compared to identify the differences as individual potential biomarkers or a combination of differential features as diagnostic/prognostic disease-associated fingerprints. In spite of advantages of case-control design such as time-efficiency and cost-effectiveness, there are many pitfalls. Surface-enhanced laser desorption/ionization (SELDI) TOF mass spectrometry (MS) (or called ProteinChip SELDI technology) is the first highthroughput technology that allows comparison of plasma/serum proteome contents in a large number of subject samples within a short period of time. Using this technology, numerous case-control studies found serum/plasma proteomic fingerprints with over 90% accuracy in the diagnosis or prognosis of various diseases. However, criticisms and hesitations on this approach have been appearing all over the world. After accumulating more research experiences, researchers now have better understandings of characteristics and limitations of applying comparative proteomic analysis of blood circulating proteins to biomarker discovery. By using our MS-based biomarker discovery studies as examples, opportunities as well as biological and statistical concerns on applications of case-control comparative proteomic analysis to biomarker discovery will be discussed in this lecture. With rapid advancement of MS technologies and proper clinical study designs, discoveries of clinically useful biomarkers should be forthcoming. CL-03 Proteomic Investigations of Heart and Lung Diseases
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要